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[technophili]

6 Ridiculous Things People Think AI Will Do

6 Ridiculous Things People Think AI Will Do But (Probably) Never Will

6 Ridiculous Things People Think AI Will Do But (Probably) Never Will

Separating AI hype from reality: Why most AI doomsday scenarios are more science fiction than future fact

AI WILL LEAD A COUNTRY OR BECOME CEO. IT WON'T AND IT NEVER WILL.

Don't you find those who repeat all day long that AI will dominate the world, AI will replace all jobs, AI will do or AI will do this boring? 💡 There are so many misconceptions being spread about what AI will or won't be able to do that it's getting annoying. 🤖💥 And if I tell you that some preconceived notions about AI are wrong and that AI will do this… won't happen. At least… not exactly as you might have hoped. 😅 Stay with me because you're eager to discover AI's true potential. We know you care about your future – your career, your creativity, your edge in a world hurtling towards automation. These six ridiculous things people think AI will do but (probably) never will. Trust me, it's worth a read. 👀

Misconception #1: Programmers Will Soon Be Obsolete

❓ In any case, AI can do what programmers can do, and they'll soon be replaced. Direct answer: No, indirect answer: Maybe. I think this is the most obvious case we hear. And you can feel it every day on social networks, just type in a prompt with ChatGPT or Claude AI and get the code for an application that will revolutionize the technology sector. Can't you already smell the scam? Already, we're forgetting that AI just collects data and regurgitates it later in the form of code. So, if AI simply recycles similar data over and over again and then sends it back to us, we'd end up with applications that don't really stand out from the crowd. So we need programmers who will implement new codes, examine different approaches to solving problems. It would be easier to ask an AI to write the code for the new Microsoft Office PowerPoint if thousands of source codes for similar applications already existed on the Web. Hence the fact that you can't simply ask an AI to create a complete software package for you. All the same, it's important to note that these comments should be taken with a grain of salt, since we're hearing talk of general AI reaching an optimal level of consciousness. Even then, it would be difficult to do without programmers. The Bug Problem: Why AI Can't Replace Human Engineers Let's imagine you have a general AI in your possession. You run a prompt and you get your new, almost identical PowerPoint program. Tell me how you're going to deal with the presence of a bug? Well, if there's one thing to bear in mind, it's that a programmer's sole aim is not to create code or programs. He's constantly dealing with potential bugs that may arise in his program, and working hard to find solutions to improve the software's performance. I don't believe that a simple prompt can solve this kind of problem, which requires a considerable amount of thought. The XZ Backdoor: A Real-World Example On April 01, 2024, Andres Freund, a software engineer at Microsoft, had recently been running performance tests on a tool he was working on, with the aim of finding anything that might slow down his program in order to optimize it as much as possible. This term has a name, and it's called micro-benchmarking. He noticed a small but unusual delay that day, during which the processor was using far more resources than usual. He measured this delay and found it to be 500 milliseconds. This was a little strange for Andres, who began to investigate and discovered that this latency was not caused by his program, but by an external program installed on his machine. Something had crept into the XZ program. XZ is a compression format. It takes a file or folder, compresses it thoroughly (stronger than ZIP or RAR most of the time), and outputs it with the .xz extension. The result: a smaller file that's easier to store or send. It uses a technique called LZMA2, which is super-efficient for reducing the size of files, especially large ones like databases, logs or programs. Back to our story, since Andres Freund had just discovered a backdoor. A backdoor is like a completely secret door that a hacker integrates into a program. Once this program is installed on a program or server, the hacker can control it remotely without anyone knowing. This can become really dangerous if it affects servers belonging to governments or large corporations. Back to our story. Where did this backdoor come from? Well, there was a contributor by the name of "Jian Tan" who helped maintain XZ mainly through certain updates. He was kind, super-active, dedicated, in short, a good guy… on the surface. Eventually, he gained everyone's trust and even took control of the project. Once in control, he slipped a modification into the code that was not easy to detect, even for experts. This backdoor enabled someone to remotely execute code on a Linux system using XZ, and especially on systems running OpenSSH (the software that enables remote connection to servers). If an update had taken place, thousands of Linux servers could have been hacked and no one would have noticed. If that Microsoft engineer, Andres Freund, hadn't noticed that certain programs were becoming slow, if he hadn't dug and dug and discovered that XZ did more than compress files: it spied quietly, Linux distributors like debian, Ubuntu and fedora would have integrated it into their systems, and here we are facing a global catastrophe in the world of computing. So yes, an AI can create prompts, can create code, but if you don't understand the code behind it, if you don't know how to optimize it to make it perform better, you'll be vulnerable to cyber attacks. This also implies that you don't need a cyber security engineer. Rather, I believe that some programmers will be obsolete. Those who can't put artificial intelligence to work for them. It's also a question of those who, having a basic knowledge of programming, think they can get by with AI at the helm. As long as they understand the code, that's fine! After all, big companies aren't going to lay off all their software engineers overnight. That's like asking them to replace their COBOL code with C++, for example. However, the new start-ups that are emerging have the capacity to limit the presence of software engineers. The arrival of tractors didn't put farmers out of work, and the birth of the Internet didn't put couriers out of work either. So engineers won't be totally replaced, but they may be reduced in companies over the decades.

We're Going to Make Movies with AI

Right! I want to take a photo. To do so, I need to exist somewhere in time and space, and as a conscious being. I'd also have to use a camera to capture a moment of physical reality, and all that light pattern I capture would automatically be transcribed into a 2D representation of that reality that we commonly call a photo. Then the light would have to pass through the eyes of another conscious being. What AI promises is that we'll no longer need this process of taking photos, no conscious being, no reality. What we have to understand is that AI doesn't really have a real referential. What I mean is that AI gathers other images, mixes them like a salad and tries to produce something that looks like an image but isn't in reality. They're just static constructions, remember that. This is particularly true when you consider that, to promote Tesla, Elon musk used AI to generate images that look surprisingly like Blade Runner 2049. AI-generated imagery often mimics existing visual styles rather than creating truly original aesthetics In a way, these AI generatives are being sold to us as a means of creating convincing visual sequences without the need for human beings. I find this sad for cinema, because films that took decades to make will be trivialized. If the next Avatar Fire & Ash came out, it would be seen several years later as something that was made by AI, or that AI can make something similar, which won't have the same visual punch. The Essence of Cinema You know what makes a good film? It's the emotions that are released, it's a question of visual impact. It's about mastering the language of film. A look, a light, a soundtrack, a silence that speaks to us, that comes from reality – that's pure cinema. — This is the Art of Filmmaking Behind every film is a director who innovates, who manages a team, who invents new technologies when the need arises. James Cameron, Christopher Nolan and Steven Spielberg don't just film, they create unforgettable experiences. Interstellar's music, composed by Hans Zimmer, creates so many emotions and thrills that we wonder if AI can do the same. The Power of Original Vision A film creates images that nobody has seen before. The proof comes from the film Avatar. It's a classic story: a stranger meets a tribe, falls in love and fights against his own people, who want to bring their own brand of imperialism. However, the visual and sensory world presented to us, never seen before, was a revelation. It would be ridiculous to think that the stunts performed by Tom Cruise in the film Mission Impossible: Rogue Nation and those performed by Jackie Chan could be done by an AI. These stunts created authenticity, truth and reality. Films will continue to be the work of men. Studios may think they can cut production costs by using generative AI everywhere, but audiences won't go for it. Real cinema is about making an inner conflict visible through powerful images. Read the full article

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Design a professional logo in 5 minutes: The Best Sites for Stunning Logos

First impressions matter—your logo is the face of your brand. But hiring a designer can be expensive and time-consuming. 🤯 What if you could create a stunning, professional logo in minutes without breaking the bank? 🔍 We tested AI logo designers to find out which AI-powered logo maker delivers the best results. The psychology of choice overload says too many options can be overwhelming—so we simplified it for you! 💡 Don't waste hours searching—discover the best AI logo generator for your business today.  🚀👉 BRANDCROWD BrandCrowd is an excellent site for creating all types of logos. Thanks to artificial intelligence, it adapts to your needs and proposes professional logos, elegant or sober, according to your wishes. There's no need for a complex interface to get the job done right. At first glance, BrandCrowd doesn't have a home menu per se, but what really appealed to me was its simplicity: as soon as you open the site, you come face to face with an interface that already lets you create your logos. A little further up, you'll see several tabs, including Logo Maker, Business Cards and Website. But let's get back to logo creation. All you have to do is enter the name of your site, and Bingo! You might be thinking, "What if the logo style isn't what I want, since the logos I'm going to generate aren't necessarily going to target a specific domain"? Well, you've got it all wrong! Once you've created your logo templates, you'll have access to a "Filters" option that gives you access to other logo styles (abstract, mascot, emblem, corporate, wordmark, vintage, classic, etc.). If you're looking for a specific color, you've got a wide range of colors like green, red, purple, pink, orange and grayscale to name but a few. To answer the question of which logo style is best suited to your target area, a search bar lets you search for a specific logo using keywords. What purpose do these tabs serve? Let's go back to the tabs: - To accompany the creation of your BrandCrowd logo, you can create business cards via the Business cards tab. - One thing that few logo generation tools do - or almost none - is offer a way to create your own websites through the Website tab. - Create a design that suits your personal needs. If it's not beautiful, then what is it? There's a catch, and you know it. BrandCrowd doesn't offer all this for free, so you'll have to upgrade to a subscription plan. For those who prefer to pay per year instead, the price will drop a tiny bit, like $6 per year. You'll get the same offers as before, no surprise there. For a Premium plan, at $9 a month - of course you can cancel the subscription at any time, otherwise it wouldn't be fun - you get 1 professional website, 100 website templates, you can connect to a personalized domain name, you also have access to a site editor, and much more. I'd like to make it clear that these are only the prices for the website package. You don't need to create logos to create websites. For the Premium Logo pack you get : - You'll be charged $5 per month or $25 per year. Just in case you want to combine the two (logo and website), the Premium logo + website pack offers you: 7$ for monthly billing or 34$ per month Pros: -  A very excellent logo generator, there's nothing to complain about -  More than 271,000 logo templates - More than 100 website Templates -  Extremely lower prices than the competition -  Inclusion of a website creator and manager, with exceptional Templates -  The business card, a little something extra -  Personalize your logos at will -  24/7 support Cons : -  I personally think that even if the prices are cheaper than what the competition is offering, it's still a lot of money, $9 a year or $108, not everyone can afford it -  The difference between $7 and $34 is far too much -  No possibility of downloading logos in high-res with the lowest subscription I think that if it's your choice to create websites and logos on BrandCrowd, choose the logo + website pack, even if you don't want to create a logo, you'll still save money LOOKA I presume that Looka is no longer a site to introduce, and I think you already know it, like when we talk about logo design sites, I think it's Looka that comes to mind the most. I don't think there's much to say, except for those of you who don't know this site. Indeed, this site is one of the best logo generators on the market, and some of your friends can confirm this. But unlike BrandCrowd, you're going to spend some time choosing the industry in which you want to generate your logo, after of course entering your company name, choosing certain logo styles you like, certain colors you like, choosing a slogan - which is indeed optional - choosing symbol types - I didn't quite understand what that was, but I think the point is to specify your company's field,and it's after all this effort that you'll have your logo. It's getting more expensive here With looka, you have four packages, Basic logo package where you pay once at $20, your files will be in low-res, and no transparent files Premium logo package: with this one at $65 which is also a one-off purchase, you can now have several types of high-res files, you can apply changes until we find the One piece, lifetime technical support, and ownership of the logos is entirely yours. Brand kit subscription includes all the above plus business cards, email signatures and more. However, you'll have to pay $96 a year - yeah, that's a lot!  Brand kit web subscription presents everything mentioned above and now includes the creation of a website Pros: - Complete customization of your logo -  Possibility of choosing whether you want your logo to give off a modern, classic, friendly, delicate, sophisticated look and many others - Inclusion of the creation of a website Cons: -  I find that the prices are really too abused compared to what other sites like BrandCrowd offer -  The competition offers more beautiful, more successful, more elegant logos for less money. I don't really like the designs - The questionnaires for generating a logo to our liking are very long and very...boring! - Still not possible to download logos in high-res quality Turbologo

Turbologo, with its range of logos to be generated, is ideal for designing your logos. Although less popular than Looka in my opinion, it's still one of the best, albeit better than Looka. Turbologo works a bit like BrandCrowd. You enter the company name and then make the changes. I particularly like this way of working, as we're given the choice of looking at all the proposals available. And it's only after a moment's dissatisfaction that you can decide to personalize the logo. For prices...it's a bit harr Let's take a look at 03-month packages. -  With the $20 lite plan, you get a low-resolution logo for digital use -  With the $40 standard plan, you can make changes and re-download logos, multiple high-quality logo files, a transparent background, unlimited additional logo creation and, most importantly, full commercial rights. - Everything you need to start your business is included in the $80 package. Everything that was in the Standard plan, plus high quality images, business cards, watermarks, wallpapaers, email signatures, favicons, envelopes, and much more I'll dispense with mentioning. Pros : -  I really love the quality of the logos - It's all really fast, no need for an interview before generating a logo. You generate your logo and make the changes afterwards. -  Works pretty much like BrandCrowd, if you like this one, you'll probably like Turbologo -  Creation of watermarks and Wallpapers, for those interested -  Create new logo versions Cons: - Price a bit too high compared to competitors like BrandCrowd - You can't do much with the lite plan, disappointing! -  No high-res quality in the lite plan. Design.com     I don't think there's much to say about Design.com since its name already reflects its potential, but there's little to say given the exceptional quality of the logos designed. In my humble opinion, this is by far the best logo designer on the market. I'm already captivated by the elegant symphony of colors, the fonts, the patterns, everything is respected, and I really love the black and dark blue of the backgrounds. It makes the logos look luxurious, regal, rich and wise. It's much the same with Brandcrowd and Turbologo: you enter your company name and the customizations come afterwards. Just a reminder for all three sites, if the company name is explicit enough, keywords will be automatically generated and you won't have to enter keywords to search for specific logos. Among the logo styles you'll find abstract, mascot, Emblem, corporate, vintage, classic, wordmark Finally, a real AI logo generator      If you look closely, you'll notice a little AI tab. In fact, on Design.com, a real feature is available for creating designs with AI. There are logo generators, business name generators, website name generators, background erasers with AI, website generators, domain name generators, business cards with AI, posters with AI, flyers with AI, bio links generated with AI, presentations with AI. Even the prices are pretty reasonable There are in fact two subscriptions, the monthly and the annual As far as the monthly subscription is concerned, -  You have the Basic plan, for the trifling sum of $15 a month. Here your logo is yours forever, - you cancel your subscription at any time, - make unlimited changes to your logos, not to mention that so far it's the only one that integrates business cards, Instagram and Facebook posts, email signatures, - over 275,000 Templates, and the icing on the cake, 50 other design tools into the lowest package. - I'm also led to believe that it's the only one that offers high-res logo files for the smallest package. The premium subscription is a little disappointing in terms of what it offers for the price. - In a nutshell, you go up to $24 a month and the only thing added to the premium subscription is website creation. On the subject of annual subscriptions, I think Design.com has really spoiled you, offering: - 5$ per year, or 60$ per month for the basic plan. -  7$ per month, i.e. 124$ per year, is the ransom to pay to benefit from the premium plan annually. Pros : - Best logo designer on the market, for me -  AI functionality for all your business needs. No need for shopify -  Only site I've tested that lets you download high-res files for under $15 -  Prices are relatively low like BrandCrowd's unlike other logo designers -  Also the only site I've tested that integrates business cards, email signatures, Instagram and Facebook posts for such a low price Cons: - The premium subscription of $24 against $15 for the basic plan, is almost useless unless you want to create a website on Design.com Tailor Brands Tailor brands, is a particular site that contains in one place everything you need to create your business. I'm not here to make a silly listing, I'd like to give you my opinion, and in this case, Tailor Brands isn't a site I'm particularly fond of. Firstly, this site works exactly like Looka, you enter the name of the company and then proceed to a long interrogation, except that with Tailor Brands, the interrogation is really longer, and the whole thing ends up being boring. Finally, the logo designs aren't pretty, they're just hideous if you try to compare them to other sites. But it's true that not everyone has the same tastes, so when it comes to packages, the basic plan starts at $4 a month annually or $10 a month. Read the full article

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5 Best AI Meal Planners Compared: My Brutally Honest Review

AI is revolutionizing everything—even meal planning. But can artificial intelligence really create personalized, nutritious, and tasty recipes tailored to your diet? We tested the top AI-powered meal planners, to see if they live up to the hype—or if they’re just another cash grab. Read on to discover which tools are worth your time and money!

Musely Ai: It's already off to a good start

Not only do you have the ability to generate recipes, but what I find interesting is that Musely AI has other tools, such as one for summarizing or rephrasing texts, and much more besides. Personally, I don't find this so relevant, because in the West, we often consider that the more things we know how to do, the less good we are at what we do. I would have preferred Musely to focus solely on recipe generation and meal planning.Let's go! On to the interface, what's inside?

In fact, the interface isn't all that revolutionary, but it's still pretty simplistic and doesn't swarm with unnecessary details.Let me explain what criteria Musely takes into account when generating your meals.

In fact, meal schedules are generated based on your calories, food preferences and the nutritional goals you've set for yourself. That's enough to let you personalize your recipes.What's more, you get a weekly recipe calendar perfectly tailored to your dietary preferences. It takes into account

breakfast, lunch and dinner, as well as a few snacks for watching Netflix in peace.What I find pretty crazy is that you can choose to add a shopping list to your recipes, i.e. the list of ingredients needed to prepare them.It gets even crazier when you realize that they take it a step further by giving you information on macronutrients and even calories for each meal, allowing you to track your nutritional goals.Oh, I almost forgot: you can choose the cuisine you'd like to eat, whether it's French, Chinese or even Italian. If you can't find a specific cuisine, you can always customize it.For example, if you want Nigerian cuisine, you'll find recipes for that.If your life is too complicated, you can remove a few ingredients if you like.Why is it always too good to be true?I think it's too good to be true. And I'm right!What gets me is generating things quietly, then not being able to anymore because you've reached your limit, and now you have to pay! That's what Musely has done, and you have to pay $5 a month.What may turn you off for sure is the very short refund period: just 3 days. Seriously, what's the big deal? What do you have time for in three days?Personally, I don't recommend this site, but if you still want to give it a try, other payment methods are available and you'll be charged $6.7 per month for a six-month period, a 33% saving compared to the $9.9 per month option.

Foodiprep: a love story (no pun intended)

I can assure you that I've fallen in love with this site. Foodiprep is THE site I'd definitely recommend.

The interface is just incredible, there are so many animations and everything is so beautiful, even if it feels a bit like the food images were generated by artificial intelligence.- At the very beginning, there's a little questionnaire that might put off the laziest among you, but it's no big deal. - Creating a recipe couldn't be easier: go to the “Create Recipe” tab or, if you're in the ‘Dashboard’ tab, click on “Create a new Recipe”. - After clicking on the recipe creation tab or button, you're taken to an interface similar to that of text generative AI, in which you actually interact with an AI, just as you would with ChatGPT. - To give it a try, I entered my prompt by telling Chef Foodie (that's the name of Foodiprep's AI): - On Foodiprep, you have a Pro subscription plan at $4.99 per month. - For $8.99/month, you get all the features of the Pro plan, plus access to unlimited generations of recipes thanks to the Nutrition Pro plan. You'll also have access to a totally comprehensive nutritional analysis, including of course calories, protein, fat, fiber, sugar, sodium, cholesterol, carbs... Wow!!!! - Here, it's even easier: just click on the Make selections button, then choose the type of food (French, American, Japanese, etc.). Treat yourself! - After following a few steps, you'll finally arrive at your dish. For my part, I got a steak with sautéed sweet potatoes and spinach, and garlic mashed potatoes. - You'll need to enter your weight, height, goal weight, and the number of weeks for your recipes (4, 6, or 10, it's up to you). - Then there is a section at the very bottom dedicated to the different types of plant-based diets: vegan, lact-vegetarian, ovo-vegetarian, lacto-ovo vegetarian (I didn't know there was such a thing), pesco-vegetarian, pollo-vegetarian. - On the small window you have the basic section where you generate your recipes according to your gender (male or female), age, units, weight, height, activity level (sedentary, nomadic, very active, extremely active) and body goal (lose weight, maintain weight, gain weight, etc.). - In the advanced section, you can indicate your dietary goals, desired calorie count and foods to which you are allergic.Then click on “Generate plan”, and choose the day from which you would like to receive weekly-generated plans.

You should now select two or three recipes for each meal, as your recipes will be generated according to your choices.

You'll get your recipes for breakfast, lunch and dinner from Monday to Friday.

By clicking on each recipe, you'll get a detailed overview of its composition.

ConclusionEach of these sites isn't perfect, but I think they're good enough to generate recipe plans that respect your choices. However, please check on Google whether the calorie content of each food is respected. AIs aren't perfect and can even make mistakes, so I wouldn't recommend referring to Claude AI or ChatGPT if you need to check calorie intake (unless you activate the “Search” option).Among the recipe generators for planned meals, I'd advise you to turn instead to Foodieprep and Planfit, which seem fairly reliable to me. But I wouldn't rule out any of the other sites, it's up to you. Read the full article

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Are We Living in a World Where Privacy No Longer Exists? What about surveillance?

Our world is far too connected these days, and with artificial intelligence (AI) everywhere, absolutely everywhere, perhaps even far more than the air.It's in our pockets, it's in our homes and it's watching us from every corner. But as AI gets smarter and smarter, it's becoming too much of a presence, something we didn't want. Find out how AI has changed privacy and surveillance, and what this means for our digital future.

What does AI privacy hide?

Do you remember a time when protecting your privacy stopped at drawing the curtains? It's funny today because that's all over.Now that everything has gone digital, having a private life is a little more complicated, because you have to be vigilant all the time, you have to protect your personal data, it's even become a complex dance of algorithms.... According to Digital Ocean, "AI privacy is the set of practices and concerns related to the collection, storage and ethical use of personal information by artificial intelligence systems". This means that we have to make sure that AI isn't a snoop and looking into what's going on in our lives all the time without our permission.What's also funny is that AI needs data to function, a lot of it, so much so that you have no idea, but I'm not telling you anything. How ironic, don't you think? As Digital Ocean points out,"AI poses complex and multifaceted privacy challenges, different from those posed by traditional data processing".It's like trying to fill a bucket with a hole in the bottom - you know the water will leak out. It's the same thing with data: the more data we pour in, the more vulnerable we are to privacy breaches.

The AI surveillance revolution: Big Brother gets better after all!

AI surveillance, I'd say, is like having a security guard who never sleeps, who never rests and who watches absolutely everything you do. And maybe you see: wesh! That's too cool to catch bad guys, right? Well, actually, it's not quite that.Forbes warns that "AI enables more detailed profiling and tracking of individuals' activities, movements and behaviors than has ever been possible before." Maybe if we gave the security guard x-ray vision, with the ability to read minds and a photographic memory, it would be a bit similar to what AI surveillance can do. At the same time, we could say goodbye to a private life.But the situation is even more worrying. Forbes also notes that "AI also facilitates the implementation of facial recognition technology, which can identify and track individuals based on their facial features, even in the real world. This technology is already used in public spaces, such as train stations or airports, and by law enforcement agencies."It's a bit like the scenario where you're walking down the street and every camera knows who you are,where you've been and where you're going. You live in a world where you feel like everyone knows your name but you don't know their names. It feels weird, very weird.

 How AI is rewriting privacy laws

As you know, Artificial Intelligence is evolving very fast, so fast that our legal systems can't keep up, and the gap is widening again and again.According to the Executive Leader Network, "the legal framework for AI privacy protection varies from jurisdiction to jurisdiction. In the European Union, the GDPR is the main legislation governing the protection of personal data." But that's where nothing goes right, because if you think about it, artificial intelligence isn't there to operate according to borders, it's not there to respect them.It operates on a global scale, so on a worldwide scale, whereas regulations are often local or national.The Executive Leader Network adds, "In addition to the GDPR, there are other regulations and guidelines that deal with specific aspects of privacy in AI. For example, the ePrivacy Directive regulates the use of electronic communication data, such as emails and text messages.”But implementing these laws is the same as hitting a moving target, because by the time you adopt a new regulation, you find that AI has already leapt ahead. It's literally a never-ending game of catch-up, an infinite loop, and for the moment, AI wins hands down.

 The impact of AI on personal data security 

Ah, the good old days, when all you were afraid of, the biggest threat to our privacy, was someone stealing your diary. Now that you've grown up and AI has arrived, personal data security has become a high-stakes game.Digital Ocean warns, "The vast data sets that AI needs to function effectively are attractive targets for cyber threats, amplifying the risk of breaches that could compromise privacy." But don't imagine that's all trouble. For example, AI can be used to reinforce what we have as digital defenses. As Digital Ocean points out,"AI can help ensure data privacy by identifying and stopping unauthorized access or information leaks, complying with laws such as GDPR and securing confidential data using encryption techniques, including de-identification processes."

 The ethical quagmire of AI surveillance

With great power comes great responsibility, I can't remember which Spiderman movie I heard this line in but that's not a problem because all you need to know is that in terms of ethics, the consequences are so many and at the same time real headaches.The Office of the Victorian Information Commissioner (OVIC) raises a notable point: "The process of going through each layer can create what is known as the 'black box' effect, making it difficult to understand and describe the steps that lead to a particular outcome."In other words, it can happen that even those who create AI systems can't 100% understand how they work. I think it's a bit like creating a Frankenstein and losing control of it, a downright terrifying prospect.CIPESA adds its share of insight by noting that "AI raises issues of bias and discrimination in data processing, abusive data practices, dissemination of misinformation and disinformation, increased real-time surveillance and aggravation of cyber-attacks such as phishing".That's a very funny perspective. Because the truth is, AI surveillance can also help us with real threats, just as it can be used to civil liberties and disrupt existing prejudices.If we gave society a superpower, we could use it for good or evil. It really depends on who's in charge.

What's the most likely future situation, a glass house or a digital fortress?

I don't think any of us want to live in a glass house, it doesn't even make sense in terms of privacy in the home. And while we're on the subject, what does the future hold for us in this new world where AI is gradually dominating? Will we find ways to build digital fortresses to further protect our data?According to Digital Samba, "AI brings a lot of convenience into our lives; therefore, we need to find a way to reconcile AI and data privacy." So it's not possible to have butter and try to have the money from that butter.We obviously want the benefits that AI offers but we much more than want to protect our privacy, we need it, which is why we may discover new ways to protect our data.As Digital Ocean suggests, "federated learning (...) allows AI models to be trained on many devices or servers that are not connected. These machines have local data samples to use for training. In this way, private information can remain on the person's device."But hey! We all know that the more sophisticated AI becomes, the more sophisticated the methods we use to violate privacy become, so while it's certainly seductive this approach, I don't think it's something that could protect us in the long term, in 20 years for example, maybe even in five.

Does our Dear AI have a role in government surveillance?

I'm not telling you anything, we all know that every government seeks to monitor its population, or already does, it depends on your country.But what if the government uses AI too? It's like leaving a magnifying glass and using a Hubble telescope: I'm telling you! Suddenly, everything's a lot...neater!CIPESA warns that AI gives more power in terms of the ability to monitor in real time, and that's not good at all because we could end up with public authorities controlling us in a more intrusive way.It reminds me of George Orwell's 1984, but with technology that even Orwell couldn't have imagined.The Office of the Victorian Information Commissioner (OVIC) adds another perspective: "Current and future use cases for AI in government remain limited by resources, technical capabilities and public trust. Some of the most immediate beneficial opportunities for the public sector are those where AI can reduce administrative burdens and help solve resource allocation problems."

You know what the price of convenience is?

Remember that one about if it's free then you're the product? And that's fine in the sense that as consumers,we've become accustomed to trading our data for services, whether it's with targeted ads on FACEBOOK or an algorithm that stuffs us full of mind-numbing content like Tiktok. But if AI enters the big leagues, we risk giving away even more, far more than we'd ever hoped.Digital Ocean warns that "predictive analytics... can infer personal behaviors and preferences, often without the individual's knowledge or consent". This has real consequences for what we are allowed to do or not do in our private lives. Do we have the right to keep everything we do in the future private?And what about what we think or prefer? These kinds of questions are ones we can't answer now, or perhaps only with difficulty, the time will come when we won't have to or won't be able to.Forbes adds another layer to this concern: "AI-based surveillance technology can, for example, be used for targeted marketing and advertising purposes." So not only can we be monitored, but they'll try to sell us products based on what we see. And I think that already exists.If you walk past a louis-vuiton store, for example, and in the past you've done a lot of research on louis-vuiton items you might get a notification that says you're next to a louis-vuiton store, so this and that.And I don't know anyone who likes this kind of functionality, it really doesn't make you want this kind of thing.

Can we tame the digital beast?

It's not so simple to find a balance between the benefits AI could offer us and what it can do negatively in terms of privacy. It's a big challenge, and a dangerous one too, just like riding a unicycle while juggling flaming torches.Digital Samba suggests that "solutions such as data anonymization, which removes all personal data from the information used by AI, can help".It may be a solution, but it's not really, because remember that AI is getting more sophisticated and smarter, so it's only natural that it will get better at de-anonymizing data.According to Forbes, "It's widely accepted that AI tools enable people to create content - text, images, videos and more can be created quickly using AI. But these tools can also be used to track and profile individuals."

Let's get to the point. What role does AI play in cyber security?

Apart from being an enemy to our privacy, AI can be used to combat cybercrime.As Digital Ocean points out, "AI can help prevent malware by analyzing code and identifying malicious patterns in order to develop more effective antivirus software".That may or may not be true! Has it occurred to anyone that cybercriminals can also use AI? In any case, each side is trying to outdo the other as best it can. The Office of the Victorian Information Commissioner (OVIC) notes, "As the output of each layer becomes the input to the next, it can become increasingly difficult to understand the decisions and inferences made at each level.”

AI and data breaches 

It's not the data breach that's a new concept - we've had it for every meal with Facebook - but maybe AI can do something about it.Digital Ocean warns: "Volume and variety of data. AI systems can digest and analyze exponentially more data than traditional systems, increasing the risk of personal data exposure."You know! AI isn't just about dangers, it's true that it's not the best friend for our privacy but it's going to depend on who's going to use it and for what purpose.If it's Meta using it, you know what to expect. If it's Oracle, you also know what to expect. And so here, we know that it can help us detect data breaches and prevent them at the same time.

 AI privacy in different sectors: one size doesn't fit all

In the healthcare sector, for example, AI can help improve patient outcomes, but it also deals with highly sensitive personal data.As Digital Ocean points out, "ensuring patient privacy should be a top priority for any AI deployment in the healthcare sector, in order to use data ethically and within the bounds of regulations such as HIPAA."In the financial sector, AI is used for fraud detection and prevention. According to Digital Ocean, "in real time, machine learning models analyze transaction data and identify patterns associated with fraudulent transactions."

The future of privacy in AI: 

What does the future hold? Nobody knows, what does AI have in store for us? No one really knows even if they claim to know by saying it will replace us, take over our world, serve as our assistant, all its contradictions prove that we really don't know what AI has in store for us. It's moving so fast, and so will our approach to privacy.Digital Ocean suggests that "quantum computing could improve AI's data security skills, making encryption sorting and threat detection easier and faster. But it also poses new problems, as the old way of preserving information security may no longer work."

 Conclusion: 

It's hard to say, but it has to be said: protecting life is far too stressful, it feels like all these technological advances are helping us, but it also feels like they're dragging us down even further, and if AI could be used intelligently for once and help us protect ourselves better, that would be great!Right now, what can we do apart from being vigilant, informed and, above all, human? A lot of other things, because in truth, the future of AI privacy is in our hands, although I'm not saying we can change the world.MAll we can do is make sure we build an environment where AI serves humanity, not the other way around. After all, we want to be the masters of our own destiny, we want to control what's ours, not be enslaved by what we've created. In fact, when we talk about protecting our privacy, we're not just talking about keeping secrets, we're also talking about keeping our individuality andhumanity, what makes us unique, in the face of a technology that's advancing really fast. Just keep your wits about you! Read the full article

[technophili]

Why AI is the Missing Link in the Renewable Energy Transition

The global AI market for clean energy is expected to exceed $75.82 billion by 2030, and the industry's confidence in this transformative technology is undeniable according to Kyotu Technology. At the moment, the energies we currently use are going to disappear, which is why we need an energy transition via solar, wind or hydraulic energy.These are the ones that will help us find sustainable, environmentally-friendly solutions. And why is it so urgent to make this transition?We don't need to tell you! We can all see how climate change is becoming more and more worrying as fossil fuel reserves run out, not to mention the fact that we're trying to reduce carbon emissions at all costs.So what can we do? What if there was a technology called artificial intelligence that could help us? And... what if there was a technology that could help operators, even just a little, to improve the energy optimization capabilities of renewable energy infrastructures?And...what if there were other technologies that are useful in 

Predictive maintenance in renewable energies

Well, we want renewable energies, we demand them, we love them, and that's normal.On the other hand, there are a few things to sort out when it comes to maintenance. According to the FDM Group ,the way we do maintenance often means that we have to do inspections all the time, or react immediately to equipment failures.All this causes unnecessary downtime, messes up the scheduling of interventions, particularly in remote areas or at sea, not to mention increasing maintenance costs.And since renewable energies don't operate continuously, because wind speeds can vary or because there's no daylight, maintenance planning becomes more complicated. So what is AI's role in predictive maintenance According to the FDM Group, these algorithms learn from historical data, identifying patterns and correlations that can indicate whether there are equipment failures about to take place.If we now switch to renewable energies, AI would be very useful when it comes to analyzing data that comes from sensors embedded in the infrastructure, past performance records and environmental factors, so as to know what problem might occur, and how this will optimize maintenance schedules. To what extent is AI-driven predictive maintenance used?In a field like solar energy, it's used to identify potential problems with photovoltaic (PV) panels. For those who don't know, photovoltaic panels are the flat surface that captures solar radiation in order to produce photovoltaic energy in the form of electricity.If AI algorithms analyze data on how well each panel is performing, we'll be able to detect anomalies such as declining efficiency or deteriorating panels, and know when maintenance is required.At least, that's what the FDM Group says. And it's relevant in the sense that it will guarantee much better energy production and extend the lifespan of solar installations.There are also wind turbines that need predictive maintenance with AI and especially wind turbines that wear out very easily and not to mention the fact that other components like bearings and gears are not what they used to be over time.According to the FDM Group, AI can tell when there will be failures by analyzing data from sensors that monitor vibration, temperature and other indicators.If operators know when this or that component is going to fail, it's a piece of cake to schedule maintenance activities as if they were anticipating everything that's going to happen, so they can make downtime as insignificant as it is improbable, but on the other hand it would make energy production so... productive.Let's turn the page and tackle hydroelectric systems, where we really need the performance of turbines and generators.So, as the FDM group would like, we really do need to avoid technical problems such as cavitation (the formation of gas and vapour bubbles in a liquid subjected to negative pressure) or imbalance,so if we let AI take the lead, it will be able to predict these kinds of headaches if, of course, we let it access past performance data as well as real-time sensor information.The real benefit here is that we could avoid costly repairs and even more unnecessary downtime.Challenges and limits: let's talk If predictive maintenance is to be effective, data must also be available in large quantities and of good quality.According to Javaid et al (2022), if AI were given data that was not accurate or unreliable to train on, I can assure you that you would find it hard to believe its predictions.So, I get the impression that it's currently a problem for operators to invest in sensors, data infrastructure and AI technologies, because without that, there's no predictive maintenance.

Energy optimization in renewable energies

The three energy sources we've already mentioned (solar, wind and hydro) are currently what's needed if we want to talk about energy that defends the cause and sustainability.However, as I said earlier, these energies don't work continuously. Wind turbines can't operate in the same way all the time, when wind speeds are constantly changing, and solar panels depend on sunlight, so when there isn't any? They stop.So these little moments mean that we have problems when it comes to matching energy supply and demand.Let me tell you about energy optimization. In a nutshell, it's a process in which we make operational parameters better, maximizing efficiency and output. And why do we talk about it? Because it's the central point between the reliability of renewable energies and their ability to compete successfully.Why is energy optimization so important?The FDM Group defines energy optimization as the art and science of maximizing the efficiency and output of renewable energy systems.It involves ensuring that energy production is aligned with demand, adapting to the fact that energies don't work all the time and that their conditions can change, and ensuring that the energy we produce meets quality standards.If we go back to our renewable energies, it's still very important to do so to cope, as I said earlier, with the fact that energies don't work continuously, so we now have other, more reliable choices and they last longer than what we're used to using if we think economically.What's more, its importance goes beyond the simple fact that it increases efficiency. According to the FDM Group, it has a direct impact on the economic viability of renewable energy projects, making them more competitive in the wider energy market.In addition, optimizing energy production contributes to the overall stability and reliability of the power grid, and thus fosters a stronger ecosystem for the integration of renewable energies.

Benchmarking AI techniques

The integration of AI techniques, including deep learning, neural networks and predictive analytics, in predictive maintenance and energy optimization, highlights their distinct strengths and applications.Deep learning Deep learning is adept at automatically learning the most relevant features from datasets, making it suitable for scenarios where manual feature engineering is difficult.According to Mansouri et al. (2021), deep learning models, in particular multi-layer neural networks, are capable of capturing complex non-linear relationships within data.Deep learning models can be computationally intensive, requiring powerful hardware and processing resources. The question is, why? Why are depp learning models so complex? In fact, it's often because we can't explain or interpret certain results, and that's what makes the decision-making process so difficult to understand.Maybe you didn't understand this part, but that's okay, just remember that deep learning is used to study a wide range of data which, let's not forget, are not eternal, and it actually comes from wind turbines, so it's easy to know when you're facing potential faults or things that are unclear or abnormal in performance, and all this by detecting subtle patterns.Image recognition tasks, such as identifying anomalies in solar panels through image analysis, illustrate the capability of deep learning in solar energy applications (Mansouri et al., 2021).Neural networksVersatile neural networks excel at recognizing complex patterns in data, making them suitable for fault detection and prognosis in predictive maintenance. According to Chen et al (2021), neural networks adapt to changing conditions, enabling them to learn continuously and adjust predictions in line with evolving data patterns.The effectiveness of neural networks is highly dependent on the quality and quantity of labeled data available for training. Neural network training can be complex and time-consuming, requiring careful tuning of hyperparameters.Neural networks are effective in fault detection applications, analyzing sensor data to identify deviations from normal turbine performance, enabling proactive maintenance. In wind energy, neural networks help predict the remaining useful life of critical components, facilitating maintenance planning (Chen et al., 2021).Predictive analysisPredictive analysis, based on statistical modeling, provides interpretable information on the factors influencing maintenance forecasts, offering transparency in decision-making.According to Sri Preethaa et al (2023), the use of statistical techniques provides a robust framework for understanding the relationships between variables and predicting future events.Predictive analysis may struggle to adapt to highly dynamic or non-linear systems, where traditional statistical models may fail to capture complex patterns. The effectiveness of predictive analysis is highly dependent on the availability of historical data, and sudden changes in operating conditions can impact on its accuracy.Predictive analysis can be applied to estimate the probability of inverter failure based on historical data and environmental conditions.In wind energy, predictive analysis can be used to efficiently schedule maintenance activities based on historical performance and weather forecasts (Sri Preethaa et al., 2023).The choice of AI technique depends on specific use cases, data characteristics and operational requirements. Deep learning and neural networks are good in scenarios where complex patterns and non-linear relationships need to be identified.Predictive analytics, with its interpretive capability and statistical modeling, may be preferred when less dynamic systems are involved and a transparent decision-making process is crucial.Challenges and opportunitiesThe fusion of AI and renewable energies has opened up new frontiers in the search for sustainable and efficient energy solutions.However, this integration comes with its own set of challenges that need to be addressed to unlock the full potential of this transformative partnership.Data security and privacyWith AI applications in renewable energy relying heavily on the collection and analysis of large amounts of data, ensuring data security and privacy has become a paramount issue. According to Shateri et al (2020), the interconnected nature of energy systems and the transmission of sensitive information pose risks that require vigilant attention.Growing dependence on interconnected devices and smart grids increases vulnerability to cyber-attacks. Malicious actors may attempt to disrupt energy infrastructures, with potential economic and environmental repercussions.Granular data collection, particularly from smart meters and sensors, raises privacy concerns (Shateri et al., 2020).Developing and implementing robust encryption methods and secure communication protocols can protect data during transmission, reducing the risk of unauthorized access.According to Seth et al. (2022), advances in privacy-preserving AI techniques such as federated learning and homomorphic encryption make it possible to extract valuable information from data without compromising privacy.Interoperability challengesThe heterogeneous nature of renewable energy systems, combined with various AI technologies, poses interoperability challenges.According to Rane (2023), the lack of standardized frameworks can hinder seamless communication between different components and systems, thus undermining the scalability and efficiency of AI applications.The coexistence of various AI models, each developed using different technologies, poses difficulties in creating interoperable systems capable of exchanging information effortlessly.The lack of universally accepted standards for data formats, communication protocols and interfaces complicates the integration of AI solutions across different renewable energy platforms (Rane, 2023).Collaborative efforts to establish industry-wide standards for AI applications in renewable energy can streamline interoperability and facilitate the exchange of information between various systems.Promoting the use of open-source platforms and tools can encourage the development of interoperable solutions, fostering a collaborative ecosystem (Rane, 2023).Difficulties of integration into existing infrastructuresIntegrating AI into existing renewable energy infrastructures poses challenges due to the need to modernize them and ensure compatibility.According to Yaqoob et al (2023), many renewable energy systems were not initially designed with AI integration in mind, making the adaptation process complex.Adapting AI solutions to older renewable energy systems, which were not initially designed to accommodate advanced technologies, requires careful planning to avoid disruption and inefficiencies.Implementing AI solutions can involve high initial costs for infrastructure upgrades, new equipment acquisition and staff training, posing financial challenges for some operators (Yaqoob et al., 2023).Phased implementation of AI solutions, starting with specific components or subsystems, enables a gradual integration process that minimizes disruption and spreads costs over time.Designing renewable energy systems with adaptability in mind makes it easier to integrate AI technologies in the future, fostering a more responsive and efficient energy infrastructure.

Opportunities for further research and development

While challenges exist, they serve as catalysts for further research and development, offering exciting opportunities to advance the application of AI in renewable energy. Key areas of opportunity include1. Developing AI-driven predictive maintenance models that can accurately anticipate equipment failures, optimize maintenance schedules and reduce downtime in renewable energy systems (Ahmad et al., 2021).2. Research into AI algorithms for real-time grid management, enabling a better balance between energy supply and demand, the integration of intermittent renewable sources and efficient energy distribution (Hannan et al., 2020).3. Investigating AI techniques to optimize energy storage systems, ensuring efficient charging and discharging cycles and maximizing the utilization of stored energy (Li et al., 2023).4. Explore AI solutions to manage decentralized energy systems, such as microgrids, to improve energy resilience, reliability and self-sufficiency (Şerban and Lytras, 2020).5. Advance AI-powered decentralized energy exchange platforms, where individuals and organizations can sell surplus energy back to the grid or trade it with each other, making clean energy more affordable according to Forbes 6. Bridge the expertise gap by encouraging collaboration between AI experts and renewable energy professionals to develop tailored solutions that meet the unique requirements of the energy sector according to Forbes.7. Improve the quality and diversity of data sources to increase the accuracy and reliability of AI-driven predictive maintenance and energy optimization models according to Forbes.8. Explore innovative techniques, such as federated learning and homomorphic encryption, to address data security and privacy concerns in the integration of AI and renewable energy (SETH ET AL., 2022).9. Develop standardized frameworks and protocols to facilitate interoperability between various AI technologies and renewable energy systems (rane, 2023).10. Design renewable energy infrastructures with inherent adaptability to enable easier integration of AI solutions in the future, creating a more responsive and efficient energy ecosystem (yaqoob et al., 2023).

Conclusion

The symbiosis between AI and renewable energies holds enormous promise for a sustainable and technologically advanced future.By harnessing the power of AI in predictive maintenance and energy optimization, the renewable energy sector can improve the reliability, efficiency and competitiveness of clean energy solutions.AI-powered tools, combined with human expertise and ingenuity, can optimize complex hybrid generation projects, seamlessly integrating renewable sources into the power grid according to Forbes.The integration of AI and renewables offers a future where decentralized energy exchange platforms, powered by AI algorithms, can predict prices, optimize the timing of exchanges and ensure efficient redistribution of energy, making clean energy more affordable and accessible (Forbes).However, the journey is not without its challenges. Data security and privacy, interoperability issues and difficulties integrating into existing infrastructure require collaborative efforts, standardization and ongoing research.By answering the call to action, researchers, practitioners and policy-makers can collectively contribute to a paradigm shift in the renewable energy sector.Through Read the full article

[technophili]

10 Groundbreaking Ways AI is Revolutionizing Scientific Research

I was wondering, is artificial intelligence really revolutionizing scientific research? Every day, new things are born that speed up scientific discoveries, and this gives us a certain advantage, since we often wonder if we could have done this or that 10, 20 or 50 years ago. Seriously, do you think that generation X could have imagined that a game like cyberpunk 2077 could exist? (personally, it's my favorite game, I love it too much!) Or get answers on command with artificial intelligence? Of course not! That's why today we're going to tell you what AI does at every stage of the research process, from hypothesis formulation to data analysis. It's going to be fascinating!

Accelerating scientific discovery

Credits: Image by jcomp on Freepik There's one thing that's important in all scientific disciplines if we want to use AI in scientific research, and that's the fact that it's capable of processing astronomical quantities of data, and the fact that it's capable of identifying patterns.

Credits: Image by freepik If I take genomics as an example (according to the dictionary, genomics is a branch of genetics that studies genomes (a genome is the set of hereditary material composed of nucleic acids (DNA or RNA) of a cellular organelle, organism or species)). So I was saying that if I take genomics as an example, AI would be very useful for analyzing huge datasets to discover which disease might be associated with a gene and vice versa.

Credits: image by freepik If we now take the environmental sciences, AI can be used to process data coming from sensors and satellites, so it can monitor climate change and predict natural disasters in advance, but of course at first it won't be at all accurate, but it will get better and better.Then there's the discovery and development of medicines. The way drugs are currently discovered is insanely time-consuming and costly, but if we used artificial intelligence, we'd be able to analyze databases of chemical compounds in no time at all, so we'd know whether they're effective or not, not to mention whether they're safe.

Credits: image on pexels Robotics and automation play an important part in this. Robots are designed to do the same tasks over and over again, so that's what they can be used for, and scientists can concentrate on other things. Another field of science in particular is materials science, where robots will be used to synthesize and test new materials in no time at all.

We can also improve data analysis and modeling.

Credits: stock photo by vecteezy It's important for scientists today to have AI models that are able to predict and make better simulations. And this could be particularly useful in climate science, for example, if we needed to know what impacts different global weather patterns might have, AI would be a great asset for making simulations. We'd even be able to understand the behavior of subatomic particles, and if you haven't got a clue, you should know that it's impossible to do that kind of thing if you were just trying to experiment with physics.On the one hand, if researchers were to use natural language processing technologies and knowledge graphs, this would help to blend different data sets, and would also be very useful if we needed to retrieve important information from the scientific literature.On the other hand, they could be used in biomedicine, because since it's its specialty to analyze data, it could do the same here by analyzing published research, so we could find potential drugs or even try other personalized therapeutic approaches.

 A warm welcome to the scientific research manager! 

An interesting study cited by techxplore,

Credits: Maximilian Koehler| ESMT Berlin

Credits: Henry Sauermann (@HSauermann) X.com published in Research Policy by Maximilian Koehler and Henry Sauermann, is examining a new role for artificial intelligence in scientific research: guess what it is! Well, as you saw in the header, it's the role of manager supervising human workers. This concept of algorithmic management(AM) represents a change in the way research projects are conducted, and could enable us to think bigger and operate on a larger scale and with greater efficiency.Koehler and Sauermann's research shows that it is indeed true that AI can replicate human managers, but it can also supervise them if we consider certain parts of research management. They identify five key managerial functions that AI can perform effectively:1. Task allocation and assignment2. Leadership3. Coordination4. Motivation5. Learning supportThe researchers studied various projects using online documents, interviews with organizers, AI developers and project participants, and even participated in some projects themselves. Thanks to this approach, it's obvious that we can find out which projects use algorithmic management, and it's also obvious that we can understand how AI manages to do all this.In fact, we're seeing more and more use of artificial intelligence in AM, and that's not good at all, absolutely not! Because by doing so, research productivity drops.  As Koehler states, quoted by Techxplore, "The capabilities of artificial intelligence have reached a point where AI can now significantly enhance the scope and efficiency of scientific research by managing complex, large-scale projects".So we're all asking the same question, what can be the: 

Key benefits of AI in research and education 

 According to the National Health Institute, AI could dramatically transform research and education through several key benefits:1. Data processing:as I mentioned above, AI's specialty is processing huge amounts of data which is a huge advantage for researchers who want to use elaborate datasets and like that they will be able to derive worthwhile insights. (National Health Institute, 2024).2. Task automation:as AI is capable of automating tasks, this can be useful for organizing certain tasks such as formatting and citation, and as it saves researchers time and energy, they can then concern themselves with more difficult and innovative work (National Health Institute, 2024).3. Personalized learning  AI can create personalized learning paths for students, tailoring the experience to their unique needs and learning preferences (National Health Institute, 2024). 

As usual, all is not so rosy 

I hope you already know that even in scientific research, all is not so rosy in terms of morality and challenges. If you remember, AI's specialty is actually analyzing data, so, as the National Health Institute makes clear, if it's just analyzing the same data over and over again, or even if it's just analyzing the same things in the same data over and over again, we can end up with predictions that are wrong, and that will lead to results that are downright bad and harmful. It's the same as when we use AI to write an entire article, the AI draws on the same data, and that's why we end up with articles that bring no value to the reader, lack personal experience and are plagiarism of other articles. The same goes for AI used to write film scripts: the more you use it, the more you'll realize that the scripts are all the same, so there's no originality left. It's a bit like the way it works with scientific research, except that here we're talking about sensitive data, especially in the fields of health and medical research. Let's not forget, too, that these biases can appear at any stage, whether in the collection of data or in the evaluation of models, so this kind of thing can lead to results that aren't true, and these results can influence the instructions given in clinics or medical interventions.Recent studies agree with this point, saying that these biases can lead to significant health disparities. If researchers are vigilant in identifying and reducing these biases, no problem! It's always important to make sure that the information generated by AI is fair and accurate, and not a hallucination . You don't want to be the guinea pig in a scientific experiment that's guaranteed to kill you, do you? The rise of AI-generated content in scientific publications is yet another dilemma to be solved, and why are we talking about this? Because the Cornell Daily Sun, reported that it has already happened that AI-generated articles containing, we must remember, totally absurd or fabricated information have been submitted to and even published in scientific journals. A perfect example occurred just recently, in February 2024, when Frontiers in Cell and Developmental Biology published an article entitled "Cellular functions of spermatogonial stem cells in relation to JAK/STAT signaling pathway".A day after publication, readers noted that the figures were undoubtedly AI-generated and contained spelling mistakes, diagrams that represented nonsense and anatomically incorrect illustrations. The journal withdrew the article within three days. It's because of stuff like this that it's important that we put in place robust peer review processes and clear guidelines on how we use and disclose AI in research publications. And at the same time, isn't AI being abused in academic publications? It's true! It's hard to maintain scientific integrity now that technology is advancing so rapidly.

Don't tell me that artificial intelligence is being used in paper mills!

I don't know if you knew this, but according to the National Health Institute, AI is even being abused in "paper mills" to produce fraudulent articles on a massive scale, and you wouldn't believe how much this use has led to an increase in the volume of false publications. And with all this, can we still believe in scientific research? I wonder. The fact that these factories use AI to generate text and images makes it increasingly difficult to know whether research is genuine or not, and that's not at all a good thing for scientific literature, which is supposed to have integrity.Also according to the National Health Institute, Gianluca Grimaldi and Bruno Ehrler address this issue in their book "AI et al: Machines Are About To Change Scientific Publishing Forever". They warn that "A text-generation system combining speed of implementation with eloquent and structured language could enable a leap forward for the serialized production of scientific-looking papers devoid of scientific content, increasing the throughput of paper factories and making detection of fake research more time-consuming".

So it's hard to detect AI-generated content?

It's true that publishers and editors have developed various software tools to detect similar texts and plagiarism, but that doesn't mean that AI-generated texts can be easily identified. However, there are various players in the academic and publishing world, such as publishers, reviewers and editors, who increasingly want to use the world's artificial intelligence content detectors, if you still haven't figured out how they're going to use them, basically, they just differentiate between texts written by humans and those generated by AI but even if there are some tools for that, they're not 100% reliable.

Advantages of AI in scientific publishing 

Leaving aside the challenges, let's think about what artificial intelligence has to offer in terms of advantages in the scientific publishing process. According to technology network, Dmytro Shevchenko, (not the footballer but) PhD student in computer science and data scientist at Aimprosoft, highlights several positive applications of generative AI (GAI) in publishing:1. Creating abstracts and summaries: we can use Large Language Models (LLM) to generate abstracts of research articles, and it's much easier for readers to understand what the conclusions and implications of the research are.2. Linguistic translation: LLMs can also make it easy to translate research articles into several languages, making research results more accessible and far-reaching.3. Text checking and correction: LLMs trained on large datasets can generate consistent and grammatically correct texts, which can improve the overall quality and readability of research articles (Technology Network, 2024).Andrew Stapleton, former chemistry researcher and current content creator for academics, agrees: "AI is a fantastic tool to streamline and speed up the publishing process. So much of the boring and procedural can be written faster (abstracts, literature reviews, summaries and keywords etc.)” 

AI policy developments in scientific publishing

According to technology network, the scientific publishing community has been debating how to start using AI in scientific research and writing. Early 2023, Many publishers adopted restrictive positions, with some, such as Science, banning the use of AI tools altogether. Herbert Holden Thorp, editor-in-chief of Science magazine, said: "The scientific record is ultimately one of the human endeavor of struggling with important questions. Machines play an important role, but as tools for the people posing the hypotheses, designing the experiments and making sense of the results. Ultimately the product must come from - and be expressed by - the wonderful computer in our heads"(Technology Network, 2024).However, given the rapid evolution of technology, many magazines have seen fit to change their policy. Science, for example, changed its stance later in the year, now allowing authors to declare how AI has been used in their work. Other major journals have done the same, so they require you to say whether you've used AI but are totally against using AI to generate or modify research images.(They're good Science, very good!)Policies vary from publisher to publisher:-  JAMA wants detailed information on any AI software used, including name, version, manufacturer and dates of use. - -Springer Nature has specific policies for peer reviewers, so they are asked not to upload manuscripts to generative AI tools if they don't have safe AI tools. - - Elsevier's policies accept the use of AI to write manuscripts so that readability and language are improved, but still require others to declare that they have used AI when they are ready to submit (Technology Network, 2024).

More policy implementation challenges? It gets boring in the end!

Despite these efforts, implementation and enforcement of AI policies in scientific publishing remain problematic. There's a recent incident and it involved an Elsevier journal that puts these difficulties in a new light when it published a peer-reviewed introduction, which, you guessed it, was generated by artificial intelligence. This particularly upset the public, who wondered whether we were really following the guidelines? (Technology Network, 2024).A study by Ganjavi et al. explored the extent and content of guidelines for AI use among the top 100 academic publishers and scientific journals. They found that only 24% of publishers provide guidelines, with only 15% among the top 25 publishers analyzed. The authors concluded that the guidelines of some leading publishers were "deficient" and noted substantial variations in the permitted uses of BGS and disclosure requirements (Technology Network, 2024).

Towards a robust framework for AI in scientific publishing

To meet these challenges, experts call for a comprehensive approach to managing the use of AI in scientific research and publishing. Nazrul Islam and Mihaela van der Schaar  suggest a multi-faceted strategy that includes:1. Developing comprehensive guidelines for the acceptable use of AI in research.2. Implement suitable peer review processes to identify and scrutinize AI-generated content.3. Foster collaboration between clinicians, editorial boards, AI developers and researchers to understand the capabilities and limitations of AI.4. Create a strong framework for transparency and accountability in the disclosure of AI use.5. Conduct ongoing research into the impact of AI on scientific integrity (Technology Network, 2024).Nevertheless, progress is already being made in developing these frameworks. The "ChatGPT and Generative Artificial Intelligence Natural Large Language Models for Accountable Reporting and Use" (CANGARU) Read the full article

[technophili]

The 12 Most Mind-Blowing Ways AI Is Innovating In Farming

The agricultural sector is developing thanks to artificial intelligence (AI), which is transforming our farming practices, from sowing to harvesting. As we can see today, the world's growing population is only getting stronger, and climate change is bringing new challenges that we need to address, which is why we need artificial intelligence not just in finance or machine learning, but also in agriculture, because we need it to increase crop yields, to conserve resources and also to create food systems that will be more sustainable. This article explores the current state and future potential of AI in agriculture, drawing on expert opinion and real-life examples.

Why innovation is needed in agriculture ?

According to the Food and Agriculture Organization of the United Nations (FAO), quoted by Forbes, the world is going to have to produce 60% more food than we have by 2050 in order to feed a projected population of 9.3 billion. But right now, there are a number of major challenges in the agricultural sector that may mean that this goal will (perhaps) never be reached. At least for the time being.Rajesh Singh, professor at Lovely Professional University and co-author of "Artificial Intelligence in Agriculture", underlines just how urgent this situation is: "The agricultural industry is at a critical juncture. Traditional farming methods are struggling to keep pace with growing demand and environmental pressures. AI offers a promising path forward, but its implementation must be thoughtful and inclusive."The current challenges facing agriculture are as follows:1. Pest damage: According to Forbes, pests destroy around 40% of everything produced in agriculture every year, causing losses of at least $70 billion. Their impact is widespread, with locusts in Africa and fruit flies damaging orchards all over the world, so it's damage that also affects the economy.2. Soil degradation: According to Forbes, nearly 33% of the world's soils are degraded, reducing their capacity to support crop growth. This results in an estimated loss of around $400 billion a year.3. Water scarcity: Again according to Forbes, agriculture uses 70% of the world's fresh water, but 60% of this is wasted through leaky irrigation systems and inefficient farming practices.4. Weed proliferation: According to Forbes, some 1,800 weed species reduce crop production by around 31.5%, resulting in economic losses of around $32 billion a year.5. Post-harvest losses: The World Economic Forum notes that in countries like India, 40% of produce is lost in the supply chain due to inadequate storage, transport and market access. These problems are particularly acute for small-scale farmers. Anita Gehlot, co-author of "Artificial Intelligence in Agriculture", explains "Smallholder farmers, who produce a significant portion of the world's food, often lack access to advanced technologies and face disproportionate risks from climate change and market fluctuations. AI solutions must be designed with their needs in mind." The World Economic Forum gives us a sad example of the challenges with the story of Krishna, a small farmer from Telangana, India. He actually cultivates an acre of land, but Krishna earns just $120 a month - not even enough to give his family what they fundamentally need. For Krishna and millions of others like him, farming is a gamble that involves enormous risks, and it doesn't pay very well. That's why we're all asking the same question 

How is AI revolutionizing agriculture? 

Thanks to artificial intelligence and other AI-dependent technologies such as machine learning, computer vision and the Internet of Things (IoT), we have other ways of making these long-standing agricultural challenges a distant memory. As Mahesh Kumar Prajapat, also co-author of "Artificial Intelligence in Agriculture", notes: "AI's ability to process vast amounts of data, recognize patterns, and make predictive analyses is being applied across the entire agricultural value chain, from soil preparation to post-harvest logistics."Let's see how AI is transforming different aspects of agriculture: 1. Crop and soil management: Thanks to AI, the way farmers monitor and manage their crops and soils is going to change completely. -Crop yield prediction through AI:in effect, this means that learning algorithms have the ability to analyze historical data, weather patterns, soil conditions and satellite imagery to predict crop yields with an accuracy that will only increase.This gives farmers greater clarity when it comes to planting, and can even extend to resource allocation and harvest scheduling. There's also- Machine learning for pest and disease detection: Image-recognition systems powered by artificial intelligence can detect any signs of pest or disease infestation in crops, and that's before humans can notice anything. I'd also like to mention Automated irrigation systems:If AI starts analyzing soil humor levels, weather forecasts and crop water requirements, it could perhaps make irrigation better. According to forbes,CropX, a company specializing in precision agriculture, reports that thanks to their AI solutions, they have been able to reduce water use by 57%, while at the same time increasing yields by up to 70%.Crop monitoring by drone: which can rapidly survey large areas and provide information on crop health, growth patterns and problems that can appear in great detail. AI-assisted soil health analysis:here, if machine learning models happen to analyze soil samples and sensor data, they will potentially be able to give information on soil composition, nutrient levels and overall health at depth.2. Livestock managementBhupendra Singh, also co-author of "Artificial Intelligence in Agriculture", talked about how AI could impact livestock farming."AI is transforming livestock management through advanced monitoring and predictive analytics, leading to improved animal welfare and productivity."Among the main applications we have, for example, the: AI-assisted animal health monitoring:here we may be talking about how wearable captures and AI algorithms can monitor vital signs, movements and the way animals behave in relation to their feed in order to detect early signs of disease, there are also:Automated feeding systems:where AI can choose the best feeding times and portions according to each animal's needs, improving nutrition and at the same time reducing waste. And let's not forget Behavioral analysis: in this case, it analyzes the way animals behave in order to predict certain events in advance, such as   estrus in dairy cows, enabling us to set up more effective breeding programs.3. Farming The AI organizes the overall management and operations of the farm:Resource optimization: this involves enabling algorithms to analyze different data streams, so that they can optimize the way in which resources such as water, fertilizers and labor are used throughout the farm. Weather forecasting: that's nothing new! I think many of you already know that AI is capable of giving us weather forecasts in every locality, and this could help farmers to make important decisions about planting, for example: when to plant? When not to? How to do it? When to do it? There will also be important decisions to make when harvest time comes, and other decisions too about how to protect crops. That's why there's Agricultural data analysis: made possible by AI-powered dashboards. And what's important to know here is that since they are in a position to integrate data that comes from different sources (sensors, machines, market prices), they will be able to provide farmers with information that they can use and thus make the way they make decisions much better.4. Supply chain managementAI improves efficiency and transparency throughout the agricultural supply chain:Blockchain for traceability:blockchain solutions can track agricultural products from farm to fork, thereby enhancing food safety and enabling consumers to check where their food comes from and where it has been before it reaches their plates.AI-enabled inventory management: we could optimize stock levels with artificial intelligence, thereby reducing waste and ensuring that agricultural products are delivered as quickly as possible..Demand forecasting:we'll be able to analyze market trends, the way consumers behave and other external factors to know in advance which agricultural products will be in greatest demand, so farmers and distributors can plan better.

In fact, AI in agriculture is also having a certain impact in the real world, as it..

  ...is already being felt worldwide. Here are a few compelling examples:1. Precision weeding: according to Forbes, the LaserWeeder, an AI-powered weeding system, claims to eliminate up to 5,000 weeds per minute with 99% accuracy. Farmers using this technology say they have been able to cut their weeding costs by up to 80%, and they have a return on investment that can be made in as little as one to three years!2. Empowering small-scale farmers:The World Economic Forum had mentioned in their article an 18-month pilot program that was done in India to test digital advisory services that used artificial intelligence and was aimed at small-scale farmers, and the results they got were insane.-     Net income doubled to $800 per acre in a single crop cycle (6 months). -    Chili production increased by 21% per acre. -     Pesticide use decreased by 9%. -     Fertilizer use decreased by 5%. -    Crop price increased by 8% because of improved quality. -  Crop yields increased by 23%. - Water use was reduced by 30%. - Fertilizer and pesticide use were reduced by 20% and 50% respectively. - Overall profitability improved by 35%. - The farm's carbon footprint has been reduced by around 25%.Rajesh Singh comments on this case study: "This example illustrates the holistic impact AI can have on farm operations. It's not just about individual technologies, but how they work together to create a more efficient, productive, and sustainable farming system.”

Conclusion: The changing face of agriculture

In the future, it's clear that AI will play an increasingly central role in agriculture. From small farms in India to large industrial farms in the USA, AI technologies are helping farmers to produce more food with fewer resources, while reducing their impact on the environment.Anita Gehlot concludes: The integration of AI in agriculture represents a paradigm shift in how we approach food production. However, as we embrace these technologies, we must ensure that their benefits are widely shared and that we don't lose sight of the fundamental connection between humans and the land."As we move into this new era of algorithmic agriculture, we must strive to ensure that the benefits of these technologies are widely shared, that their implementation is environmentally sustainable, and that they serve to enhance rather than replace the rich tradition of human agricultural knowledge. In so doing, we will be able to write a new chapter in the age-old history of human agriculture, one in which silicon and soil work together to feed the world.The authors of "Artificial Intelligence in Agriculture" remind us that, while artificial intelligence offers powerful tools for tackling the challenges facing agriculture, it is not a panacea. Its successful implementation will require ongoing research, thoughtful policymaking and a commitment to inclusive development that benefits farmers at every scale and in every region.At the intersection of traditional farming wisdom and cutting-edge technologies, the future of agriculture promises to be both exciting and complex. By harnessing the power of AI responsibly and equitably, we have the opportunity to create a more resilient, sustainable and productive global food system for generations to come.  Read the full article

[technophili]

AI is Taking Over Trading.Here’s What You Need to Know

Thanks to the advent of artificial intelligence, we've been able to improve the way we trade, invest and manage our risks. When I read Federico Cecconi's "AI in the Financial Markets: New Algorithms and Solutions" and with the latest research I've done on this sector, I gained a lot more understanding of the technological revolution in investing and its far-reaching impact. So I say to you.... Happy reading!Reinforcing algorithmic tradingI'd like to point out that AI-powered algorithmic trading has done something remarkable for the financial markets. According to a report by Infomineo, AI tools allow traders to take into account economic conditions, market trends, trading strategies that are complicated, in fact, I would say they take into account several factors.

The high-frequency advantage

High-Frequency Trading (HFT): What It Is, How It Works, and Example-Investopedia  There's something called high-frequency trading (HFT), and it has found a kind of strong ally in AI. The infomineo study reveals that, thanks to AI, people involved in HFT can get what's called an autonomous value chain... in fact you don't need to know what it is, who cares, just they'll reduce execution times to a few microseconds. That's the difference between profit and loss in fast-moving markets.

Trading software AI: your digital market analyst

Close your eyes, close your eyes! And think of a market analyst who works non-stop, 24 hours a day, seven days a week. Now, I know that 99.99999999% of you haven't really closed your eyes, but I wanted to give you an idea of how modern trading software works with AI. It's crazy when you consider that they can monitor thousands of stocks at the same time and analyze market trends in real time. Not to mention the fact that they give instant stock recommendations and alert traders directly to how prices are moving. As I learned from Cecconi's book, AI trading platforms go so far as to test strategies and run simulations, and as traders have a kind of virtual trial, so they can make their approaches better.

The brains of financial AI: machine learning

Machine learning is the head of the whole thing. Machine learning algorithms are able to analyze large datasets and discover patterns that the human eye can't see, improving the way we make decisions without the need for an emotional being, as Forbes points out.Adaptive trading strategiesWhen it comes to machine learning, there are a few things that are captivating about it, and that's its ability to adapt. In fact, current trading algorithms are designed to obey strict rules. What makes the difference, then, is that systems powered by machine learning have every right to adjust their strategies, which are, let's not forget, real-time, according to the way market conditions are evolving. AI-driven trading strategies: Outperforming the marketIt's great when AI improves existing strategies, and even better when it creates brand new ones. From sentiment analysis  to predictive modeling, there's plenty to choose from.The wheel of algorithms: AI's traffic controllerWhen I was reading Mr. Cecconi's book, there's one sick evolution I came across, and that's the "algo wheel". It's a kind of traffic controller for trades and so they send orders to the algorithms and brokers that are most efficient and it depends mostly on real-time market conditions. So, since they're going to reduce the presence of humans to make trades, it promises performance and efficiency with "algo wheels".Sentiment analysis : Market moodAs I mentioned in my previous article on natural language processing, AIs are getting better and better at assessing market sentiment. When they analyze newsletters, social network messages and even corporate earnings calls, they'll be able to detect the tiniest changes that humans wouldn't be able to see at all.

Real-world applications: AI in action

Don't think that all this is just blablabla no jutsu like Naruto, absolutely not! He already has real results from everything I've said above. I'd like to show you a few examples.Nasdaq's AI-powered order typeNASDAQ has introduced a type of order that works with AI, so they've given orders to an AI and thanks to this, there's a 20.3% improvement in execution rates and an 11.4% reduction in plagiarism.BlackRock's Aladdin

BlackRock’s Aladdin technology: Touching all aspects of an evolving investment ecosystem- Reinsurance News    Investment giant BlackRock came up with the idea of turning its risk management function into a way of making a lot more money with Aladdin, an AI-based software tool for risk assessment and portfolio management.Goldman Sachs' automated trading deskHere's a nugget that shows just how much AI is impacting the financial market, and it's Goldman Sachs. In its US equities trading desk in New York there were only 600 human traders 2000 and in 2017 there were only two, simple! AI systems had taken over, humans were useless.

The future of AI in financial markets

The more time passes, the more the time when artificial intelligence will have almost unlimited potential accelerates. From what I've read of Cecconi's work and industry trends, these are some of the developments we can still look forward to.Hyper-customized investment strategiesWe could create investment strategies that are suitable for risk profiles thanks to AI, and these strategies could also be suitable for objectives and even for what each investor prefers in terms of ethics. And all in real time.

Ethical considerations in AI-driven finance

As AI spreads further and further into financial markets, ethical considerations become a property. So there are issues we need to tackle, such as fairness, transparency and accountability, so that AI can benefit all players in the market.The black box problemAnother challenge of AI in finance is its "black box" nature. In fact, as systems become increasingly complex, it's important to guarantee transparency, as I said above, but also explicability, particularly for regulatory compliance.Systemic risk For systems that control a part of the larger market, the fact that these systems malfunction or act in an unexpected way can entail a risk of cascading failure, which is why it's important to have safeguards and security devices in place.

Conclusion

 The key to harnessing the full potential of AI in finance will be to strike the right balance between technological progress and ethical considerations.That's why I'm saying that whether you're a seasoned trader, a curious investor or just someone who's interested in what the intersection of technology and finance might look like, staying informed about the role AI plays in the markets isn't something you can choose or not, it's an obligation. Read the full article

[technophili]

How Machines Learn to Speak Our Language. Understanding NLP for dummies

Natural language processing (NLP) has come a long way since its inception in the middle of the 20th century. Without boasting, I'm a direct witness to the transformation of this field, and can testify to how far we've come from tiny, rule-based systems to today's most advanced artificial intelligence models. As well as changing the way machines understand and process human language, it has definitely changed the way we interact with tech.

The beginnings

It all started with fairly old-fashioned attempts to analyze and understand human language. And so was born the Georges-IBM experiment in 1954, which at the time was insane because it showed the potential that machine translation could have. Even though this experiment was limited in terms of technology or hardware, there were some nosy little investors who had a particular interest in NLP. And now that computing power is becoming monstrous, along with the evolution of language theories, NLP systems have been upgraded from rule-based approaches to stable methods in the 1980s and 1990s.

The language learning machine

Basically, NLP was just about machines understanding, interpreting and generating human language. And to achieve this process requires a very difficult analysis of syntax, semantics and pragmatics. These words may be difficult for the average person, but they are in fact the elements that make up linguistic understanding.Syntax

Syntax Tree – Natural Language Processing-GeeksforGeeks In fact, syntax is there only for the structure of language, looking at how words come together to form sentences. Natural language processing uses grammar to analyze sentences, looking at the parts of speech and how words relate to each other.Semantics 

Semantic Features Analysis Definition, Examples, Applications - Spiceworks Inc  Semantics is more about the meaning of words and sentences, so it enables machines to understand what is written in the text, and this level of understanding goes far beyond the way it is formed grammatically.Pragmatics

Pragmatics in NLP- Scaler Now, pragmatics takes context into account, which helps natural language processing systems to understand the meaning they are intended to convey, according to the situation at hand.I'd also like to point out that these components work together, enabling NLP systems to process language in a way that mimics the way humans use that language.

Digital linguists at work

Before NLPs can examine a text, the raw input must first be processed. This is an important step, as it transforms unstructured text into a format more suitable for automatic examination. Pre-processing text requires a number of techniques, each of which is important in preparing the data for NLP tasks.Tokenization 

Understanding Tokenization in NLP!-WordPress.com Tokenization is often the first step, breaking down the text into smaller units called tokens. These can be words, phrases or even individual characters, I don't know, but it depends precisely on the NLP task. For example, I take the sentence "the dog sat on the sofa", you can see that it can be transformed into . So that's pretty much how it works.Lemmatization and steaming 

Day 4: Stemming and Lemmatization- Nomidl Then there's stemming and lemmatization, which are techniques we use to reduce words to their original form.  Stemming is a quick and simple process, a little crude you may say, and it cuts off the ends of words. For example, " running" becomes "to run".Lemmatization, on the other hand, uses morphological analysis to restore a word to the form it originally had in the dictionary. So, if you've understood, stemming may reduce "better" to "bet", but lemmatization will know perfectly well that "good" is its basic form.

Silicon polyglots

If you haven't already heard, machine translation has been the Holy Grail of NLP since its inception. The systems they had in the early days used direct translation, so they'd take a word, translate it, take another word, translate it and so on, often with funny results (I think that's how Google translation works, wouldn't you agree?). On the other hand, the translation systems we have today use highly sophisticated neural networks that are trained on large quantities of multilingual data, i.e. from several languages.And you and I both know that today's systems are capable of translating around a hundred languages with astonishing accuracy. They don't just translate word by word, like Google translation; they understand the context, they understand idiomatic expressions and they're able to maintain, as it were, the tone of the original text.But bof! There are always challenges to improving when it comes to understanding multiple languages. If there are languages with structures that are totally different, or if there is a lack of digital resources, this can pose problems. Researchers are therefore obliged to explore other techniques, such as learning from scratch and unsupervised machine translation, to improve on the challenges these systems will face.

Sentiment detectives

There's something called system analysis, and it's all about knowing the emotional tone of a text. So whether you want to analyze customer comments or gauge political opinion on social media, these sentiment analysis tools have become indispensable to businesses and organizations.You should know that these systems have evolved away from simple keyword-based approaches. Now they've become advanced models and can understand contrarianism, sarcasm and sentiments of all kinds. I'd also add that they can classify texts as positive, negative or neutral (if you're a blogger and use Rank math for your SEO, you'll notice that they give you a point when you use a positive or negative word in the title of your article) and there are even some systems that can detect specific emotions like anger, joy or surprise.I'd also like you to know that the applications are numerous. For example, companies are using sentiment analysis to monitor what people think of their brand, so they can improve customer service.For their part, financial institutions analyze market sentiment to predict stock market movements.And social media platforms use these tools to detect and hide the spread of negative or harmful content.

Question answering systems

Here, I'm going to talk about question answering (QA) systems. They used to be simple algorithms that allowed models to communicate with each other. Today, they understand and depend on complex requests. Previously, the only thing these systems did was limit themselves to specific domains and answers that had already been programmed, so they followed rules.Now, QA systems benefit enormously from deep learning and understanding natural language to process questions in context, search a vast library of knowledge and generate human-like answers. This is what powers Alexa and Siri, customer service chatbots and enterprise search tools.When transformer models like BERT and GPT were developed, they greatly improved the way quality assurance systems performed.

Chatbots and digital assistants

Let's face it, we've all been blown away by the evolution of conversational AI. Back in the 1960s, when the first chatbots like ELIZA were created, they used a then-trivial correspondence model to simulate conversations. But now, it's sophisticated NLPs that power these chatbots and digital assistants we have today. Not only can they understand context, they are also able to retain long-term memory and even adopt a kind of personality of their own.If we take customer service as an example, they're there to deal with routine inquiries, leaving human agents to concentrate on other things.If I take the field of healthcare, chatbots are able to provide simple symptom assessment and mental health support.And if I go to the education side, they can be used to help students answer their questions and offer personalized learning experiences. Unless those chatbots don't lose their marbles and tell the students some nonsense like these kidsUnless those chatbots don't lose their marbles and tell the students some nonsense like these kids

NLP in the wild

I'd like to delve a little deeper into what NLPs are capable of in healthcare. So, I'd like to mention that NLPs can analyze medical records, help with diagnosis and retrieve information from scientific literature.There are even legal professionals who use NLP tools to review contracts, and they are just as indispensable for carrying out due diligence and analyzing jurisprudence, i.e. the law or the constitution.I'm also thinking of finance, where NLPs power algorithmic trading systems that analyze news and social media to make investment decisions.Marketing teams use NLPs to make content better, so they analyze trends and personalize advertising.What's crazy and funny, and surprising at the same time, is that creative fields aren't leaving NLP behind. Like, it's used to generate content, it helps write automatic subtitles for videos.

The ubiquity of NLP

I myself have seen how NLP is developing, and how its presence in our everyday lives is only growing. If I remember correctly, last week, I smiled a little because I saw how to communicate with my smart home appliance in a totally ultra-fluid way. It was just crazy, it understood when I said to "dim the lights a bit" and I assure you I didn't give any other precise commands, it's just that, but it set the brightness to a level I liked I'd say COMFORTABLE, I was fine!!!Even in blogger work, I use them, for example Grammarly to correct grammar, and it also suggests style improvements, it helps me maintain a consistent tone and if I use for example Surfer SEO or Ahref, it sometimes helps me generate content ideas (I confess this article in fact I didn't have the idea, it came from Chatgpt, I wanted to test how generated content ideas, were they good? Did they have a medium level of competition? because often if you ask GPT to generate keywords with low competition it will generate keywords that have no competition at all, like a score of Zero)The impact of NLP on societyAccording to a report from Syracuse University, the impact of NLP on society is profound and far-reaching. The report states:NLP is used in AI chat bots and automated phone support to help diagnose issues without the need for a person in a call center. NLP is also used in automatic language transcription and translation, such as with automatic subtitles in YouTube. Researchers use NLP regularly to scrub websites for information and analyze that information for keywords or phrases. This report also says that NLP was born in 1906, when Swiss language professor Ferdinand de Saussure created several courses at the University of Geneva. In this course, it was said that language was like a system in which sounds were like concepts, and these concepts had meanings that changed according to context. This work, later published under the title Cours de Linguistique Générale, (General linguistics course in English ) laid the foundations for what we know today as NLP.According to Syracuse university In the future, we'll (perhaps) have machines that pass the Turing test with flying colors, and if all goes well, we'll also have better real-time translation of speech and text.

Conclusion

Chatterbox, I know this article has been long and I apologize for that, I hope if you've made it this far it's because you're dying to know about Natural language processing or you're a technophile. But thank you for reading this far and I'll leave you saying that I totally agree that NLP will continue to improve our world, whether in communication or to enable more natural interactions with AIs, but if it's developed irresponsibly without taking into account privacy, prejudice and the impact on society, there will be a serious problem. At the same time, I'm discovering how ingenious humans are, and how they're always trying to make machines understand better. It's totally interesting! Read the full article

[technophili]

AI-Driven Cars Have Transformed from Science Fiction to Street Reality in 2024

Have you ever caught yourself dreaming of driving around town in a self-driving car?Get ready, because this future is right around the corner, and you don't even know it, because the race to build cars is at its peak, and artificial intelligence is the central pillar.But before we dive headlong into this field, I'd like to stop for a moment and think about why anyone would want a self-driving car. Try to imagine this scenario: you're driving along the freeway and suddenly you see a chicken and a sloth playing pickleball on the side of the road (sorry, that's the best I could come up with). In a normal car, you'd probably be concentrating on the road and miss this strange sight (which, let's face it, sucks). But what if you were in a self-driving car? You'd be able to enjoy the show as you please, without fear of finding yourself in oncoming traffic.

The need for autonomous cars

Let's face it, the previous example was the lamest of them all, and it's certainly not the most appropriate. But I'd like you to keep this in mind: around 90% of car accidents in the United States are due to human error, according to the National Highway Traffic Safety Administration.  Let's face it, we humans are broken drivers. We're (always) distracted, we're tired, we make bad decisions. Why do we behave as if we had 16 at the wheel?It's because of this kind of attitude that AI steps in, and promises to be wise and responsible adults in the car.the AI doesn't get distracted by an incredible song on the radio, or by a discussion about whether that's Ryan Reynolds we've just passed on the street - just the opposite of us. It keeps its eyes (that's a metaphor okay!) on the road at all times.

How does AI power autonomous vehicles?

The question on everyone's mind is how can AI enable a car to drive itself? Well, it's not magic (even if it looks a little like magic, that is). Rather, it's about giving the car a 2.0 version of the five senses, and a brain to make sense of the hubbub.

Key AI Technologies Enabling Self-Driving Cars

The Car's Eyes and Ears

Lidar in Autonomous Driving: Pros, Cons & More dubizzle First of all, we have the car's eyes. They come in the form of cameras, radars and a device called LIDAR (which looks like a radar, but with lasers - in case you didn't know, this technology was used in Avengers Infinity War, the scene where we see Thanos' ship land on a city - it wasn't a photo, a video or even a 3D city). These sensors scan the environment all the time, looking for absolutely everything, whether it's other cars, pedestrians or even the chicken and sloth we mentioned earlier ( Good! I promise this is the last time we'll talk about this stuff)

Vehicle tracking using ultrasonic sensors & joined particle weighting Semantic Scholar  Next, we have the ears. These are ultrasonic sensors that work like bat sonar, bouncing sound waves off nearby targets to estimate their distance. And it's very useful for parking, because, let's be honest, not even the AI wants to parallel park if it can avoid it.then there's the sense of touch, which in this case consists of feeling the car's movements. Accelerometers and gyroscopes track the car's speed and position, a bit like the inner ear does for humans (but without the risk of discomfort in the car, otherwise it would have been weird).The Brains of the Operation

active learning and autonomous-vehicles-cloudfactory.com All this sensory information is fed to the car's brain, a sort of hyper-powerful on-board computer running artificial intelligence algorithms. And this is where the magic happens. The AI will use all this data to build a real-time 3D model of the world around the car. It's a bit like The Matrix, but instead of dodging bullets, it dodges potholes and pedestrians.Machine Learning: Practice Makes Perfect But it's not enough for AI to see the world, it has to understand it. For this, we need machine learning. When it analyzes millions of hours of driving data, AI will learn to recognize what I'll call patterns and make anticipations, or in more appropriate terms, predictions.If a car overtakes, is it likely to change lanes? That pedestrian, is he about to step off the sidewalk? AI simply asks itself these kinds of questions all the time, answering them on a regular basis and far more quickly than a human ever could.Path Planning and Navigation 

Autonomous Driving and the Need For Motion Planning- Realtime Robotics Once the AI has understood what's going on around it, it needs to decide what to do. This is where it really starts to get exciting (and a little philosophical, I'd say). The AI has to be programmed with a set of rules and priorities. Should it prefer the safety of its passengers to that of pedestrians? How should it handle moral situations like the famous cart problem?These are difficult questions, and it's because of situations like these that we're all driving around in self-driving cars.But progress is being made. Companies like Tesla, Waymo and Uber are traveling millions of kilometers in autonomous cars, and they're teaching their AIs how to handle all kinds of situations, from rush-hour traffic to unexpected weather conditions.

Levels of Vehicle Automation 

SAE Levels of Driving Automation™ Refined for Clarity and- SAE international Speaking of progress, let's talk about autonomous driving levels, because if you didn't already know, the Society of Automotive Engineers (SAE) has kindly and carefully only determined six levels for us, from 0 to 5Autonomy levels- Level 0 corresponds to grandfather's old pickup truck, with no automation whatsoever. No automation whatsoever. - Level 1 can, and I mean can, have cruise control or lane-keeping assistance. Other than that, zilch. - We're currently seeing a lot of Level 2 cars on the road, which can handle certain driving roles and nothing more, but still need a human to take over at any given moment. - It's at level 3 that we get to caress autonomous driving a little. These cars can handle the majority of driving tasks, and still need WE, the human saviors, to take over. - Level 4 cars can drive themselves in most conditions, but when the going gets tough and the going gets a bit red, like the weather, they gently give way to humans. - And level 5? This is the Holy Grail, the pearl of pearls, a car capable of driving itself, in all conditions, in all situations, without the need for human intervention.Now, I've had some personal experiences with this technology that I'd like to share. I recently test-drove a car with Level 2 autonomy, and let me tell you, it was both exhilarating and terrifying. Watching the steering wheel move on its own as the car navigated a curve was like witnessing magic. But I also found myself constantly hovering my hands over the wheel, ready to take control at a moment's notice. It's a stark reminder that we're still in a transitional phase, where AI driving automation in the automotive industry is impressive but not yet perfect.Technical ChallengesYou may be thinking: all this sounds cool, but when am I going to buy this thing? That's the million-dollar (or multi-billion-dollar) question.The truth is, we're not there yet. While companies like Tesla are still inventing advanced driving assistance systems, truly autonomous vehicles are still a long, long way off - in fact, they're still in the testing phase. Personally, I couldn't care less about autonomous cars, it's not something I'm looking forward to, but if autonomous cars tempt you, some analysts think that by 2030, up to 15% of cars sold could be fully autonomous. It may not sound like much, but in 2010 electric cars accounted for less than 1% of new car sales. Today, they account for almost 10% in many countries. Technology has a way of surprising us.The catch (there is always a catch)As always, and this is starting to get on my nerves, there are challenges to be met. One of the most important is ensuring that these AI drivers are capable of handling unpredictable real-world events. It's one thing to navigate a well-mapped city street, but not the same for a country road that's not on any map, or a construction zone that appears overnight. These are the kinds of scenarios that keep AI engineers up at night.There's also the question of public confidence. There are many who disagree or are afraid of entrusting absolute control to a computer. It's going to take time, time and more time (I don't think the 2030s are still relevant.) and proof of safety, so there's a lot of proof of safety before most of us feel comfortable taking a little nap while our car takes us to work. In any case, if we can be persuaded to implant a neuralinks chip in our brains, this trick will be a piece of cake.And yes! We're already entrusting our lives to artificial intelligence. Every time you board a commercial aircraft, you're trusting the artificial intelligence systems that manage much of the flight. And without lying to us, most of us trust Google Maps more than our own sense of direction.Is it worth fighting for?By the time we get to fully autonomous cars, we'll have traveled a whole long and boring, with plenty of potholes along the way. But if we consider the benefits it could bring, it might just be worth it. Isn't a world where road accidents are drastically reduced, where the elderly and disabled can move around freely, where traffic flows like water because every car communicates with every other car, worth it?Yes! it's worth working for, even if we have to deal with a few bugs along the way. After all, as any programmer will tell you, the first step in fixing a bug is to find it. Not to mention that test vehicles cover millions of miles every year, which could eliminate these bugs at a hell of a rate.So if you happen to be stuck in those pesky traffic jams and think about this article and the day when you'll be able to sit back and let your car do the driving, remember our little secret: that day will come. Maybe not tomorrow, maybe not next year, maybe not even in the next 10 years. But it's coming. And when it does, it will change the way we travel forever. 

Conclusion

In the meantime, keep your eyes on the road. And if you happen to see a chicken and a sloth playing pickleball, well ( damn! I promised I wouldn't talk about that thing anymore)... maybe it's time to stop and check if you're dreaming. Or if you've stumbled across a Pixar movie. Either way, it might be best to let a human take the wheel on this particular journey. Read the full article

[technophili]

AI vs human bosses: who leads better in the workplace?

AI at workplace is becoming increasingly common. From chatbots handling customer service to algorithms optimizing supply chains, artificial intelligence is transforming how we work. Now, a new question arises: Could AI in management be the next big step? Let's explore whether having an AI boss might be better than your current human one.

Pros of AI bosses

 AI leadership could offer several advantages:1. Reduced bias and favoritism Human managers often have unconscious biases that affect their decisions. An AI supervisor might avoid these pitfalls. According to a survey reported by Tech.co, 28.2% of US workers believe a robot boss would make less biased decisions than humans. Furthermore, 28.8% think AI would lead to lower levels of discrimination in the workplace. 2. Efficient task management and planning

Photo by cottonbro studio: pexels   AI at workplace excels at organizing and optimizing tasks. A study at Student Marketing Agency, reported by BBC News, demonstrated this capability. An AI manager achieved a 44% success rate in getting employees to pre-plan their workdays. It also motivated employees to log in on time 42% of the time. These figures were comparable to human managers' performance. 3. Data-driven decision making

Photo by Burak The Weekender: pexels   AI in management can process vast amounts of data to make informed decisions. This ability could lead to more objective and efficient project management. AI could analyze performance metrics, market trends, and resource allocation to optimize business strategies. 4. Consistent performance Unlike human bosses, AI doesn't get tired, emotional, or distracted. A robot boss could provide consistent management 24/7, potentially leading to improved productivity and employee satisfaction. Let's explore how AI might excel at certain leadership skills: Setting clear objectives: AI could use data analytics to create precise, measurable goals for each team member. It could track progress in real-time and adjust objectives as needed, ensuring that everyone always knows what they're working towards.  Rewarding good initiatives: 

Photo by kues1: freepik  An AI boss could consistently recognize and reward good work. It could use predefined criteria to identify outstanding performance and provide immediate positive feedback. This consistent recognition could boost employee morale and motivation.Decision-making skills: AI can process and analyze vast amounts of data to make informed decisions. It could consider multiple factors simultaneously, weigh pros and cons objectively, and make choices based on logical reasoning rather than emotion or gut feeling.

 Cons of AI bosses

 Despite these potential benefits, AI leadership has significant drawbacks: Lack of emotional intelligence AI struggles with understanding and managing emotions, a crucial aspect of leadership. Human bosses excel at:  Building trust: 

Image by freepik  Human leaders create a safe, supportive environment by showing empathy, being vulnerable, and consistently following through on commitments. They understand the nuances of human interaction and can adapt their communication style to each team member's needs. Enabling knowledge exchange: 

Image by freepik  Effective leaders foster open communication by creating an environment where people feel comfortable sharing ideas. They facilitate brainstorming sessions, encourage diverse perspectives, and know how to navigate complex interpersonal dynamics to ensure everyone's voice is heard. Personifying company values: Human bosses embody the organization's principles through their actions and decisions. They can explain the reasoning behind company values, demonstrate them in challenging situations, and inspire others to align with these principles. Caring for others: 

Photo by Edmond Dantès: pexels  Genuine concern for team members' well-being is a uniquely human trait. Good leaders check in on their team's mental health, offer support during personal challenges, and create a work environment that prioritizes work-life balance.  Limited creativity and innovation AI might struggle with creative thinking, which is crucial for innovation and problem-solving. Justin Berg's research, as reported by Sushantvohra on Medium, shows that managers aren't always best at spotting good ideas. Human bosses can engage in both divergent thinking (generating creative ideas) and convergent thinking (evaluating those ideas). AI, primarily relying on existing data and patterns, might struggle with truly innovative thinking.  Difficulty in handling complex, nuanced situations Many workplace scenarios require understanding context, reading between the lines, and making judgment calls based on subtle cues. An AI supervisor might struggle with these nuanced situations, potentially leading to misunderstandings or inappropriate responses.

 What people think about AI bosses

 Public opinion on AI in corporate management is mixed. A survey by Business Name Generator, reported by Tech.co, revealed interesting insights: -  One in five workers would prefer an AI boss, believing it would do a better job than their current human manager. - However, 80% of employees aren't ready for AI managers. -  40% would be scared if AI replaced their boss. -  About two-thirds of 18-24 year-olds are open to AI bosses. - Only 12% of people over 55 like the idea. - 30% of UK workers in Arts and Culture welcome AI bosses - 23% in HR are open to the idea - 19% in Manufacturing and Utility, and Finance agree -  17% in Healthcare would accept an AI boss -   One group was managed by humans -  Another by AI -  A third by both AI and human managers -  The AI-only and human-only groups achieved about 45% success in getting employees to plan their day and log in on time. -  The group with both AI and human managers performed significantly better, with 72% of workers planning their day in advance. - Project management AI: Scheduling, resource allocation, and progress tracking - AI inventory management: Optimizing stock levels and supply chain logistics -  Data analysis: Processing large amounts of data to identify trends and insights Meanwhile, human managers could focus on skills that AI struggles with:  Communicating transparently: Human leaders can share information honestly and clearly, adapting their communication style to their audience. They can handle sensitive topics with tact, address concerns empathetically, and foster an open dialogue within the team. Listening skills: Understanding team members' needs and concerns often requires reading between the lines. Human bosses can pick up on subtle cues, interpret the emotional context of a conversation, and respond with empathy and understanding. Having convictions: Strong leaders stand up for what they believe is right, even in the face of opposition. They can articulate their values, make principled decisions, and inspire others to uphold ethical standards. Being innovative: Human creativity allows leaders to come up with novel solutions to complex problems. They can think outside the box, challenge conventional wisdom, and encourage their team to explore new ideas and approaches.

 What to expect with AI leaders in the future ?

 As we look ahead, it's likely that we'll see more collaboration between AI and human leaders. AI in management could handle data-heavy tasks, freeing up human managers to focus on interpersonal and strategic aspects of leadership. To thrive in this new landscape, human leaders might need to develop new skills: Learning agility: The ability to adapt quickly to new technologies and AI tools will be crucial. Leaders will need to stay curious, embrace continuous learning, and quickly apply new knowledge to solve problems.

 Passion: 

Showing enthusiasm for integrating AI in the workplace can help teams navigate the transition. Leaders who are passionate about technological advancements can inspire their teams to embrace change and see the potential in AI collaboration. Resilience: As AI transforms the workplace, leaders will need to bounce back from challenges posed by new technologies. This includes adapting to changing job roles, helping team members who may be displaced by AI, and maintaining a positive outlook in the face of rapid change.  Delegation: Knowing when to assign tasks to AI vs. human team members will be a critical skill. Leaders will need to understand the strengths and limitations of both AI and human workers to optimize team performance.

 Conclusion

 So, would an AI boss be better than a human one? The answer isn't straightforward. AI offers advantages in fairness, efficiency, and data-driven decision-making. However, it lacks crucial human skills like empathy, creativity, and the ability to navigate complex social situations. Let's say that the future of leadership  likely lies in a balance between AI and human managers. By leveraging the strengths of both, organizations can create more effective, efficient, and empathetic leadership structures. Furthermore, I think both leaders and employees need to adapt to this changing landscape. Welcoming AI's potential while valuing uniquely human skills will be key to success in this workplace. The challenge – and the opportunity – are all gathered in finding the right balance between technological advancement and human touch in leadership. Read the full article

[technophili]

Netflix Embraces generative AI for Story Quality, Not Cost-Cutting

During a fairly recent earnings interview, Netflix co-CEO Ted Sarandos gave his optimistic view of what generative AI could become in the entertainment industry. He believes that AI tools will enable creators to improve the way they tell their stories, which could in turn revolutionize the way content is created on the streaming platform. "I think that AI is going to generate a great set of creator tools, a great way for creators to tell better stories, " said Mr. Sarandos during the company's second-quarter earnings conference call. Quote found on variety.He also reiterated that the main purpose of implementing this technology was to improve story quality, not to reduce production costsMr. Sarandos also drew a link between the advent of AI and the shift from hand-drawn animation to computer-generated animation. "Animation didn't get cheaper, it got better in the move from hand-drawn to CG animation," he explained. "And more people work in animation today than ever in history." as cited by variety Ahead, the Netflix executive confessed confidence in the human element of creativity, saying AI would not replace human talent, but rather augment it. "I don't believe that an AI program is going to write a better screenplay than a great writer, or is going to replace a great performance," said Mr. Sarandos. He added: "AI is not going to take your job. The person who uses AI well might take your job" Taking into account AI's experimentation in the film industry at present, Mr. Sarandos cautioned against making hasty predictions about its effect in the long term. "We've got to see how that develops before we can make any meaningful predictions of what it means for anybody," he noted.Netflix hasn't waited to already use AI and machine learning in its content recommendation algorithms. Co-CEO Greg Peters emphasized the potential of generative AI to further improve these algorithms, saying "We think that generative AI has tremendous potential to improve our recommendations and discovery systems even further."While the streaming embarrassment only continues to seek out AI's abilities in content creation, Sarandos emphasized the fundamental elements that make for successful storytelling absolutely do not change. "Shows and movies, they win with the audience when they connect, " he said, noting how important the quality of the writing, the chemistry between the creators and the plot twists are.Although the extent of AI's impact remains to be seen. On how to create Netflix content, Sarandos' comments hint at a future where technology and human creativity work together to create even more compelling and innovative storytelling experiences for viewers around the world. Read the full article

[technophili]

AI Tool Triples Alzheimer's Detection Rate

Memories slip away like sand through fingers. Loved ones become strangers. Simple tasks morph into insurmountable challenges. This is the harsh reality for millions grappling with Alzheimer's disease. Yet, amidst this bleak landscape, a beacon of hope emerges from an unexpected source.

Cambridge’s New AI Tool

What It DoesResearchers at the University of Cambridge have recently unveiled a revolutionary tool for rethinking the way we treat Alzheimer's disease. This innovative technology uses full-scale artificial intelligence to detect Alzheimer's disease before it takes hold, giving us greater accuracy in predicting its progression.Not much longer ago, Yahoo news reported that this new AI tool boasted a rather astonishing 80% accuracy rate when asked to identify potential cases. Compared to current methods, this could triple our ability to spot signals of this disease.

Professor Zoe Kourtzi interviewed by the Alan Turing institution Professor Zoe Kourtzi, the project's mastermind, explains what this means: "This advancement holds the potential to dramatically enhance patient care. We can now pinpoint individuals requiring intensive support while alleviating concerns for those likely to maintain stability."

The Human Side of Alzheimer’s

The Pain of Uncertainty

Dr. Ben underwood|Cambridge Advance online If you want more details, I'll let you listen to Dr. Ben Underwood, veteran consultant psychiatrist "Daily, I witness the toll of uncertainty on patients and their families. The frustration of inconclusive answers weighs heavily on both sufferers and medical professionals alike." What's more, the global impact of Alzheimer's disease is worrying. If we take, for example, statistics from Florida Atlantic University, which reveal that 44 million people suffer from this disease or dementia related to it, it is just as worrying to learn that in the USA alone, 5.8 million Americans are carriers of this disease, making it the sixth leading cause of death nationwide. Not to mention the financial divisions, which are just as important: the Alzheimer's Association predicts that costs per year are likely to rise to 1.1 billion dollars by 2050, which is why innovative solutions are needed to contain this epidemic.However, numbers don't tell the whole human story, because behind every statistic lies a person, or a family, or even a life that will be forever changed. 

Alison Gilderdale’s Journey

Take the story of Alison Gilderdale, reported by sky news. Her story begins ten years ago, when she had memory problems - well, at the time, everyone thought it was just forgetfulness. But it took six years before she was diagnosed once and for all. And you know how precious time is that can never be recovered, and here's an example of time wasted in uncertainty"I convinced myself everything was fine," Alison recalls. "It took others pointing out that something wasn't right. Now, I rely heavily on assistance. Even dressing myself became a struggle."If a diagnosis had been made beforehand, it could have changed the path she was going to take, she would have had good interventions, and even benefited from any support she might not have needed.So, what this new tool promises us is to offer hope where none existed before.And I suppose you're wondering how this technology works?

Inside the AI’s Mind

Analyzing Cognitive Tests and MRI ScansFirstly, the system analyzes congnitive tests and MRI brain scans, then uncovers patterns invisible even to the naked eye, Predicting Disease ProgressionWhat's more, it can predict the rate at which symptoms are progressing very effectively. And I believe that, thanks to this technology, patients who may be weakening may be entitled to what I would call aggressive interventions, while those who are progressing more slowly will be able to benefit from tailored management approaches.However, let's calm our enthusiasm for a second. I wonder about the moral implications of this.Because, as you know, the more we have AI-based solutions like Alzheimer's here, the more questions arise about patient privacy and what this might entail when using predictive diagnostics. How can we protect sensitive medical data in a world where everything is becoming almost digital?What effects would predictions about the cognitive future have on a person?Upstream, I also wonder about accessibility, which is an equally important concern. Will this technology be accessible to everyone?I think we need to ensure that these innovations are shared equally, to avoid a system where only those who can afford it benefit from high-quality diagnostics. But if we set aside these two challenges, which aren't such a big concern at the moment, Advance planning could enable us to expand our treatment options, giving patients and families enough time to plan, cherish moments and seek essential support,because if you haven't already heard, research continues to evolve.  If we take the team at Cambridge University as an example, they are constantly improving their tool, rigorously testing it to ensure that its effectiveness is not limited to demographic boundaries. At the same time, other institutions are exploring other approaches to better understand this rather complex disease.Take, for example, researchers at Florida Atlantic University who are investigating how machine learning can be used to assess congestive health. If we refer to their work published in the Journal of Alzheimer's Disease, we can see that they are highlighting the potential of artificial intelligence to discover new and somewhat complicated patterns in different areas of brain health.I know this blog already seems long, but bear with me and let's get into the details of this AI research tool. Unlike traditional diagnostic methods, which rely heavily on subjective assessments, this system uses complex algorithms to analyze large datasets. It will examine the details of brain scans in great detail, identifying slight or trivial changes in brain form and function that experienced neurologists would be unable to see.What's more, his ability to predict how quickly the brain can weaken is uncanny.

Emily Zhao-NYU Tisch School of the Arts Dr. Emily Zhao, a neurologist who was not involved in the study, explains This predictive ability could revolutionize patient care: "This predictive capability could revolutionize how we approach treatment plans. We could potentially tailor interventions based on each patient's projected disease trajectory, maximizing the effectiveness of our therapies."Thus, its implications will no longer be limited solely to the care of individual patients, as it could make clinical trials to test new treatments for Alzheimer's much simpler. If they can accurately identify people who are declining rapidly, researchers will seem better placed to test more effective therapies, and they could speed up the drug development process.

Ethical and Accessibility Challenges

As with any revolutionary technology, there is no shortage of challenges.

Mark levy Dr Mark levy (Write in the comments if you know another levi, in an anime where there are disgusting titans), joking aside, I was saying that Dr Mark Levy, psychiatrist at Stanford University, raises questions that I find quite pertinent:How can we ensure that this powerful predictive tool does not lead to discrimination in employment or insurance? What safeguards need to be put in place to protect patients' privacy and autonomy?And I particularly agree with Levy, (I still do anyway) because it's crucial to establish sound ethical frameworks for the successful implementation of AI in healthcare. Whether it's policymakers or healthcare providers, they need to work together to put guidelines in place so that there are no problems between innovation and patient protection. I'd also point out that we need to remain aware of the importance of the human element in this technological revolution.

Dr. Sarah chen- therapyDen Dr Sarah Chen, a geriatric psychiatrist, insists on this point: "While AI can provide invaluable insights, it can never replace the empathy and nuanced understanding that comes from a doctor-patient relationship. These tools should augment, not replace, human judgment and compassion."

More Than Just One Tool

Combining AI with Genetic DataAs research progresses, scientists are exploring similarities between AI diagnostics and other cutting-edge technologies.Dr. Alexander Novak, a neurogeneticist at the University of California, San Francisco, is investigating how AI predictions can be combined with other genetic risk factors to produce even more sophisticated predictive models."By integrating multiple data streams, we might be able to paint a more complete picture of an individual's Alzheimer's risk and progression," he explains.Everyday Monitoring Innovations Even researchers at MIT are developing AI systems that could monitor everyday behaviors for even the faintest signs of cognitive decline, and then detect changes long before family members or clinicians notice anything.Blending Empathy and TechnologyAs exciting as these advances are, I can only tell you to calm your expectations.

Dr. Rachel Simmons- Brown University Internal Medicine Residency Program Directors Dr. Rachel Simmons, a geriatrician at Johns Hopkins, cautions:"While these tools offer tremendous promise, they're not a silver bullet. Alzheimer's is a complex disease influenced by myriad factors. We must continue to invest in a multifaceted approach to prevention, treatment, and care."Indeed, some lifestyle factors can play an important role in the fight against Alzheimer's disease.

Dr. Michael chen by Michealchenmd.com Hence the intervention of Dr. Michael Chen, a neurologist specializing in cognitive health, who prefers a different approach: "While we await wider availability of advanced diagnostic tools, we mustn't overlook the power of lifestyle interventions. Regular exercise, a healthy diet, cognitive stimulation, and social engagement all play vital roles in maintaining brain health."

Supporting Caregivers and Patients

Maria Gonzalez’s StoryFor caregivers, who are often important but overlooked Alzheimer's sufferers, this may give them a small glimmer of hope.Maria Gonzalez, who has been caring for her mother with Alzheimer's for five years, says: "Knowing that science is making progress gives me strength. Even if these new tools don't help my mom directly, I'm hopeful they'll make a difference for future generations."

Conclusion 

We've finally finished this exploration of AI's role in revolutionizing Alzheimer's research. Be optimistic and cautious, because it's not just about beating a disease, it's about keeping the stories, the connections, the essence of what makes us human, and in this noble quest, every tool, every idea, every moment of clarity is a victory worth celebrating. Read the full article

[technophili]

The Climate Change Cheat Code: Artificial Intelligence (AI)

Climate change isn't some far-off problem. It's here, it's now, and it's messing things up big time. But here's the kicker: we've got a new ally in this fight, and it's smarter than your average bear. I'm talking about AI and climate solutions.Now, you might be thinking, "AI? Isn't that just fancy tech for beating humans at chess?" Well, hold onto your hats, because AI is doing a lot more than that. It's rolling up its virtual sleeves and getting down to the nitty-gritty of saving our planet. Let's break down how these silicon-brained buddies are helping us tackle the climate crisis.

The Speed Demon of Climate Research

Photo by Asile Clairette, Unsplash Remember when scientists spent years hunched over data, trying to make sense of our changing climate? Well, those days are as outdated as dial-up internet.The World Economic Forum dropped this bombshell: AI can measure changes in icebergs 10,000 times faster than humans. You read that right. Ten thousand times faster and I personally think this speed isn't just about efficiency. It's about giving us more time to act. While AI crunches the numbers, scientists can focus on developing solutions.  And let's not forget the potential for predictive modeling. With all this data and processing power, AI could start forecasting ice melt patterns decades in advance. Imagine knowing exactly how our coastlines will change over the next 50 years. That's not just cool (pun intended) - it's crucial for planning and mitigation.

 The All-Seeing Eye

Photo by Tolga Ahmetler, Unsplash Now, let's talk about AI technology to tackle climate crisis. We are not only talking about number-crunching but also keeping a watchful eye on our planet 24/7. The Bezos Earth Fund (yes, that Bezos) says AI is already helping monitor greenhouse gas emissions, detect forest fires, and advance renewable energy. Kind of having millions of tireless environmental scientists working round the clock.AI could for instance be integrating data from all sorts of sources - satellites, ground sensors, even social media. What if we've a system that can spot a forest fire from space, confirm it with ground sensors, and track its spread through locals' social media posts - all in real-time. That's not just monitoring; that's a game-changer for rapid response. And let's not stop there. This kind of holistic view could reveal connections we've never seen before. Maybe we'll find out that algae blooms in the Pacific are linked to dust storms in the Sahara. Who knows? With AI watching everything, we might uncover climate connections that blow our minds.

Master Juggler of Renewable Energy

Photo by Kervin Edward Lara Greenly reports that AI algorithms are analyzing weather data and electricity demand patterns to optimize wind and solar power operations. Moreover, I deem AI could revolutionize energy storage too. See, the big challenge with renewables is that the sun doesn't always shine and the wind doesn't always blow. But what if AI could predict these fluctuations days or even weeks in advance? We could store energy more efficiently, making renewable power more reliable than ever.And let's think bigger. AI could guide the development of completely new storage technologies. Maybe it'll figure out how to store energy in synthetic fuels, or find a way to make batteries that last for decades. The possibilities are as endless as a politician's promises - but way more likely to come true.

AI: The Low-Calorie Computing Champ

Now, I know what you're thinking. "But doesn't AI use a ton of energy? Aren't we just trading one problem for another?" Well, that might true, but The Guardian dropped this truth bomb: the emissions generated by powering AI are much smaller than those created by sectors like construction or transport. In fact, in some cases, AI uses less power than traditional computing methods, especially for complex climate models.  To my mind, this efficiency could be a game-changer for developing countries. They could leapfrog over energy-hungry traditional computing and go straight to more efficient AI systems. And get this: AI could optimize its own energy use. Think of AI systems that are constantly refining their processes, getting more energy-efficient over time. 

Matchmaking for Climate Innovation

Remember how excited you got when your friend set you up on a blind date? Well, AI is playing matchmaker too, but for climate solutions. And trust me, these matches are a lot more promising than your friend's cousin's roommate's brother. The Bezos Earth Fund is creating new alliances among innovators across climate, nature, and AI to identify and support impactful AI-powered solutions. It's like a dating app, but instead of matching people, it's matching brilliant minds with insane ideas.  AI could, I reckon,  spot connections that humans might miss. Maybe a technique used in deep-sea exploration could be applied to carbon capture. Or a method for predicting stock market trends could help forecast extreme weather events. AI could be the ultimate outside-the-box thinker, connecting dots we didn't even know existed. Furthermore, let's not forget about the potential for AI to democratize climate solutions. By analyzing successful projects and adapting them to different contexts, AI could help spread good ideas faster than gossip in a small town. 

Welcoming Green Architect

Photo by Ricardo Gomez Angel Let's talk about buildings. Turns out, they're not always the most eco-friendly structures. But AI is here to give them a green makeover that would make any HGTV host jealous. Besides, The Guardian reports that in the built environment, AI can help shift away from polluting materials like concrete, reduce the carbon impact of energy and transport systems, and support the reuse of existing buildings.  Therefore, AI could revolutionize the entire lifecycle of a building. Ideate AI systems that can simulate a building's entire life before we even break ground. We're talking about predicting energy use, maintenance needs, and even how the building could be repurposed or recycled at the end of its life.Why not think even bigger. AI could help design entire cities that are not just energy-efficient, but actually help clean the air and water around them. Kind of turning our concrete jungles into actual, air-purifying jungles. Now that's a cityscape I'd like to see.

 Big Tech Goes Green (Finally!)

Inclusion & accessibility labs  You know those tech giants we all love to hate? Well, they're actually doing something pretty cool when it comes to AI and clean energy. I know, I'm as shocked as you are. The Guardian spills the tea: all the big tech firms involved in AI – Meta, Google, Amazon, Microsoft – are seeking renewable energy resources to meet their climate targets. Amazon bought more than half the output of a Scottish offshore wind farm. Microsoft is backing billions in renewable energy projects. Google aims to run its data centers on carbon-free energy by 2030. But here's what's missing from this picture: the potential ripple effect. As these tech giants go green, they're creating demand for renewable energy on a massive scale. This could drive down prices and spur innovation in the renewable sector, making clean energy more accessible for everyone. Better yet, let's not forget the potential for these companies to open-source their AI climate tools. Imagine if they shared their tech with the world, democratizing access to powerful climate solutions. It could accelerate global efforts to combat climate change really faster.

 The Climate Crisis First Responder

 Here's a sobering thought: according to the World Health Organization, almost 4 billion people live in areas highly vulnerable to climate change. That's more than half the world's population! This is where AI-driven environmental solutions come in clutch. By helping us predict and prepare for climate-related disasters, AI could potentially save millions of lives. Picture having a crystal ball, but instead of telling you who you'll marry, it's telling you where the next hurricane might hit.But here's what the report doesn't explore: the potential for AI to not just predict disasters, but to help design more resilient communities. Imagine AI systems that can model thousands of different city layouts to find the ones that best withstand floods, hurricanes, or heat waves, like cities that bend but don't break when disaster strikes.Likewise, AI could provide tailored disaster preparedness plans for individual households. Based on your location, resources, and needs, AI could give you a custom evacuation plan or survival strategy. Let's compare it to having a personal disaster coach. Let's hope we never need it, but it's good to know it's there.

  AI and the Future of Climate Action

 So, what's next in the world of AI climate tools ? The possibilities are as vast as the ocean (which, thanks to climate change, is getting vaster by the day). From developing new green technology to finding innovative ways of reducing carbon emissions, AI is set to play a crucial role in our fight against climate change. But here's the exciting part: we're just scratching the surface. We could think about AI systems that can design super-efficient carbon capture technologies, or that can optimize global supply chains to minimize emissions. Picture AI-powered robots planting billions of trees, or AI algorithms discovering new, more effective ways to store renewable energy.

 Conclusion: AI - The Sidekick We Didn't Know We Needed

 So, there you have it. AI isn't the caped superhero that's going to swoop in and save us from climate change. But it might just be the sidekick we need to turn the tide. With speeding up research to optimizing renewable energy, and predicting disasters to designing greener cities, AI is proving to be a powerful ally in our fight against global warming.  But remember, AI is just a tool. A really smart, really powerful tool, but a tool nonetheless. It's up to us to use it wisely, to act on its insights, and to make the changes we need to save our planet. Because at the end of the day, this is our home we're talking about. Our big, beautiful, blue marble floating in space. And whether you're a human or an AI, there's no place like home. So let's roll up our sleeves, fire up those AI systems, and get to work. Because the clock is ticking, and we've got a world to save. And who knows? With AI by our side, we might just stand a fighting chance. Now, if you'll excuse me, I'm off to have a chat with my smart thermostat about saving the planet. Who knows? It might just have the next big idea for tackling the climate crisis. Stranger things have happened, after all. Like AI helping us save the world. Read the full article

[technophili]

The Climate Change Cheat Code: Artificial Intelligence (AI)

Climate change isn't some far-off problem. It's here, it's now, and it's messing things up big time. But here's the kicker: we've got a new ally in this fight, and it's smarter than your average bear. I'm talking about AI and climate solutions.Now, you might be thinking, "AI? Isn't that just fancy tech for beating humans at chess?" Well, hold onto your hats, because AI is doing a lot more than that. It's rolling up its virtual sleeves and getting down to the nitty-gritty of saving our planet. Let's break down how these silicon-brained buddies are helping us tackle the climate crisis.

The Speed Demon of Climate Research

Photo by Asile Clairette, Unsplash Remember when scientists spent years hunched over data, trying to make sense of our changing climate? Well, those days are as outdated as dial-up internet.The World Economic Forum dropped this bombshell: AI can measure changes in icebergs 10,000 times faster than humans. You read that right. Ten thousand times faster and I personally think this speed isn't just about efficiency. It's about giving us more time to act. While AI crunches the numbers, scientists can focus on developing solutions.  And let's not forget the potential for predictive modeling. With all this data and processing power, AI could start forecasting ice melt patterns decades in advance. Imagine knowing exactly how our coastlines will change over the next 50 years. That's not just cool (pun intended) - it's crucial for planning and mitigation.

 The All-Seeing Eye

Photo by Tolga Ahmetler, Unsplash Now, let's talk about AI technology to tackle climate crisis. We are not only talking about number-crunching but also keeping a watchful eye on our planet 24/7. The Bezos Earth Fund (yes, that Bezos) says AI is already helping monitor greenhouse gas emissions, detect forest fires, and advance renewable energy. Kind of having millions of tireless environmental scientists working round the clock.AI could for instance be integrating data from all sorts of sources - satellites, ground sensors, even social media. What if we've a system that can spot a forest fire from space, confirm it with ground sensors, and track its spread through locals' social media posts - all in real-time. That's not just monitoring; that's a game-changer for rapid response. And let's not stop there. This kind of holistic view could reveal connections we've never seen before. Maybe we'll find out that algae blooms in the Pacific are linked to dust storms in the Sahara. Who knows? With AI watching everything, we might uncover climate connections that blow our minds.

Master Juggler of Renewable Energy

Photo by Kervin Edward Lara Greenly reports that AI algorithms are analyzing weather data and electricity demand patterns to optimize wind and solar power operations. Moreover, I deem AI could revolutionize energy storage too. See, the big challenge with renewables is that the sun doesn't always shine and the wind doesn't always blow. But what if AI could predict these fluctuations days or even weeks in advance? We could store energy more efficiently, making renewable power more reliable than ever.And let's think bigger. AI could guide the development of completely new storage technologies. Maybe it'll figure out how to store energy in synthetic fuels, or find a way to make batteries that last for decades. The possibilities are as endless as a politician's promises - but way more likely to come true.

AI: The Low-Calorie Computing Champ

Now, I know what you're thinking. "But doesn't AI use a ton of energy? Aren't we just trading one problem for another?" Well, that might true, but The Guardian dropped this truth bomb: the emissions generated by powering AI are much smaller than those created by sectors like construction or transport. In fact, in some cases, AI uses less power than traditional computing methods, especially for complex climate models.  To my mind, this efficiency could be a game-changer for developing countries. They could leapfrog over energy-hungry traditional computing and go straight to more efficient AI systems. And get this: AI could optimize its own energy use. Think of AI systems that are constantly refining their processes, getting more energy-efficient over time. 

Matchmaking for Climate Innovation

Remember how excited you got when your friend set you up on a blind date? Well, AI is playing matchmaker too, but for climate solutions. And trust me, these matches are a lot more promising than your friend's cousin's roommate's brother. The Bezos Earth Fund is creating new alliances among innovators across climate, nature, and AI to identify and support impactful AI-powered solutions. It's like a dating app, but instead of matching people, it's matching brilliant minds with insane ideas.  AI could, I reckon,  spot connections that humans might miss. Maybe a technique used in deep-sea exploration could be applied to carbon capture. Or a method for predicting stock market trends could help forecast extreme weather events. AI could be the ultimate outside-the-box thinker, connecting dots we didn't even know existed. Furthermore, let's not forget about the potential for AI to democratize climate solutions. By analyzing successful projects and adapting them to different contexts, AI could help spread good ideas faster than gossip in a small town. 

Welcoming Green Architect

Photo by Ricardo Gomez Angel Let's talk about buildings. Turns out, they're not always the most eco-friendly structures. But AI is here to give them a green makeover that would make any HGTV host jealous. Besides, The Guardian reports that in the built environment, AI can help shift away from polluting materials like concrete, reduce the carbon impact of energy and transport systems, and support the reuse of existing buildings.  Therefore, AI could revolutionize the entire lifecycle of a building. Ideate AI systems that can simulate a building's entire life before we even break ground. We're talking about predicting energy use, maintenance needs, and even how the building could be repurposed or recycled at the end of its life.Why not think even bigger. AI could help design entire cities that are not just energy-efficient, but actually help clean the air and water around them. Kind of turning our concrete jungles into actual, air-purifying jungles. Now that's a cityscape I'd like to see.

 Big Tech Goes Green (Finally!)

Inclusion & accessibility labs  You know those tech giants we all love to hate? Well, they're actually doing something pretty cool when it comes to AI and clean energy. I know, I'm as shocked as you are. The Guardian spills the tea: all the big tech firms involved in AI – Meta, Google, Amazon, Microsoft – are seeking renewable energy resources to meet their climate targets. Amazon bought more than half the output of a Scottish offshore wind farm. Microsoft is backing billions in renewable energy projects. Google aims to run its data centers on carbon-free energy by 2030. But here's what's missing from this picture: the potential ripple effect. As these tech giants go green, they're creating demand for renewable energy on a massive scale. This could drive down prices and spur innovation in the renewable sector, making clean energy more accessible for everyone. Better yet, let's not forget the potential for these companies to open-source their AI climate tools. Imagine if they shared their tech with the world, democratizing access to powerful climate solutions. It could accelerate global efforts to combat climate change really faster.

 The Climate Crisis First Responder

 Here's a sobering thought: according to the World Health Organization, almost 4 billion people live in areas highly vulnerable to climate change. That's more than half the world's population! This is where AI-driven environmental solutions come in clutch. By helping us predict and prepare for climate-related disasters, AI could potentially save millions of lives. Picture having a crystal ball, but instead of telling you who you'll marry, it's telling you where the next hurricane might hit.But here's what the report doesn't explore: the potential for AI to not just predict disasters, but to help design more resilient communities. Imagine AI systems that can model thousands of different city layouts to find the ones that best withstand floods, hurricanes, or heat waves, like cities that bend but don't break when disaster strikes.Likewise, AI could provide tailored disaster preparedness plans for individual households. Based on your location, resources, and needs, AI could give you a custom evacuation plan or survival strategy. Let's compare it to having a personal disaster coach. Let's hope we never need it, but it's good to know it's there.

  AI and the Future of Climate Action

 So, what's next in the world of AI climate tools ? The possibilities are as vast as the ocean (which, thanks to climate change, is getting vaster by the day). From developing new green technology to finding innovative ways of reducing carbon emissions, AI is set to play a crucial role in our fight against climate change. But here's the exciting part: we're just scratching the surface. We could think about AI systems that can design super-efficient carbon capture technologies, or that can optimize global supply chains to minimize emissions. Picture AI-powered robots planting billions of trees, or AI algorithms discovering new, more effective ways to store renewable energy.

 Conclusion: AI - The Sidekick We Didn't Know We Needed

 So, there you have it. AI isn't the caped superhero that's going to swoop in and save us from climate change. But it might just be the sidekick we need to turn the tide. With speeding up research to optimizing renewable energy, and predicting disasters to designing greener cities, AI is proving to be a powerful ally in our fight against global warming.  But remember, AI is just a tool. A really smart, really powerful tool, but a tool nonetheless. It's up to us to use it wisely, to act on its insights, and to make the changes we need to save our planet. Because at the end of the day, this is our home we're talking about. Our big, beautiful, blue marble floating in space. And whether you're a human or an AI, there's no place like home. So let's roll up our sleeves, fire up those AI systems, and get to work. Because the clock is ticking, and we've got a world to save. And who knows? With AI by our side, we might just stand a fighting chance. Now, if you'll excuse me, I'm off to have a chat with my smart thermostat about saving the planet. Who knows? It might just have the next big idea for tackling the climate crisis. Stranger things have happened, after all. Like AI helping us save the world. Read the full article

[technophili]

AI vs. Humans: Who's Got the Upper Hand in Decision-Making?

Hey there, fellow tech enthusiasts! Let's kick things off with a little thought experiment, shall we?Imagine you've got a nasty medical condition that's got you worried. Would you rather have a human doctor who can explain their reasoning but gets it right 70% of the time, or a machine that just spits out a diagnosis without explanation but nails it 95% of the time? Tough call, right?From chatbots to medical diagnoses, these silicon-brained wonders are starting to outperform us mere mortals in many areas.But here's the million-dollar question: If AI is more often right than humans, which should we trust?

The AI Revolution: More Than Just Hype

AI has been making waves in various fields, and it's not just about beating us at chess anymore.The Amazon exampleTake Amazon, for instance. According to Amazon AI, "Amazon uses machine learning in several ways, including the development of chatbots, voice recognition, fraud detection, and product recommendations." They're not stopping there either. "AI and ML are used in Amazon products, such as Alexa's and Amazon's recommendation engine, as well as other business areas, such as in Amazon warehouses." (Amazon AI)But let's get personal for a second. Remember the ChatGPT 3 craze? I was one of the first to take it for a spin, and let me tell you, it was mind-blowing. With just one prompt, it could generate a personalized response that would make your high school English teacher weep with joy. And speaking of high school, I know a few kids who've been using AI for their homework, freeing up time for video games and hanging out. But here's the kicker – they always end up with F-'s in math and physics because their work makes about as much sense as a chocolate teapot to their teachers.When AI Outsmarts Us (And When It Doesn't)So, what makes AI so special in decision-making? Well, for starters:1. Speed: AI can process information faster than ever2. Data analysis: These digital brainiacs can crunch numbers that would give humans a migraine.3. Reduced bias: Unlike us, AI doesn't have a favorite sports team or a crush on the barista down the street.But before we start bowing down to our robot overlords, let's pump the brakes and look at some limitations:1. Limited creativity: AI is great at crunching numbers, but ask it to write a sonnet about your cat, and you might get some weird results.2. Lack of personal touch: AI can't infuse content with your unique personality or experience. It's like asking a ghost to have a personality – not gonna happen.3. Risk of plagiarism: AI analyzes existing content to produce new stuff, some tool like wiston AI can detect any content made with AI.And I don't think Google is a fan of this if you use it on your blog.4. Potential inaccuracies: Sometimes, AI can produce information that's about as reliable as a husband in a couple5. Quality control issues: While AI has some basic checks, it's not as sharp as a human editor. You might end up with content that's as polished as a mud pie.

Human Judgment vs. AI

Now, let's get into the nitty-gritty of human judgment vs. AI. According to a study on PubMed Central, we humans are a bit... noisy.And I don't mean we talk too much (well, some of us do)."Evidence indicates that humans are intrinsically prone to noise, leading to inconsistent and, at times, inaccurate decisions. In contrast, simple rules demonstrate a higher level of accuracy and consistency, while artificial intelligence demonstrates an even higher capability to process vast data and employ logical algorithms." In other words, we humans can be as unpredictable as a cat on catnip when it comes to decision-making.AI's intuition might be outpacing human gut feelings in some areas.I think that AI is developing a sixth sense while we're still trying to figure out our five!Further, The same study suggests that"The potential of AI, particularly its intuitive capabilities, might be surpassing human intuition in specific decision-making scenarios. This raises crucial questions about the future roles of humans and machines in decision-making spheres, especially in domains where precision is paramount."

Putting ChatGPT to the test of intuition or reason 

To put this to the test, we tried out a few prompts on ChatGPT. Remember that classic bat and ball problem?You know, the one where a bat and a ball cost $1.10 in total, and the bat costs $1.00 more than the ball? Most humans instinctively answer 10 cents for the ball, but the correct answer is 5 cents.Prompt 1

A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?ChatGPT, nailed it with a step-by-step explanation that would make your math teacher proud.ChatGPT answerLet's denote the cost of the ball as b dollars. According to the information given:The bat and the ball together cost $1.10.The bat costs $1.00 more than the ball.So, if the ball costs b dollars, then the bat costs 𝑏+1b+1 dollars. Now, we can set up the equation based on the total cost:𝑏+(𝑏+1)=1.10Let's solve for 𝑏b:22b+1=1.10Subtract 1 from both sides:2𝑏=0.10Divide both sides by 2:b=0.05Therefore, the ball costs 0.05 dollars, or 5 centsWe also threw the Linda problem at it 

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice and also participated in anti-nuclear demonstrations.Q: Which option is the most probable?– Option 1: Linda is a bank teller.– Option 2: Linda is a bank teller and is active in the feminist movement.– Option 3: Linda is a member of the NRA."Surprisingly, ChatGPT considered both human intuition and probability base rates in its answer, showing a level of nuanced thinking that's, well, almost human.Chatgpt answer The most probable option among the given choices is Option 1: Linda is a bank teller.This conclusion arises from understanding the conjunction fallacy, which suggests that the probability of two events occurring together (in Option 2) cannot be higher than the probability of either event occurring alone (in Option 1). Since being a bank teller is a broader category than being a bank teller and also being active in the feminist movement (Option 2), Option 1 is logically more probable.Option 3, which states that Linda is a member of the NRA, does not provide any additional information that would make it more probable than Option 1, given the details provided about Linda.When asked to choose the most probable option about Linda's current activities, ChatGPT considered both human intuition and probability base rates. Pretty impressive, huh?Personal insights on trusting AI vs. humansNow, let's get personal again. I've got to admit, I find it hard to trust doctors. And knowing that AIs are better at reading X-rays than humans? That's both comforting and terrifying.On the flip side, if a top-notch doctor can't explain their diagnosis, I'm not likely to believe them anyway.And an AI that can't explain its decisions? That's about as trustworthy as a used car salesman in a cheap suit.When it comes to education, though, I wouldn't trust AI as far as I could throw it (which, being software, isn't very far).Not only will you end up with F-'s like those kids I mentioned earlier, but you'll miss out on the wisdom that comes from actual human interaction.AI can't replace the life lessons you get from your parents, aunts, uncles, and that weird neighbor who always has advice about everything.But here's a thought – if you trust your blender to make juice, your microwave to heat food, and your washing machine for clothes, why not trust an AI?Some things machines just do better than humans because they're designed for repetitive tasks.

The Final Verdict: AI's Got Options, Humans Make Decisions

Here's the deal: AI can crunch data faster than you can say ever and come up with options that might make our jaws drop. But at the end of the day, it's us humans who need to make the callI.t's lightning-fast, incredibly accurate, and doesn't get tired after crunching numbers all day. But when it comes to understanding the nuances of human life? Well, let's just say it's still learning.Think about it. AI can tell you the odds of rain tomorrow, but it can't decide whether you should risk ruining your new suede shoes. It can diagnose a medical condition, but it can't weigh the emotional impact of different treatment options on your life. AI is the GPS giving you route options, but you're still the one behind the wheel. It can tell you the fastest way to get somewhere, but only you can decide if you want to take the scenic route instead.That's where we come in, with all our messy, beautiful humanity.So, here's the bottom line: AI is a tool, not a replacement. It's like having a really smart assistant who can give you a bunch of great ideas, but you're still the boss. You get to call the shots, make the tough choices, and live with the consequences.In this AI-powered world, our job isn't to compete with the machines – it's to use them to enhance our own decision-making. We need to be smarter about how we use AI, not let AI make us dumber.After all, we're not just logic machines – we're complex, emotional, sometimes irrational beings. And that's what makes us uniquely qualified to have the final say.What do you think? Are you ready to team up with AI, or are you still skeptical? Drop your thoughts in the comments – let's keep this conversation going! After all, discussing the future is one thing AI still can't do for us... at least not yet!. And remember, whether you're pro-AI or team human, we're all in this wild, tech-filled future together! Read the full article