#like it's really a process of learning to identify the different parts of the machine

[everythingispirates]

im not v good at media analysis but ive had some thoughts bouncing around in my head that i need to outlet somehow so just, bear with me okay lmfao

anyway i think that at least in the first 3 movies death is meant to be synonymous with love, and it drives me batshit insane (affectionate). like ok first there was that post that went around a few years ago that rightfully pointed out that every man elizabeth kissed has both been in love/lust with her and also has died. (i am also including jack in this, because although i dont think they were ever "in love", they're practically obsessed with each other in a strange toxic sort of way that weaponizes affection so it counts lol. i could go on about this too bc it makes me insane too)

not to mention, like, davy jones' entire story being a tragic love story, the man literally cannot die because of calypso... i mean its not that simple obv but from his perspective at least he believes calypso does not return his affection for her even though she DOES, just not in the way he can really anticipate or comprehend. which to me i think means that while death = unrequited love, undeath or coming back to life (like will does as well) is a sign of love that is actually returned

like i could go on but ANYWAY, this all in mind, the fact that jack and barbossa then are both responsible for not only killing each other but that barbossa led the charge to bring jack back to life is crazy like?????? are we seeing this

I really really like this anon!! I might personally not go so far as to say they're synonymous but I'd absolutely agree that they go hand in hand, like it's sort of hard at least for me to draw direct symbolic parallels but so much of the death we see is definitely tied super closely to love just like you say!! the elizabeth thing is like an observed phenomenon and I esp find it interesting that the captain of the dutchman curse has this strong connection to love where I'd argue love is sirt of what's supposed to keep the captain tied to the mortal world in a sense?? also really neat that barbossa's curse has the "I feel nothing" aspect to it in combination with the unable to die, as well as love and death having a certain connection in media like historically there's really so much to be done with this concept so I'd love to hear more from you regarding it!! I like just woke up so sorry for rambling but anon you say you're no good w media analysis and I just wanna take this moment to say that a) you obviously have a sense for it and b) this is how you get better at it like do exactly this find a reoccurring motif and inspect it, see where it leads you!! I really value media literacy and I'd argue it's definitely a learned skill so I just wanna like encourage you and anyone who reads this who might find analysis a bit daunting to give it a shot. I think potc especially is really good as a starting point if you wanna get into it because it's not The most complex thing in the world like it's still a kids movie but it does have a lot of stuff going on without it being like overwhelming I guess the word is???

[sumikatt]

(Has alt text.)

AI has human error because it is trained on “human error and inspiration”. There are models trained on specifically curated collections with images the trainer thought “looks good”, like Furry or Anime or Concept Art or Photorealistic style models. There’s that “human touch”, I suppose. These models do not make themselves, they are made by human programmers and hobbyists.

The issue is the consent of the human artists that programmers make models of. The issue—as this person did correctly identify—is capitalism, and companies profiting off of other people’s work. Not the technology itself.

I said in an earlier post that it’s like Adobe and Photoshop. I hate Adobe’s greedy practices and I think they’re evil scumbags, but there’s nothing inherently wrong or immoral with using Photoshop as a tool.

There are AI models trained solely off of Creative Commons and public domain images. There are AI models artists train themselves, of their own work (I'm currently trying to do this myself). Are those models more “pure” than general AI models that used internet scrapers and the Internet Archive to copy copyrighted works?

I showed the process of Stable Diffusion de-noising in my comic but I didn’t make it totally clear, because I covered most of it with text lol. Here’s what that looks like: the follow image is generated in 30 steps, with the progress being shown every 5 steps. Model used is Counterfeit V3.0.

Parts aren’t copy pasted wholesale like photobashing or kitbashing (which is how most people probably think is how generative AI works), they are predicted. Yes, a general model can copy a particular artist’s style. It can make errors in copying, though, and you end up with crossed eyes and strange proportions. Sometimes you can barely tell it was made by a machine, if the prompter is diligent enough and bothers to overpaint or redo the weird areas.

I was terrified and conflicted when I had first used Stable Diffusion "seriously" on my own laptop, and I spent hours prompting, generating, and studying its outputs. I went to school for art and have a degree, and I felt threatened.

I was also mentored by a concept artist, who has been in the entertainment/games industry for years, who seemed relatively unbothered by AI, compared to very vocal artists on Twitter and Tumblr. It's just another tool: he said it's "just like Pinterest". He seemed confident that he wouldn't be replaced by AI image generation at all.

His words, plus actually learning about how image generation works, plus the attacks and lawsuits against the Internet Archive, made me think of "AI art" differently: that it isn't the end of the world at all, and that lobbying for stricter copyright laws because of how people think AI image gen works would just hurt smaller artists and fanartists.

My art has probably already been used for training some model, somewhere--especially since I used to post on DeviantArt and ArtStation. Or maybe some kid out there has traced my work, or copied my fursona or whatever. Both of those scenarios don't really affect me in any direct way. I suppose I can say I'm "losing profits", like a corporation, but I don't... really care about that part. But I definitely care about art and allowing people the ability to express themselves, even if it isn't "original".

[theminecraftbee]

Hi, here with a vault hunters question — what's the best way to start with the big tech mods if i never played with any of them before (or, haven't played with three of them and lowkey hated Botania)? I'm not *quite* at that point yet, but i want to try them and don't want to get too overwhelmed to play.

it depends on what you’re looking for! first, let me say that none of the big tech mods will become required at any point; production mods probably define your farm-building capabilities way more than your big mods in most cases, and unless you’re playing in skyblock or with grindy crystals you can normally last well into the late levels without needing absurd farms for much (although most people identify a specific pain point they want modded farm for eventually, and this can be a good reason to go into a specific mod). choose a specific thing you want to be the First Task you do with a big tech mod, and focus on getting the steps done to do That Specific Task; that often gives you the path to learn everything you need!

from there, if you’re a beginner with no preferences, my advice is that you will probably want to watch or read tutorials! all four big mods have wikis/guides of varying qualities; botania also has the botania lexicon in-game, and create has the “ponder” feature. these will help you figure out how to play one of the big tech mods, once you’ve decided which interest you!

as for how each mod plays: thermal expansion is less powerful than its cousins, but is far, far simpler. almost everything thermal expansion does is a one-block solution, and I’ve never had to chain more than maybe two machines together to do any given task. you will craft a machine using all components you can craft at a crafting table, provide that machine power, and you’re off to the races! so if you like simple solutions, this is probably the mod, especially since it doesn’t require any mod-specific crafting.

mekanism is all about chaining machines together. it can do a lot of things thermal expansion can do much more powerfully thanks to the ability to upgrade many of those basic blocks into factories, but boy do some processes in mekanism require a LOT of pipe spaghetti, machinery, and steps to get things done. luckily, determining what these steps ARE tends to be fairly simple, as you can just follow backwards in JEI from your intended endpoint to the eventual one or many starting points you need, and there are a lot of video guides, plus a wiki. if you LIKE some of the most powerful ore processing and tech and eventually power production in the game this is for you, but it can be complicated or intimidating at first glance.

botania also, from what I’ve seen, requires chaining multiple machines together, this time in order to send mana from place to place. the hard part of botania, at least for me, is wrapping my head around how to move mana around; once you understand that, I suspect it’s a bit simpler than late-game mekanism, but its logic can feel VERY different from the other three mods due to that mana abstraction. it and create also make the coolest decorative blocks in my opinion, with botania having a lot of cool “natural” blocks.

create is the mod that requires the most “thinking” in my experience to get things to work, but is often the most capable of being cheaply powerful. you will need to use spatial reasoning to figure out how to connect gears together in order to make anything work, and you’ll likely need to use create’s conveyor belts to move items around. this can make doing anything in create feel very complicated if you don’t like it, but extremely satisfying if you do like it, since in create you will often feel like you can use your own, special solution for things, instead of a set chain of machines set up the way anyone else would. a lot of players who otherwise dislike tech mods also tend to really like create, despite it being that deep down! it also has by far the best in-game documentation, with the ponder system on every single mechanical part letting you see what they do in-game, with animations to demonstrate, and it’s probably the most useful all-in-one package of the big mods for the most things, even if not necessarily the best at each of those jack of all trades things it does.

so yes! hopefully this helps you out! all of the big mods have sort of their own playstyle, and maybe this helps you figure out where to start!

[paranormeow7]

autism machine brain

some random thoughts. disclaimer I am between levels 1-2 and have generally low support needs. please do not take my personal experiences as written to describe the whole community!! if others have similar experiences to me, maybe with different words, feel free to share them. it’d be interesting to hear from people all around the spectrum. but do not take my words and use them to talk over others who are not me.

this is mostly about ideas of what is seen as ableist in the community and how it pertains to how I like to identify and describe myself. there’s a stereotype that is seen as ableist, that (usually low support needs) autistic people are like robots. honestly, I feel like one, and it comforts me to identify with them, as I feel like my brain operates and processes language/actions etc like one. specifically, a slow, old family computer.

I call myself slow, which may be seen as ableist language, because I am slow. Maybe due to catatonia (I think that’s the right word?) and like. cognitive stuff? like how it’s kind of hard to like. comprehend and process things unless they are perfectly laid out for me. it is not unlike writing lines of code. if the line of code is not written perfectly into my brain engine, I will freeze up and be unable to complete the action properly. Ive gotten better about this as I’ve gotten older, but I still usually need to be told the exact details of how to do a lot of complicated things, like schoolwork, especially math.

there are just simply too many steps and possibilities. I get overwhelmed and don’t know where to start, as there is too much room for error. even as I try to fill in the blanks and infer what I am meant to do based on what I know, it is simply too much of a risk to attempt something I understand so little. my brain short circuits and blue screens, and I end up sitting, staring at my task and thinking of nothing. this is not ideal for school!! but it is so hard to ask for help, because I feel stupid and disruptive. other kids just run on a newer and faster operating system than me. i am simply behind on software updates.

a big part of my experience as autistic is having an incredibly hard time figuring out how to do or even comprehend things that are new to me, foreign to me, too complicated and large for my mind to run efficiently. I don’t even really know if I’m explaining this properly. At this very moment I am scraping through those lines of code, looking for errors. I very much have a hard time deviating from my “comfort zone”, things that I have already been doing and repeating. repetition is comforting to me. I have already run these programs countless times, and they are proven to work.

My robot brain is my explanation as to why I have trouble improving my art, why I have struggle with disordered eating, why I sound so dry while texting and so awkward while talking. i need the steps broken down for me in such a specific way that is simply not possible most of the time if I want to understand how to do something new or in a new way. for example, I draw the same things over and over, and as such, I do not improve. need to learn fundamentals like lighting, space, form color etc. but attempting such a task is so very daunting. what if I do it wrong? what if I crash? where do I start? Or I try to make something for myself to eat. What if I ruin the dish? There are ingredients in this dish that are not proven to be edible by me. This is cooked in a way that may not be able to run on my operating system. Corrupted code, threatening to break the program. Instead of eating something otherwise healthy and nutritious, I may choose the same, simple food, or not eat altogether.

I am rather verbose, having collected many evocative words over the years, but when there is a concept that I have not attempted to explain before or must explain in a new way, my brain struggles to put it together. a jigsaw puzzle can only be put together successfully in one way. I am not a creative person. I cannot find new and creative ways to complete the puzzle. all I can do is put it together in the same way each time. I often upset people when texting with them, as I use the same responses, same wording, same punctuation etc over and over. To them, they may feel like I am simply uninterested or bored with the conversation. Texting can be stressful because I must rearrange the puzzle in a different way over and over as to not make the person feel ignored. It must hurt to see someone reply with the same mannerisms and phrases each time you speak to them.

I have compared myself to a generative ai before. That may be what I am, but I don’t think I am a very good generative ai. I am more like a factory machine being made to run the software of a generative ai. A machine that has been putting cars together over and over is suddenly asked to create a picture. it is so very strange to be an artist in this state!! again, I do not consider myself a creative person!! it is a lot of the reason I see my work as lacking the same spark and life to it as others work does. they can imagine all sorts of ways to create, all I can do is haphazardly rip apart what I already know, put it back together, run the program and hope it works.

I do love to learn. I do love to scrape and compile new words, new techniques, new food, new tasks to update my software. this is why I have low support needs, as over time I have been slowly integrating more and more features into my program. but it is still overwhelming and disheartening to see my classmates diligently working on an assignment that I rainbow wheeled through the too fast, too complicated explanation of, or see another artist younger than me create beautiful work using techniques that threaten to crash my brain trying to deconstruct, or eating something that I wish to try, but may threaten to poison my code.

I don’t want to be a factory machine, assembling the same parts over and over. I want to be a person, capable of creativity and confidence and working around error and operating smoothly without freezing or shutting down or overheating all the time, needing long cooldown periods, time spent laying in bed doing nothing when I could be lending my time to be productive and do things I want to do. but since I don’t have any other words to describe my experience, it is a comfort to at least be able to name the feeling in a way that others may be able to understand. saying you function like a vintage IBM on dial up also sounds better than saying you’re developmentally retarded.

or maybe my attention span will get better if I get off that damn phone amirite LOLOLOL

but sorry if this was incomprehensible. I feel like it was.

[jzontheazarian]

Akini Jing Pumps Up Creativity with Latest Career Shift

Known for her psychedelic voice, unique music production approach that some have compared to Grimes and Bjork, and work as a DJ, Akini Jing hails from Yunnan, China. Born Jingxi Zhu, her notoriety as a solo artist came in 2019, when she adopted a new cyborg identity that she named "Akini Jing." Through electronic music, cyberpunk aesthetics, interactive installations, wearable art, video, and artificial intelligence trans-semantic creation, etc. Akini has set out to create an art form that attempts to reflect on human beings from a cyborg perspective, to be aware of oneself, and to build a unique spiritual universe through introverted exploration. A few months ago, we had the pleasure of talking with her after a GRAMMY week writing session to discuss the new age of music she’s a part of, her latest single ‘Pump Up,’ and how she’s bringing a different wave of creativity to the forefront with her own unique artistic style.

Jzon Azari: GQ named you one of the “Most Exciting Musicians on the Planet.” Why do you think that is so? What makes Akini Jing stand out amongst artists that may identify in similar ways whether it be musically or aesthetically in style?

Akini Jing: I’m happy that they gave me the honor, but to be honest, as an artist myself, I don’t analyze how others perceive me. If I really think about this question, maybe it’s a matter of good timing for this kind of theme and aesthetic - the merging and conflicts of humanity and technology. So I created my alternate persona “cyborg Akini”, to observe myself reflexively, through the lens of a non-human entity.

And I also believe that anyone who embraces their own root sincerely will make them unique. This is how I created my genre “Oriental Cyberpunk”.

At first glance you are a unique individual when it comes to your musical sound and your fashion taste. How would you describe both to someone who isn’t aware of the Akini Jing brand as of current?

My genre is called Oriental Cyberpunk, which includes electronic music, avant-garde dance, interactive installations, Chinese poetry, machine learning, martial arts, video art, performance art and more.

With previous work as a DJ, how would you say it has aided in your skill set to be a better musician overall?

Being a DJ is a side gig, I have a lot of fun with DJing, and it helps me connect the underground scene more, which is one of my biggest inspirations from.

The American market is the top tier breakthrough music market. As a musician hailing from China, what advantages have you had thus far and what are unique pros you’ve had that other music markets may have prepared you for that have made the transition much smoother for your career?

Haha this question looks like a job interview. I appreciate that the American market is diverse, so I believe that they will likewise appreciate the place that I’m coming from culturally and artistically.

At the same time, I think as an artist, to be devoted to the things that you believe in is enough. There’s nothing you need to worry about.

With many companies embracing “the future” your choice of embodying a cyborg identity that is referred to as oriental cyber is quite frankly, genius. What helped create that concept and what ways are you making it your own to propel your music and image?

“Oriental” is the place and culture that I came from, “Cyber” is the time and the world that I’m living in. I merge myths, archetypes, thoughts and things I learn from nature into our high tech, confusing, but also beautiful, everyday mundane life.

The persona Akini Jing is created in 2019. At that time, I was kinda getting lost in artist creation. I didn’t want to repeat life and my creation process anymore. I decided to take a pause and tried to look inside deeply. So I created Akini Jing to observe myself from a non-human perspective. At the same time, I got an offer to work with Microsoft on developing AI art creation as an expert consultant. That experience inspired me a lot to bring my aesthetic together.

You are creating your own imprints in history in your own right. Firstly, you are the first Chinese artist to enter the US Billboard Indicator Chart with your single ‘Shadow’ that rose to #30. How does it feel to achieve those accomplishments? What can be done so more first opportunities can happen and doors are opened for other artists to achieve similar feats?

It’s not the first time. I've tried many things, but the direction I’m going right now makes me feel most comfortable.  

I’m very grateful for the people who have helped me and the opportunities that I have had. 

Sometimes I’m just lucky. Also, I see every new song as a new beginning, like how artificial intelligence has been updated.

Finally the reason why we are here today. You have a new single ‘Pump Up’ that just came out. Tell us about it. What can listeners expect? Also what inspired it and who helped bring it to life?

The inspiration for this song came from a midnight workout; I usually don’t go that late. I noticed that there were still men lifting weights like crazy and women practicing yoga in the gym at 1AM and wondered why people were working out in the middle of the night.

Meanwhile in China, there are always many middle-aged and elderly people working out in outdoor parks. They may look ordinary, but when they start working out, it can be breathtakingly beautiful. I like to call them the Eastern mystical forces.

So the interesting timing of these exercise scenarios was the inspiration behind the unusual fitness aesthetics we explored for Pump Up.

I hope people feel energized throughout the song, and that their preconceived notions of what “working out” can be broken. Walking dogs, breathing exercise, brainstorming, etc. can all be a form of working out.

Watch ‘Pump Up’ streaming now.

[christianjaym]

Pitch Experience: Part 1 Not Bad Huh?

It’s pitching time! Isn’t it exciting? No, it’s not. To be honest, the word “pitch” that I am familiar with is the one from baseball, not the one I’ll be sharing. So, after learning the other meaning of this term, I suddenly felt nervous because pitching is the same as talking in front of a crowd, which is a weakness of mine. Pitching is the process of presenting the innovative idea you have thought of in a professional way.

We have been instructed to look for facilities and identify problems to construct an innovative solution. I have several ideas in mind, but only a few were presented since most are impractical, like the first one I thought of: self-arranging chairs powered by Bluetooth. Really? Is that even possible? I thought of that because I felt sorry for those people who are exhausted and weak while they are forced to carry and arrange the chairs to our teacher’s preference. I wanted to create technology to make our lives easier, but I realized that the more I thought about it, the more complicated it became. I can’t make it possible because I envisioned wheels on every foot of the chair, which made me realize that carrying the chairs is easier than implementing that technology.

Next, I thought of the idea of a vending machine with different types of bread, but it already exists in some countries. I also thought of gym filters to help you visualize your fitness goals, whether to be muscular or just slim. There would be a mirror with filters, but I couldn’t pursue it due to a lack of resources. I considered applying the filters to a barber shop so you could see how different haircuts would look based on your head size and select your desired style. Unfortunately, that idea was already taken, and I ran out of ideas.

So it’s pitching time! The venue was the AVR room in CIC. It was very comfortable there, so I didn’t waste time pitching. I struggled to find ideas, but luckily, just before our pitching session, I came up with an idea! It was about agriculture, specifically ducks. The problem is that it is hard to determine the gender of a duckling, so I proposed a solution that involved analyzing their blood to identify gender differences. As I went in front of the crowd, I presented my idea thoroughly, but sadly, it was not accepted. Later on, I reflected on why it was rejected and realized that there were some aspects of the problem that my technology couldn’t solve.

[sergeykir]

Sergey Kir - Conceptivism and The Art of Technology

Conceptivism is a new art style invented by artist Sergey Kir. The model uses many different Conceptivisms and techniques derived from art history and recent technological advances. This creates a bridge between the old and the new. Combining the digital processing of computers, the strategy of money laundering, and the love of bright colors and history, the concept is an insight into the art of this changing age. I have always been interested in art. At one point, I changed jobs in the office of my new investment bank next to MoMA. It was a big step in my Fashion and Art development. I spent my lunch break and after work at MoMA for months wandering the floor. One day in 2014, I learned that a Jeff Koons tour was to be held at another local museum, the Whitney Museum. I went to the exhibition and became aware of his talent and desire to change. It really got me interested!

I learned that Jeff, like me, had worked for many years in the financial industry and, before that, had worked for many years at MoMA in New York, spending time in the same building where I became a guest too week. I was surprised to find myself in a position similar to that of a famous artist. At that time, I was struggling in the middle of life, looking for a way to express myself and find purpose - this show and Jeff's story really inspired me and showed me what I can do with my life do.

That's when the thought came to me: I too should try to create art and share the thoughts I want to express. I finally met Jeff and talked to him about the inspiration he brought. I think my story is similar to the one Jeff Koons met many years ago with Salvador Dali, which inspired him to become the most successful artist of our generation.

How did financial modeling and analysis lend a hand in your creative development?

I spent a lot of time thinking about how aspects of financial modeling and portfolio optimization techniques, knowledge which I acquired in my professional career in financial and quantitative risk management, could be integrated into the artistic process. I want to combine the world of art and research, creating the power of manual drawing, digital photography and scientific methods.

Many of my artworks combine the common structure of the financial quantitative model, its limits and its goals, methods and parameters, as well as identifying the representative subject, and the model is organized and works in a certain degree of freedom to create a visual representation. This model produces an image or part of an image by "magic" from the computer, the artist creates the ultimate model, which can be used as a final product or as an intermediate step in 'to create the latter. An artist can repeat any number of designs until the work is done. The artist's artwork aims to create the look and feel of a handmade work, but is designed digitally.

What is the connection between your work and that of Roy Lichtenstein?

The goal of conceptualism is the opposite of the goal of artists such as Roy Lichtenstein, who created art that looked industrial but was made by hand. In my work, I come around the world and try to create a look that is similar to the human hand but using a production process like grass. Therefore, the final work must be done by hand in order to be able to clean imperfections and shine.

Sometimes it's a computer system that optimizes the color combinations and textures of a work of art. Other times, the work begins as a hand drawing.

What led to Conceptivism?

The Conceptivism was born from my passion for bright colors, money and technology. Much of the inspiration for creating this style comes from the work of the Fauve artists who tried to capture the changes of light in their paintings.

Other influences include the work of artists from the Russian Futurist movement in Italy. These people celebrate the creative process, the love of speed, the interest in machines and the spirit of the youth without the definition of ancient Conceptivisms and artistic values. So, among other things, the Conceptivism of ​​Zaum. Zaum preached that art must overcome the barriers of language to affect the viewer.

What is the relationship between theory and architecture? We can reveal the concept better by comparing the architectural differences between the Romanesque and Gothic building styles. Romanesque architecture defines a large, free-standing style that is often built on a strong concrete foundation that can support large stones and walls. If a block of stone, or even a whole part, is removed from a Romanesque building, nothing will happen to the whole structure. He will continue to stand, leaning on his great wall to hold himself together. Gothic architecture is very different. Each part of Gothic architecture is a reservoir of energy embedded in a network of interconnected elements. These components, along with the forces passing through them, hold the structure together. Excluding any individual component from a Gothic structure will most likely result in the collapse of the entire structure. This is because every stone has a special place of purposes and purposes. To learn more about Sergey Kir and his work, visit the official site: www.sergeykir.com

[360digitmgmalaysia]

What are some of the best data analysis tools

The job market for data analysts is fast expanding in Malaysia. As such, data analysis is a promising career path for a lot of people. In addition to that, organizations of here and now seek workers who can learn with neat lessons from the big data that is produced across a wide range of areas of application and translate such into practical and game-changing solutions. There have been lots of the data analytical centers cropping up in many parts of the country because of the high demand and they provide different courses and programs to cater for all the needs.

What is data analytics :

Data analytics is the act of accumulating, organizing, and analyzing data to obtain new insights, draw conclusions, and make decisions determining the outcomes. While it consists of different approaches, methods, and tools, which by itself enables comprehension of the dramatically growing size and complexity of data.

What does data analytics do :

Data analysts take a pivotal position in companies by utilizing data collection, cleaning, and analysis skills to make sense of large datasets and draw valuable insights that can be translated into actions. In this way, they apply statistical methods and data analytic tools to discover patterns, trends, and associations and later on they visualize all these findings in a clear and eye-catching way. In collaboration with stakeholders, these professionals analyze results and suggest solutions to support the decision-making process with correct information. The importance of continuous improvement of the analyzing processes to maintain their timelessness and precision in providing the necessary solutions to business problems is undeniable.

What are some of the best data analysis tools?

In the dynamic democracies of data-driven decisions, in Malaysia the professionals are applying a lot of tools for the extraction of insights from the huge volumes of data. Regardless of whether is it about identifying market patterns or expanding business operations or improving customer experience, analytical tools used in the right way, is very important. Firstly, let us look at the some of the most popular data analysis tools used by Malaysian professionals and take into account some of the insights provided by the leading training provider 360DigiTMG.

Python:

Python is the one language to rule them all, especially because the incredible Pandas, NumPy, and SciPy libraries have it covered. Python with its pythonic syntax and well-sewn community is a perfect tool for data analysts in Malaysia to handle complex data manipulation, statistics and machine learning tasks.

Click here to learn more about the: Data Analyst Course.

R, which is a programming language designed for statistical computing and graphics, still is the principal tool employed by data scientists. Python's collection of packages and built-in functions as compared to others make this program the first choice for the conduct of complicated statistical analyses, visualization, and construction of predictive models that are industry specific for Malaysia.

Microsoft Excel:

Though newer developed tools have been invented, the Microsoft Excel is still considered to be universal application for data analysis purposes in Malaysia. Data analysis in Excel is easy due to its intuitive user interface and powerful tools like pivot tables, formulas and charts. They enable users to have quick exploratory data analysis and easy to understand reports in the various sectors.

Tableau:

The fact is that Tableau is the brightest star in the sky of data visualization which really helps Malaysian professionals in creation of interactive dashboards and reports leading to actions which are based on these insights. Its simple drag-and-drop functionality, integrated data connectivity and graphic rich visualization features make it the largely preferred medium for varied data illustration and decision support.

Data Analytics Demo:

SQL:

The well-known Structured Query Language (SQL) is often applied by data analysts that work with relational databases in Malaysia. Having a sound knowledge of SQL allows analysts to retrieve data promptly, manipulate them, and analyze them efficiently resulting in good decisions, and effective strategic plans across all sectors ranging from finance to e-commerce.

MS Power BI has arisen as the ultimate business analytics tool that attracts business executives who want to make informed decisions based on their data. Its integration with multiple data sources, powerful analytical tools, and easy-to-use visualization feature, are the main reasons as to why most of the companies in Malaysia prefer to use it especially when the main goal is to enhance data-driven decision making for their business.

SAS that is a data analytic platform is adopted by industries that include banking, healthcare and government in Malaysia, among others. By providing complete range of services for data management, superior analytics, and business intelligence, SAS enables companies to discover hidden value from the data resources and ride on it to the sky. 

IBM SPSS:

IBM SPSS still retains its esteemed position of being the companion of Malaysia's data analysts who do statistical analysis, data mining and predictive analytics. This program’s high user interface, complex statistics, and integration features makes it invaluable to the researchers, analysts and policy makers from different areas of expertise.

Kickstart your career by enrolling in this: Data Analyst Course Malaysia.

Conclusion:

The nature of data analysis tools in Malaysia illustrates a story of diversity and innovation supporting the emerging requirements of a broad range of sectors. Along with its versatility Python, to Tableau's visual ability, and each tool enables analysis to turn data into value. With the data-driven environment, people in industry are urged to stay relevant. Therefore, the role of training providers like 360DigiTMG becomes imperative in giving the professionals the necessary skills and knowledge to harness these tools well. Through the use of up-to-date tools together with staying updated about the technological developments, Malaysian workers have the potential to excel at the cutting edge of the rapidly growing data analytics field.

[jcmarchi]

Putting AI into the Hands of People with Problems to Solve - Technology Org

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Putting AI into the Hands of People with Problems to Solve - Technology Org

As Media Lab students in 2010, Karthik Dinakar SM ’12, PhD ’17 and Birago Jones SM ’12 teamed up for a class project to build a tool that would help content moderation teams at companies like Twitter (now X) and YouTube. The project generated a lot of excitement, and the researchers were invited to demonstrate at a cyberbullying summit at the White House — they just had to get the thing working.

Pienso has developed a no-code AI builder so the people closest to problems can use the technology rather than relying on machine learning engineers. Image credit: Courtesy of Pienso

The day before the White House event, Dinakar spent hours trying to put together a working demo that could identify concerning posts on Twitter. Around 11 p.m., he called Jones to say he was giving up.

Then Jones decided to look at the data. It turned out Dinakar’s model was flagging the right types of posts, but the posters were using teenage slang terms and other indirect language that Dinakar didn’t pick up on. The problem wasn’t the model but the disconnect between Dinakar and the teens he was trying to help.

“We realized then, right before we got to the White House, that the people building these models should not be folks who are just machine-learning engineers,” Dinakar says. “They should be people who best understand their data.”

The insight led the researchers to develop point-and-click tools that allow nonexperts to build machine-learning models. Those tools became the basis for Pienso, which today is helping people build large language models for detecting misinformation, human trafficking, weapons sales, and more, without writing any code.

“These kinds of applications are important to us because our roots are in cyberbullying and understanding how to use AI for things that really help humanity,” says Jones.

As for the early version of the system shown at the White House, the founders ended up collaborating with students at nearby schools in Cambridge, Massachusetts, to let them train the models.

“The models those kids trained were so much better and nuanced than anything I could’ve ever come up with,” Dinakar says. “Birago and I had this big ‘Aha!’ moment where we realized empowering domain experts — which is different from democratizing AI — was the best path forward.”

A project with purpose

Jones and Dinakar met as graduate students in the Software Agents research group of the MIT Media Lab. Their work on what became Pienso started in Course 6.864 (Natural Language Processing) and continued until they earned their master’s degrees in 2012.

It turned out 2010 wasn’t the last time the founders were invited to the White House to demo their project. The work generated a lot of enthusiasm, but the founders worked on Pienso part time until 2016, when Dinakar finished his PhD at MIT and deep learning began to explode in popularity.

“We’re still connected to many people around campus,” Dinakar says. “The exposure we had at MIT, the melding of human and computer interfaces, widened our understanding. Our philosophy at Pienso couldn’t be possible without the vibrancy of MIT’s campus.”

The founders also credit MIT’s Industrial Liaison Program (ILP) and Startup Accelerator (STEX) for connecting them to early partners.

One early partner was SkyUK. The company’s customer success team used Pienso to build models to understand their customer’s most common problems. Today those models are helping to process half a million customer calls a day, and the founders say they have saved the company over £7 million pounds to date by shortening the length of calls into the company’s call center.

“The difference between democratizing AI and empowering people with AI comes down to who understands the data best — you or a doctor or a journalist or someone who works with customers every day?” Jones says. “Those are the people who should be creating the models. That’s how you get insights out of your data.”

In 2020, just as Covid-19 outbreaks began in the U.S., government officials contacted the founders to use their tool to better understand the emerging disease. Pienso helped experts in virology and infectious disease set up machine-learning models to mine thousands of research articles about coronaviruses. Dinakar says they later learned the work helped the government identify and strengthen critical supply chains for drugs, including the popular antiviral remdesivir.

“Those compounds were surfaced by a team that did not know deep learning but was able to use our platform,” Dinakar says.

Building a better AI future

Because Pienso can run on internal servers and cloud infrastructure, the founders say it offers an alternative for businesses being forced to donate their data by using services offered by other AI companies.

“The Pienso interface is a series of web apps stitched together,” Dinakar explains. “You can think of it like an Adobe Photoshop for large language models, but in the web. You can point and import data without writing a line of code. You can refine the data, prepare it for deep learning, analyze it, give it structure if it’s not labeled or annotated, and you can walk away with fine-tuned, large language model in a matter of 25 minutes.”

Earlier this year, Pienso announced a partnership with GraphCore, which provides a faster, more efficient computing platform for machine learning. The founders say the partnership will further lower barriers to leveraging AI by dramatically reducing latency.

“If you’re building an interactive AI platform, users aren’t going to have a cup of coffee every time they click a button,” Dinakar says. “It needs to be fast and responsive.”

The founders believe their solution is enabling a future where more effective AI models are developed for specific use cases by the people who are most familiar with the problems they are trying to solve.

“No one model can do everything,” Dinakar says. “Everyone’s application is different, their needs are different, their data is different. It’s highly unlikely that one model will do everything for you. It’s about bringing a garden of models together and allowing them to collaborate with each other and orchestrating them in a way that makes sense — and the people doing that orchestration should be the people who understand the data best.”

Written by Zach Winn

Source: Massachusetts Institute of Technology

You can offer your link to a page which is relevant to the topic of this post.

[kabirshahni]

Amperity reveals marketing data platform and Microsoft partnership as it aims to become Seattle’s next great startup

Amperity reveals marketing data platform and Microsoft partnership as it aims to become Seattle’s next great startup

GeekWire’s in-depth startup coverage tells the stories of the Pacific Northwest entrepreneurial scene.

Amperity co-founders Kabir Shahani and Derek Slager. Photo via Amperity.

Amperity has top talent, solid venture investment, and a tested solution to a pressing problem. Now the company is coming out of stealth mode to share its vision with the world as it aims to become one of Seattle’s top new startups.

Kabir Shahani and Derek Slager are back at it again, four years after selling Seattle-based healthcare marketing startup Appature to IMS Health in 2013.

A year-and-a-half ago, the entrepreneurs teamed up for another go in the marketing automation space, only this time with a much bigger vision for a product tackling a much more difficult problem.

The result is Amperity, which today lifted the hood on its technology that helps some of the world’s largest companies better understand their individual customers by connecting disparate data sources from across the internet.

The 40-person startup raised $9 million in February 2016 from Madrona Venture Group — which participated in the initial venture round for Appature in 2009 — along with a who’s-who list of angel and venture investors like Liquid 2 Ventures founder Joe Montana; Founders’ Co-op partner Chris DeVore; former Microsoft corporate vice president S. Somasegar; Concur co-founder Rajeev Singh; former drugstore.com CEO Dawn LePore; Isilon Systems founder Sujal Patel; and former ExactTarget chief marketing officer Tim Kopp, who now is a partner at Hyde Park Ventures.

Amperity used that cash to build out its team and create a marketing technology platform which ties together unorganized data about individual customer habits and helps clients fine tune their targeted marketing campaigns.

Based on their experience at Appature, which helped healthcare companies track and enhance marketing campaigns, Shahani and Slager knew that there was an opportunity across various industries to better leverage data to create a more complete view of a customer. They worked with Dan Suciu, a computer science professor at the University of Washington and a data management expert, to help figure out how to connect customer data across different sources without a unique identifier.

“We spent a bunch of time with Dan last year to really understand if this was technically feasible — how to handle this scale of data and how to apply machine learning to this problem,” Shahani told GeekWire this week.

Amperity had early pilot customers test its technology earlier this year and saw “extraordinary results,” Shahani said, with revenue increasing and customer acquisition costs dropping. The startup has continued to build out its technology that ingests trillions of data points from a single customer and crunches that information with machine learning to give marketers a holistic understanding of a given user.

Amperity links together several discrete data sources related to one customer — everything from an in-store transaction, online purchasing tendencies, browsing behavior, mobile app activity, email campaign responses, CRM information, etc.

Shahani noted that part of Amperity’s secret sauce is making it easy for companies to plug that data into its system. The CEO said this process has historically been human-driven and “incredibly error prone.”

“We have the scale to not only ingest that data very quickly, but actually do something really meaningful and useful with it so you can action on it to drive the kind of results we’re seeing with our customers,” he added.

The platform can help a retailer figure out customers who spent more than $1,000 last year, but only $250 so far in 2017, for example. Or, it can help an airline identify customers who flew four times in 2016, but only once this year.

“This arbitrary question you might want to ask of your customer data — this is stuff that these companies can’t do today,” Shahani said. “It’s impossible for them to quickly get that data.”

Amperity is not a predictive analysis platform; instead, its clients can take this data and then figure out how to tweak their marketing campaigns. Its technology is particularly valuable for companies that are not “internet-first.”

Amperity’s customers, which include Fortune 500 companies, range from a wide variety of industries — one of many differences from Shahani and Slager’s experience at Appature.

“We have a very big vision around what we see this business capable of being in terms of its contribution to the market and our customers, and to our employees and this community,” Shahani said. “That’s something that really drives us in a way that I don’t think we were driven by before and thought about before.”

Shahani and Slager have long-time ties to the Seattle area and are bullish about creating the next great local startup. Shahani said today marks a milestone in reaching that goal.

“If we continue to play our cards right, continue to serve our customers well, and continue to build great product, we will have the same opportunity that many of those lighthouse companies like Tableau, Apptio, and others have done,” he noted.Microsoft CEO Satya Nadella speaks at Microsoft Envision.

Being in the Seattle region also helped Amperity link up with Microsoft for a “really meaningful partnership,” said Shahani, who has spent the past year working directly with Microsoft CEO Satya Nadella. Microsoft customers will get access to Amperity via its independent software vendor system while the company will look to integrate the platform with its Adobe partnership.

“The way Amperity lights up products like Azure, Azure Data Lake, Azure Machine Learning, the Power BI stack, Dynamics — when you feed those products with better, unified data about the customer that is more complete, those products perform a lot better for the user,” Shahani explained. “Satya was very persuasive in getting us to build Azure compatibility, which is something we hadn’t done historically with our product.”

Shahani noted that Amperity is not exclusive to Azure and expects the same level of integration with Amazon Web Services in the future.

Amperity has steadily added veteran talent to its leadership team, from bringing on Dave Fetterman as vice president of engineering, to hiring Amy Pelly as its CFO, to adding Aashish Dhamdhere as vice president of marketing.

But Amperity has also seen a bit of churn with some hires. Shahani acknowledged a “sub-15 percent attrition” but said the company considers that within a normal range.

“One thing we’ve always been committed to as an operator is that if things aren’t working, you try to make it work where it makes sense, but if it doesn’t, you make the call early,” he said. “It’s not always us saying it’s not a fit, or the other party saying it’s not a fit. Sometimes you both look each other in the eye and say, ‘you know what, we thought this was a great idea, but for both of us it turns out it’s not working out.’ We’ve worked extra hard to make sure we have those conversations candidly and we do them quickly when we realize there isn’t a great fit and we treat people the way we want to be treated on the way out.”

Shahani said the company is still working off its initial $9 million investment — “we’ve been very capital efficient,” he noted — but expects to entertain additional funding conversations down the road.

Amperity is celebrating its launch with an event in Seattle today featuring executives from Crate & Barrel, Louis Vuitton Moët Hennessy, Alaska Airlines, Nordstrom, and Starbucks.

“Every brand wants to have a more personal relationship with their customers, but they often have data in disparate locations and systems,” Matt McIlwain, managing director at Madrona Venture Group and Amperity board member,’ said in a statement. “Amperity combines modern machine learning and cloud technology to create compelling customer data management solutions that help the world’s leading brands serve those customers better.”

[financestrats]

ism manufacturing: Potential of A Comprehensive Overview

ism manufacturing Introduction:

In the constantly changing global economy, Ism Manufacturing stands out as an excellent example of how modern technology and innovative methods can significantly transform the way businesses work. The language in this text might seem difficult, but by expanding the content and using simpler words, a clearer understanding of the topic can be reached.

Adopting this new approach, which deviates from conventional manufacturing methods, lends itself to more efficient, flexible, and customer-focused processes. As we proceed to discuss this topic, we will delve into Ism Manufacturing's origins, essential concepts, and overall impact, placing emphasis on its practical application and addressing the challenges it faces. With a comprehensive grasp of the subject, the content will become more accessible and reader-friendly. Throughout the conversation, numerous transitional phrases will be employed to ensure enhanced clarity and coherence.

Initially, by examining the historical evolution of Ism Manufacturing,

we gain a deeper understanding of its enduring relevance. Subsequently, the exploration of key ideas will further clarify the concept's core components and, consequently, help elucidate its importance. Furthermore, discussing Ism Manufacturing's extensive influence will unveil the benefits it has on the broader industry landscape. Additionally, studying effective implementation methods will enable businesses to harness the model's full potential, fostering better results.

Nevertheless, it is crucial to acknowledge and assess the obstacles that Ism Manufacturing must surmount.

Therefore, identifying potential roadblocks and shedding light on potential solutions will lead to a more well-rounded comprehension of the concept. This new method, which moves away from usual manufacturing practices, helps make processes simpler, more adaptable, and focused on the customer. In the coming conversation, we will look into where Ism Manufacturing comes from, its main points, and its effects. On top of that, we will pay attention to the best ways to use it and talk about the issues to fix. By learning about this subject in detail, the content will become easier to understand for everyone. To help make it clear, several connecting words will be used throughout.

To start off, knowing the past of Ism Manufacturing helps show how it has changed over time; this lets us see why it is important today. After that, exploring central ideas will make the key aspects of Ism Manufacturing clearer, which helps us grasp the topic better. More than that, the impact of Ism Manufacturing is a big deal, as using it offers benefits to not just the companies that choose it, but others too. So, going into more detail about what it brings to the table is very helpful. What's more, learning the right way to apply Ism Manufacturing means businesses can make the most of it, improving their overall work. However, it's worth noting that Ism Manufacturing comes with challenges we need to face. As a result, we will bring up these issues, highlight what needs work, and discuss how to fix these problems. In the end, by looking closely at different parts of Ism Manufacturing -from its history and main ideas to its effects, best use, and challenges - we will create a simple story that makes this difficult idea easier to understand.

I. Origins and Evolution of Ism Manufacturing

A. Historical Background and Development At first, conventional manufacturing techniques were widely used. But as time passed, Ism Manufacturing came forward as a groundbreaking alternative. B. Technological Advancements and Recent Breakthroughs It's essential to note that developments such as artificial intelligence, robots, and the Internet of Things have really helped Ism Manufacturing grow, making it more versatile and creating new possibilities. C. Main Factors Behind Its Growth Simple factors like using machines, fast data checking, growing customer wants for personalized items, and connections around the world have led to the expansion of Ism Manufacturing, pushing businesses to explore new ideas. If the language here is too hard to understand, we can provide more detail and simpler words to reach a wider audience.

II. Key Ideas in Flexible Production Systems

A. Using Flexible Resources and Adapting First, using resources wisely and adjusting to market changes and customer needs are crucial in Ism Manufacturing. B. Working Together and Sharing Information Next, cooperating and sharing information easily helps businesses get useful insights, making operations better and driving smarter decisions. C. Creating a Creative and Customized Environment Also, having a creative and innovative atmosphere is important to Ism Manufacturing, supporting unique solutions for different customers. D. Focusing on Customers in Production Lastly, a customer-focused approach is essential in Ism Manufacturing, leading to happier and loyal customers.

III. Using – A Smart Plan

A. Finding Market Gaps and Meeting Different Customer Needs Mainly, companies should find specific market gaps and create products and services for different customer wants, increasing satisfaction and loyalty. B. Making a Flexible Supply Chain and Adapting Production Then, building a flexible supply chain is important in Ism Manufacturing, allowing quick changes to market conditions and staying competitive. C. Creating and Applying Ism-based Production Systems Also, usingFlexible Production Systems ideas in the production process increases efficiency, adaptability, and customer happiness. D. Promoting Ongoing Improvement and Tech Progress Finally, encouraging continuous improvement and tech growth helps businesses stay flexible, competitive, and responsive to changing markets.

IV. The Far-Reaching Impact

A. Effects on the Global Economy: First, Flexible Production Systems can improve how the world's economy works by increasing productivity across sectors. Second, advanced technology leads to new inventions and progress in many fields. Finally, this new approach to manufacturing opens up more job opportunities, shaping the workforce of the future and supporting job growth. B. Effects on Industries and Businesses: First, Ism Manufacturing offers new advantages to companies that want to stand out in a changing world. Next, businesses that follow these methods are more likely to find new growth opportunities and gain profits in untapped markets. Lastly, using makes companies stronger and more flexible in a competitive and uncertain market, ensuring lasting success.

V. Overcoming Roadblocks and Challenges

A. Solving Tech Adoption and Infrastructure Issues Addressing technology problems and strengthening support is vital for success. B. Ensuring Data Security and Privacy in the Internet Age Very importantly, businesses need to keep data safe and private to maintain trust and meet rules and regulations. C. Understanding Rules and Making Sure Companies Follow Them Effectively working with set guidelines contributes to successful use and long-lasting results of Ism Manufacturing. D. Closing the Skills Gap Between Tradition and the Future Finally, preparing workers for new technologies and systems helps an organization's ability to adopt Ism Manufacturing for lasting success. Learn more about Flexible Production Systems and their impact on manufacturing

Conclusion:

To sum up, our detailed study shows that Ism Manufacturing is a fantastic opportunity for businesses, and it can have a positive effect on the world's economy. By understanding its main concepts, impacts, smart uses, and challenges, companies all over the world can use its potential to do well in fast-moving markets. While it may not be perfect for every industry, is a great example of what can happen when we combine creativity, flexibility, and customer needs. By adopting these approaches, businesses can competently confront forthcoming obstacles and maintain their success in a highly competitive environment. Employing an abundance of transitional expressions will serve to enhance the flow and clarity of this concept. Consequently, it becomes evident that when organizations utilize these techniques, they can proficiently tackle imminent challenges, thereby ensuring continued growth and achievement in their respective markets "Learn more about the global economic trends we've covered previously." Read the full article

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Comparing ChatGPT to Software Developers: Which is Superior?

Quick Summary: As everyone is aware, AI has grown into one of the hottest topics today. Almost every tech giant has announced some sort of plan to start building AI-powered applications. Most importantly, not only have the tech giants gotten involved, but major players in the open-source community have also gotten on board with AI. You can find AI projects for cloud, serverless, event serverless, etc. Microsoft has introduced its Bot Framework and Cognitive Services tool set and IBM has developed Watson. Google too has played a huge role in the development of neural networks and Deep Learning by introducing TensorFlow which now runs on Google Cloud Platforms.

Technology has taken over our lives—we use it in almost every aspect of our professional and personal lives. From mobile phones to tablets, tech has invaded most corners of the universe. This shift in how we view and use devices has greatly impacted our lives. While technology can be used to further any industry on a large scale, it’s up to you, the individual, to figure out how to get the most out of technology.

As developers, we are always looking for ways to increase our productivity. One of the most powerful tools that can help us in this quest is AI. You might be familiar with machine learning, neural networks, and deep learning, but generative AI acts differently from these technologies. It is a creative tool that can shine a light on your development projects and improve your efficiency.

What is ChatGPT?

ChatGPT is an advanced language model developed by OpenAI. It is part of the GPT-3.5 series, which stands for “Generative Pre-trained Transformer 3.5.” GPT-3.5 is trained on a vast amount of text data, enabling it to generate human-like responses and understand complex language patterns. ChatGPT is designed to engage in conversational interactions and provide responses to a wide range of prompts, making it useful for various applications such as customer service, virtual assistants, and general conversation. It can understand context, generate coherent responses, and adapt to different conversational styles. While it excels in natural language processing, it is important to note that ChatGPT is not a substitute for human software developers but rather a tool to assist and augment their work. It can provide suggestions, help with code snippets, and offer insights, but the creativity, problem-solving skills, and domain expertise of human developers remain invaluable in software development.

Key Features of ChatGPT

ChatGPT is an engine that produces conversational content in real-time. Its main distinctive feature is a sophisticated computer vision system as a plugin to the core ChatGPT engine providing chat scripts with the ability to understand the human spoken language and identify image and video content extracted from the internet.

ChatGPT is the world’s first artificial intelligence AI chatbot to develop any code for you. ChatGPT uses a huge language model and deep learning to convert your text into revolutionary programming code.

The AI technologies developed by Google are really impressive. According to a report by Stanford University, ChatGPT has 175 billion parameters and is trained on 570 gigabytes of text. This shows how powerful this AI tool is, as it is capable of performing tons of computations and processing with a large set of text data.

ChatGPT is an artificial intelligence software-powered chatbot solution. It doesn’t just provide answers in English, but can also interact in other languages. ChatGPT aims to make learning English and other languages a fun and engaging experience that you can access anytime, anywhere, and on any device.

ChatGPT and software development

In late November 2022, ChatGPT came out on the market. A brand new communication tool created by an ambitious company whose name was also ChatGPT. At first glance, this tool might seem like a standard chat app that isn’t going to be very popular, but that isn’t the case at all. ChatGPT turned out to be an incredibly advanced communication app, bristling with hidden features that made it one of the most powerful development tools in the world. However, what most users were not aware of was that this software was capable of doing something else as well. Developers quickly discovered that ChatGPT also had the potential to automatically generate source code as well. This revolutionized a huge sector in the programming industry, showing programmers how they could make their life easier by using this tool.

ChatGPT is an interesting tool that has the ability to automate parts of the programming process. It can write huge chunks of code with all instructions included, just by getting the developer’s input on a number of options. The final result is always correct and even understandable, requiring only basic final checks to ensure that no genuine error has been made in the process.

Many people fear that this great evolution could replace their jobs in the future. This is not an overestimation, but a very valid point to examine. The capabilities shown by ChatGPT are certainly exemplary, which is why its potential growth in the future and the implications thereof have people concerned  in spite of the fact that artificial intelligence in applications is still in its early stages of development and its full impact on society is uncertain

Enhancing Software Development or a Potential Threat?

ChatGPT is not necessarily a threat to software developers. While it can assist in certain aspects of software development, such as providing code suggestions and answering programming-related questions, it is not capable of replacing the skills and expertise of human developers. ChatGPT is a tool that can help streamline certain tasks and provide insights, but it lacks the ability to fully comprehend complex business requirements, make critical decisions, or architect complex software systems. Software development requires creativity, problem-solving abilities, and deep domain knowledge, which are currently beyond the scope of AI models like ChatGPT. Therefore, software developers should view ChatGPT as a helpful tool rather than a threat to their profession.

Advantages of ChatGPT for Software Developers

Natural Language Interface: Developers are able to communicate with code at an unprecedented level of clarity through the use of ChatGPT’s natural language interface. This removes the frustration and time wasted on programming by making it more accessible than ever before.

Streamlined Development Process: ChatGPT is an automation platform that allows Web and Mobile Developers to automate their development processes, surfacing features and functionality that allow developers to focus on the logic and functionality of their applications. This results in higher-quality software products for their clients/customers.

Enhanced Collaboration: Natural language is a key technology in ChatGPT’s fully distributed platform. The chatbot provides a natural interface for developers to maintain the conversation with non-technical stakeholders, like project managers and clients. This has led to more efficient development processes and higher-quality software products, such as conferencing tools and a new customized “Bot for Events” product for event organizers.

Increased Efficiency: ChatGPT is a powerful code automation tool for everyone in the web development field. Whether you are a developer, designer, or content writer, the ChatGPT can help you complete tasks with increased speed and improved accuracy. The multitude of features that ChatGPT has to offer allows users to focus on more important projects.

Improved Accessibility: ChatGPT’s natural language interface can make software development more accessible to individuals who may not have a background in coding. This can lead to a more diverse and inclusive development community by lowering the barrier of entry for non-tech-minded creators.

Drawbacks of ChatGPT

ChatGPT is undeniably a great artificial intelligence tool that comes with various perks for software development. However, it is still a computer bot and has a few limitations in its systems. These boundaries can’t be passed by the AI itself, and can only be resolved by a human. This is one of the core reasons why human developers still have an edge over ChatGPT. There are numerous jobs in which software developers need to work themselves because they can’t be done using automated code.

It has been proven many times that automated software cannot code a program that meets product custom requirements. It is something that a human developer can do. Humans can perform quality checks in an app, and fine-tune it so that it can run perfectly. Tools such as ChatGPT cannot perform all the tasks efficiently and are limited to defined tasks.

Conclusion  

ChatGPT uses deep learning to assist software engineers to develop more efficiently. This software is intelligent enough to create code from scratch based on nothing more than the user’s intent. It uses many techniques including natural language processing and backpropagation to enable understanding where the gaps in the specification exist and complete the user’s intent through simple chat-based text communication. In addition, this revolutionary chatbot solution can test code prototypes, perform unit tests and simplify repetitive tasks.

AI is being applied widely in software development and many see it as a quick fix to their company’s problems. But in reality, AI is not intelligent enough to be relied on instead of human developers. ChatGPT is capable of writing code, but not at the level that a human can. While AI will continue to advance and become more sophisticated, it will never be able to match the creative spark of a living, breathing human being. If you want software development services for your project, contact Brain Inventory we will provide you with the best solution at an affordable price.

[datascienceinforgraphics]

Price Optimization – How to use Data Science To Increase Conversions

Data science is the application of statistical techniques to large amounts of data. Many businesses use data science to optimize their pricing models and other processes. This article will look at a few different ways companies can leverage data for profit optimization and provide some recommendations for other points businesses should consider when it comes time to optimize their pricing strategies.

What is Price Optimization in Data Science?

To begin, we need to understand what exactly is meant by “pricing optimization”. Pricing optimization refers to changing the price of a product or service so that it increases profits for a company. The goal is to reduce costs while raising revenue for the company. The process usually involves collecting data about customer behavior and trends in order to determine how much customers are willing to pay for different products or services. This allows companies like yours to identify which price points will maximize sales while minimizing losses due to unsold inventory or excess inventory costs (such as shipping).

There are many ways that businesses can use data science when pricing their products or services. For example, one popular approach involves using machine learning algorithms such as neural networks which can be used to predict future prices based on past patterns of demand/supply relationships such as seasonal sales cycles. For neural network and other ML techniques, refer to the machine learning course in Pune. Data scientists use algorithms that analyze large sets of data to determine what types of pricing patterns are most effective at getting consumers to buy more products from a particular company or brand.

For example, if a business has already established that its customers are willing to pay $50 for a certain type of product but they don't have any sales data yet, then they may want to change their price point in order to increase their sales volume. This could mean raising prices on all items by $10 or lowering prices on all items by $10.

Dynamic Pricing Vs. Price Optimization

While these two factors are commonly used interchangeably, they really refer to separate ideas. The primary distinction between the two is that whereas price optimization can employ any form of pricing strategy to accomplish its objectives, dynamic pricing is a specific type of pricing strategy.

For instance, retailers can dynamically change the prices of their items to match the price of their competitors by utilizing a dynamic pricing strategy. Although this technique would require frequent price changes, it may not always be the right one. Price optimization techniques concentrate on identifying the price that increases a specified cost function (such as the company's margin), taking into account a variety of variables to recommend such a price or price range for various circumstances.

This can be done in a dynamic manner depending on the specific use case, therefore in many cases combining dynamic pricing with optimization is the best choice.

Benefits Of Data-Driven Price Optimization

Data-driven price optimization offers the great benefit of allowing for the consideration of price elasticity. But using machine learning to sort through data and establish the best prices for your goods and services will also bring about a number of additional significant advantages. Some of the key benefits are: 

Balanced viewpoint on prices

The biases that shape how people think about issues and develop solutions can inhibit advancement. However, when given accurate, high-quality data, machine learning models demonstrate distinct, bias-free problem-solving abilities.

Complex computing

The average human brain is not as computationally capable as AI. Therefore, it may become impossible to even consider the rising complexity of pricing structures. Fortunately, machines can handle this part by accounting for each new variable and computing how it affects the variables that already exist. This is crucial, particularly given that for effective outcomes, the number of data sources must also increase daily and be taken into account almost immediately.

Free of human error

Machine learning models are trustworthy and they eliminate human error because they are inherently mathematical and precise constructs. Additionally, you might not need 100% accuracy to figure out the best price, depending on a variety of factors and scenarios. You might want some space to maneuver and make adjustments. You'll be happy to know that you can modify the level of accuracy in accordance with your unique requirements in that case.

Predictive capabilities

The most captivating advantage of using machine learning to optimize your price is that, with enough data, your pricing models can spot patterns that would normally go unnoticed and predict pricing trends that might otherwise catch your company off guard. You can prepare and modify your prices in the best possible way by utilizing these predictive insights.

Price Optimization With Machine learning

The procedure for using a machine learning model to enhance your pricing strategy is as follows:

Gather Data

A historical data set, current data, or—as is most frequently the case—a combination of the two—are all possible sources of data for machine learning models. Regardless, before starting price optimization, the model must first be trained using a starting set of data.

Define goals and limits

The model is shaped here by the parameters you provide. These settings will particularly inform the model on the KPIs that matter most to you. 

Choose an algorithm 

Although the concept "machine learning" is generic, there are several subtypes. These algorithms can be generative or discriminative, supervised or unsupervised, explainable or unquantifiable, etc. Find out if deep learning techniques are applicable? Working with a data scientist will be necessary to choose the best algorithm for your requirements.

Modeling and Training

The training data is then used to build and prepare each individual model. You may now begin to analyze if the decisions you made in stages one through three were the proper ones.

Adjust the prediction and mechanism

At this point, the model goes through many rounds, testing various assumptions and modifying its prediction algorithm. In essence, this is how a machine learning model "learns."

Execute and adjust 

Once you've decided on pricing, it's time to put it to the test, collect data, and repeat the procedure. This stage is always required and should be continuous, since modifications will most likely be required in the future.

Conclusion

To sum it up, price optimization is the process of finding the ideal price for a product by using various statistical procedures like regression, neural networks, non-parametric estimation, etc. This will help maximize the revenue for a company.

Instead of programming each time a new pricing beam is created, this algorithm can improve the quality of a pricing beam that already exists. And you know what else? It will churn out numbers that are better than what the merchant's intuition could come up with.

If you're curious about where to start learning data science, a quick search for a Data Science course in Pune  will provide you with options that work with your schedule and budget.

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What Are the Best Ways to Juice? Top Tips for Juicing Successfully

What Are the Best Ways to Juice? Top Tips for Juicing Successfully Looking for the best ways to juice? Do you know what benefits juicing can bring? Juicing has been shown to help people’s health in a number of ways. It can increase metabolism, which raises your energy and stamina. It can also help you eliminate junk foods by satisfying your body’s nutritional needs. Perhaps this has made you interested in learning more about the benefits of juicing. Look no further! Use a masticating juicer. This type of juice machine will gently extract the juice and help retain more nutrients in the juice. Juice from masticating juicers also stands up better to storage. If you juice dark, leafy greens like spinach, add some cucumber. Dark greens can be bitter if used alone in your juice. Cucumber will not only mask this taste, but adds a refreshing flavor to your drink. Using the cucumber with the peel on will also add extra nutrition to your drink. When making juices to foster better health, select darker green produce for the foundation of your drinks. Dark vegetables, including broccoli, chard or spinach, should constitute as much as 75 percent of your juices. To give the juice a palatable taste, round it out with your favorite fruit juice. Choose the most ripe and sweetest apples you can find to make homemade apple juice with. It is okay to get apples that are bruised, just make sure you cut them out. Apples like Red Delicious, Gala, Fuji, and Rome have sweet tastes that lend themselves well to producing a rich, sweet, and flavorful juice. Immerse yourself in the taste of the juice, and don’t rush things. Give your body time to take in all of its delightful flavors. Leave the juice in your mouth so that it can blend with your saliva, beginning the digestion process. Juicing vegetables is a great way to get healthy foods into a child who won’t eat them whole. There are lots of kids who aren’t crazy about vegetables. So instead of forcing the vegetables on them, you can juice some fruits as well as vegetables and combine the juices. They will enjoy drinking the juice and not even realize that they are consuming vegetables. Look into the benefits of each fruit and vegetable you’d like to include in your juices before you choose your recipes. Different foods offer different benefits for your body; some are high in vitamin C, whereas another item might be rich in antioxidants. You may want to mix different fruits and vegetables together to ensure you get essential nutrients and vitamins. Not only will your body benefit from all the healthy nutrients you’ll take in, but your palate might also enjoy some of the blends you’ll be tasting. You can get rid of the pulp by using a coffee filter. Some juices will be pulpy when you finish them. Just pour juice into a coffee filter to strain. Cheesecloth also works very well for this task. Storing the fresh juice in a refrigerator is a really good idea, but keep in mind the juice will change colors. Brown or off-colored juice is less than appetizing. Try juicing half a lemon into the juice you plan to store. The lemon juice will help the juice retain its bright color, and it will not significantly alter the flavor. When you select a juicer to purchase, make sure you choose one that is easy for maintenance. If your juicer is time consuming to assemble and clean, you are less likely to use it on a regular basis. You will have to take the time out to clean your juicer whenever you use it, though, to ensure that no mold begins to grow and the blade stays sharp. Use cranberries as part of your juicing routine if you are suffering from a bladder condition or urinary tract infection. Once you feel problems starting, begin to add them to your juice. Listen to the smoothie diet plan your body if it reacts negatively to any of the juice that you drink. Sometimes a certain fruit or vegetable can cause your system to become upset. If you experience nausea or other stomach upset, take the time to identify the ingredient that might have caused it. Often this will be something you rarely consume. Many people can condition their bodies to tolerate the ingredient by eating small amounts to begin with. If you have specific nutritional requirements, it is in your best interest to explore different varieties of fruit that you may not ordinarily eat. This allows you to consume nutrients you don’t get from your whole food diet. Adding apples or lemons to the juice will help to mask undesirable flavors. Try using negative caloric foods in juicing so that you can get the nutrition you need without having to burn fat from them. Some common negative calorie foods are dark greens, including the childhood menaces of broccoli, cabbage and kale. You also want to consider getting fruits high in fiber, so that you can break down and digest food properly. Serve your juice quickly after you prepared it. That’s the best time to drink it because that is when it has the most nutrients. Getting the entire family involved can make juicing fun. Your children can wash the ingredients before you chop them. It is vital that you think about how certain juices can affect the way your teeth look. There are some juices that will stain your teeth, so bear this in mind. This can happen with juices from things like beets and carrots. Brush your teeth immediately after consuming juices made with these veggies if you have stain-prone teeth. Use a variety of different fruits and vegetables to make a tasty juice that will help fight off constipation. Consuming fresh juice daily is a great way to solve chronic constipation. Now that you’ve learned more about some of the benefits of juicing, we hope that you’ll consider juicing as a means to improved health and quality of life! There are a variety of delicious recipes for juicers, and you’ll doubtless find it easy to incorporate many of them into your daily routine.

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Wondering if RPO is here to stay or not? Dive deep into this article to find out.

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Synthetic Dataset And Text Dataset In Artificial Intelligence

Let's consider a scenario. You receive a project that requires you to create an AI system which can tell if doors are open or shut the use of images. Computers are now stupid. Really dumb. The computer isn't aware of what an open-door looks like or what a closed-door has.

Text mining is among the primary methods through that we manage and organize Speech Recognition Dataset that is not structured that accounts for 80percent of all the data that is generated. Large organizations and companies store massive amounts of data, and typically it is kept in huge data warehouses as well as cloud platforms. To build models like this you need to feed the model with these two kinds of images. In order to build models, you require top-quality data. You've collected hundreds or thousands of photos with closed and open doors. Now, in order to help the model recognize the different doors, it is necessary to mark (or label) every photo, with the doors closed and open for the purpose of training the AI model.

What exactly is Image Annotation?

Image annotation of data or images is a method where annotators label objects within the image in order to help the AI model recognize them even in images that are not labeled. This helps to identify how to classify, categorize and group various objects that are part of an algorithm that uses machine learning for efficient learning of data.

Text mining: What exactly is it?

The process of text mining is also referred to by the name of text mining also known as text analytics is the method of converting Text Dataset into structured formats to find high-quality patterns and information. Information we collect via text messages, documents emails, files, and other documents is written in simple text. It is mostly used to find patterns or information from large quantities of information.

What are the various types of Image Annotation?

There are four different types of classification. The nature and degree of complexity of your project will determine the kind of annotation for images you employ.

Image classification

This is a form of machine-learning model in which an image is only one object. The goal of image classification is finding the object within an image and does not consider its place of the object. If you have an image where a cat could be seen sitting in a position. In the classification process the image, you don't identify the spot where the cat is; you inform the computer of it as a cat in the image.

Object detection

In the process of detecting objects There are a variety of factors like determining the location, presence and the number of objects within the picture. Annotators draw boxes around objects, which allow the model to determine the location and the amount of objects within the frame.

Image segmentation

Image segmentation is one type of technology where an object is annotated pixel for pixels. There are three elements of image segmentation semantic segmentation and instance segmentation as well as panoptic segmentation.

Tracking objects

After the object has been identified and tracked, it is then used to determine the location of an object within sequences of frames, as in videos. The movement or tracking of an object is analyzed using surveillance footage, or camera footage.

What are the techniques for mining text?

Text mining is several operations that enable you to obtain information from text data that is unstructured. Text mining methods include:

Information Retrieval: Based on a set of pre-defined phrases or queries, Information Retrieval (IR) is a method of retrieving relevant documents or information. Algorithms are utilized within IR systems to observe the behavior of users and to locate relevant information. Information retrieval is widely used in cataloguing systems for libraries and popular search engines like Google.

"NLP," as it is known, refers to Natural Language Processing came from computational linguistics and utilizes features from a range of disciplines, such as the fields of computer science, artificial Intelligence and data science to aid computers in understanding human language both in audio and written form. NLP can allow computers to "read" by analyzing sentences' syntax and structure.

3.Data Mining: the method of identifying patterns and obtaining valuable insights from large amounts of data is referred to by the term data mining. This method analyzes both unstructured and structured data to uncover new data and is commonly employed in sales and marketing to study consumer behavior. The process of text mining can be described as a type of data mining which concentrates on giving unstructured information structure, and then analyzing it to generate new information. Textual data analysis is a part of the methods described above that are a type that are part of the data mining.

What are the latest trends in the industry?

As per Gartnerresearch the Gartnerresearch report, synthetic data may be more suitable for AI to train for. It is suggested that synthetic data may prove to be more beneficial than information gathered from actual individuals, events or objects. This efficiency of synthetic data is one reason why it is why deep learning neural network designers are increasing utilizing it to create top-of-the-line AI models.

A study on synthetic data suggested the year 2030 would be when the majority of the data used in machine learning models for for training will be created by algorithms, computer-generated simulations such as statistical models, and many more. But, synthetic data makes up less than 1 percent of market data today, however by 2024, it is predicted to make up over 60% of the data created.

Benefits of Synthetic Data

The data scientists of today are continuously searching for AI Training Datasets that is trustworthy and balanced, free of bias and can be identified as having distinct patterns. Some of the benefits of using data that is synthetic include:

Synthetic data is much easier to produce, and takes less time to note down, and is more well-balanced.

Since synthetic data complements real-world data this makes it simpler to fill in data gaps that exist in the real-world

It's scalable, adaptable, and provides privacy and protection of personal data.

It is not contaminated by bias, data duplication and inaccuracies.

Access to information is available that pertains to uncommon events or edge cases.

Data generation is speedier, less expensive, and more precise.