#Fake News Detection using Machine Learning

[naamahdarling]

I'm probably going to piss some people off with this, but.

The use of AI and machine learning for harmful purposes is absolutely unacceptable.

But that isn't an innate part of what it does.

Apps or sites using AI to generate playlists or reading lists or a list of recipes based on a prompt you enter: absolutely fantastic, super helpful, so many new things to enjoy, takes jobs from no-one.

Apps or sites that use a biased algorithm (which is AI) which is not controllable by users or able to be turned off by them, to push some content and suppress others to maximize engagement and create compulsive behavior in users: unethical, bad, capitalism issue, human issue.

People employing genAI to create images for personal, non-profit use and amusement who would not have paid someone for the same service: neutral, (potential copyright and ethics issue if used for profit, which would be a human issue).

People incorporating genAI as part of their artistic process, where the medium of genAI is itself is a deliberate part of the artist's technique: valid, interesting.

Companies employing genAI to do the work of a graphic designer, and websites using genAI to replace the cost of stock photos: bad, shitty, no, capitalist and ethical human issue.

People attacking small artists who use it with death threats and unbelievable vitriol: bad, don't do that.

AI used for spell check and grammar assistance: really great.

AI employed by eBay sellers to cut down on the time it takes to make listings: good, very helpful, but might be a bad idea as it does make mistakes and that can cost them money, which would be a technical issue.

AI used to generate fake product photos: deceptive, lazy, bad, human ethical issue.

AI used to identify plagiarism: neutral; could be really helpful but the parameters are defined by unrealistic standards and not interrogated by those who employ it. Human ethical issue.

AI used to analyze data and draw up complex models allowing detection of things like cancer cells: good; humans doing this work take much longer, this gives results much faster and allows faster intervention, saving lives.

AI used to audit medical or criminal records and gatekeep coverage or profile people: straight-up evil. Societal issue, human ethical issue.

AI used to organize and classify your photos so you don't have to spend all that time doing it: helpful, good.

AI used to profile people or surveil people: bad and wrong. Societal issue, human issue, ethical issue.

I'm not going to cover the astonishingly bad misinformation that has been thrown out there about genAI, or break down thought distortions, or go into the dark side of copyright law, or dive into exactly how it uses the data it is fed to produce a result, or explain how it does have many valid uses in the arts if you have any imagination and curiosity, and I'm not holding anyone's hand and trying to walk them out of all the ableism and regurgitated capitalist arguments and the glorification of labor and suffering.

I just want to point out: you use machine learning (AI) all the time, you benefit from it all the time. You could probably identify many more examples that you use every day. Knee-jerk panicked hate reflects ignorance, not sound principles.

You don't have beef with AI, you have beef with human beings, how they train it, and how they use it. You have beef with capitalism and thoughtlessness. And so do I. I will ruthlessly mock or decry misuse or bad use of it. But there is literally nothing inherently bad in the technology.

I am aware of and hate its misuse just as much as you do. Possibly more, considering that I am aware of some pretty heinous ways it's being used that a lot of people are not. (APPRISS, which is with zero competition for the title the most evil use of machine learning I have ever seen, and which is probably being used on you right now.)

You need to stop and actually think about why people do bad things with it instead of falling for the red herring and going after the technology (as well as the weakest human target you can find) every time you see those two letters together.

You cannot protect yourself and other people against its misuse if you cannot separate that misuse against its neutral or helpful uses, or if you cannot even identify what AI and machine learning are.

[qqueenofhades]

I’m in undergrad but I keep hearing and seeing people talking about using chatgpt for their schoolwork and it makes me want to rip my hair out lol. Like even the “radical” anti-chatgpt ones are like “Oh yea it’s only good for outlines I’d never use it for my actual essay.” You’re using it for OUTLINES????? That’s the easy part!! I can’t wait to get to grad school and hopefully be surrounded by people who actually want to be there 😭😭😭

Not to sound COMPLETELY like a grumpy old codger (although lbr, I am), but I think this whole AI craze is the obvious result of an education system that prizes "teaching for the test" as the most important thing, wherein there are Obvious Correct Answers that if you select them, pass the standardized test and etc etc mean you are now Educated. So if there's a machine that can theoretically pick the correct answers for you by recombining existing data without the hard part of going through and individually assessing and compiling it yourself, Win!

... but of course, that's not the way it works at all, because AI is shown to create misleading, nonsensical, or flat-out dangerously incorrect information in every field it's applied to, and the errors are spotted as soon as an actual human subject expert takes the time to read it closely. Not to go completely KIDS THESE DAYS ARE JUST LAZY AND DONT WANT TO WORK, since finding a clever way to cheat on your schoolwork is one of those human instincts likewise old as time and has evolved according to tools, technology, and educational philosophy just like everything else, but I think there's an especial fear of Being Wrong that drives the recourse to AI (and this is likewise a result of an educational system that only prioritizes passing standardized tests as the sole measure of competence). It's hard to sort through competing sources and form a judgment and write it up in a comprehensive way, and if you do it wrong, you might get a Bad Grade! (The irony being, of course, that AI will *not* get you a good grade and will be marked even lower if your teachers catch it, which they will, whether by recognizing that it's nonsense or running it through a software platform like Turnitin, which is adding AI detection tools to its usual plagiarism checkers.)

We obviously see this mindset on social media, where Being Wrong can get you dogpiled and/or excluded from your peer groups, so it's even more important in the minds of anxious undergrads that they aren't Wrong. But yeah, AI produces nonsense, it is an open waste of your tuition dollars that are supposed to help you develop these independent college-level analytical and critical thinking skills that are very different from just checking exam boxes, and relying on it is not going to help anyone build those skills in the long term (and is frankly a big reason that we're in this mess with an entire generation being raised with zero critical thinking skills at the exact moment it's more crucial than ever that they have them). I am mildly hopeful that the AI craze will go bust just like crypto as soon as the main platforms either run out of startup funding or get sued into oblivion for plagiarism, but frankly, not soon enough, there will be some replacement for it, and that doesn't mean we will stop having to deal with fake news and fake information generated by a machine and/or people who can't be arsed to actually learn the skills and abilities they are paying good money to acquire. Which doesn't make sense to me, but hey.

So: Yes. This. I feel you and you have my deepest sympathies. Now if you'll excuse me, I have to sit on the porch in my quilt-draped rocking chair and shout at kids to get off my lawn.

[ranidspace]

So, this is a scary headline so we're gonna read it closely.

TechCrunch managed to get an internal company memo that details a few "strategic corrections" for the myriad Mozilla products. Mozilla has a "mozilla.social" Mastodon instance that the memo says originally intended to "effectively shape the future of social media," but the company now says the social group will get a "much smaller team." Mozilla says it will also "reduce our investments" in Mozilla VPN, Firefox Relay, and something the memo calls "Online Footprint Scrubber" (that sounds like Mozilla Monitor?). It's also shutting down "Mozilla Hubs," which was a 3D virtual world it launched in 2018—that's right, there was also a metaverse project! The memo says that "demand has moved away from 3D virtual worlds" and that "this is impacting all industry players." The company is also cutting jobs at "MozProd," its infrastructure team.

This is specifically saying that they're just downsizing teams which are focused on things which are NOT the main firefox browser. quote "It now looks like Mozilla may refocus on Firefox once more". layoffs suck, yeah, but firefox doesnt seem to be affected. Mozilla's a small company and firefox is getting bigger, and it looks like this is just a move to shift focus away from the side projects

As for the AI thing, the AI company they bought about was simply one that used machine learning to detect fake product reviews. (what i would say is a good use of machine learning.). "Generative AI" is said thought, and that concerns me a bit, but there's one thing about Firefox that's makes me think it's gonna be fine:

no matter what it is, you can turn it off.

"Pocket" is the weird mozilla thing about saving news articles for later and it recommends you news. you can just turn that off. The home page has sponsored links. you can turn them off. nearly everything about firefox you can just turn it off and ignore forever. if it is some awful AI bullshit, an annoying feature, something whatever it is, you can turn it off. I think firefox would STILL be the best option even if it's worst case. for a private browser, the only other option really is Brave, which is LOADED with web3 and cryptocurrency features and we're at the same problem here, but you cant turn those off completely, you can only just ignore them.

Also it might not even be part of the browser itself, just rather a single website or an extra service that you'll forget exists and then like 2 months later you hear it shuts down. idk.

Let's wait until firefox makes an actual public statement about this shit before anything becuase we literally know nothing. it's likely they're already getting some awful feedback and this may not even make the light of day.

Mozilla is a non-profit organization. i highly doubt they're firing people to replace them with AI. but again. wait and see what they say publically because it's hard to tell

[mariacallous]

Some Fortune 500 companies have begun testing software that can spot a deepfake of a real person in a live video call, following a spate of scams involving fraudulent job seekers who take a signing bonus and run.

The detection technology comes courtesy of GetReal Labs, a new company founded by Hany Farid, a UC-Berkeley professor and renowned authority on deepfakes and image and video manipulation.

GetReal Labs has developed a suite of tools for spotting images, audio, and video that are generated or manipulated either with artificial intelligence or manual methods. The company’s software can analyze the face in a video call and spot clues that may indicate it has been artificially generated and swapped onto the body of a real person.

“These aren’t hypothetical attacks, we’ve been hearing about it more and more,” Farid says. “In some cases, it seems they're trying to get intellectual property, infiltrating the company. In other cases, it seems purely financial, they just take the signing bonus.”

The FBI issued a warning in 2022 about deepfake job hunters who assume a real person’s identity during video calls. UK-based design and engineering firm Arup lost $25 million to a deepfake scammer posing as the company’s CFO. Romance scammers have also adopted the technology, swindling unsuspecting victims out of their savings.

Impersonating a real person on a live video feed is just one example of the kind of reality-melting trickery now possible thanks to AI. Large language models can convincingly mimic a real person in online chat, while short videos can be generated by tools like OpenAI’s Sora. Impressive AI advances in recent years have made deepfakery more convincing and more accessible. Free software makes it easy to hone deepfakery skills, and easily accessible AI tools can turn text prompts into realistic-looking photographs and videos.

But impersonating a person in a live video is a relatively new frontier. Creating this type of a deepfake typically involves using a mix of machine learning and face-tracking algorithms to seamlessly stitch a fake face onto a real one, allowing an interloper to control what an illicit likeness appears to say and do on screen.

Farid gave WIRED a demo of GetReal Labs’ technology. When shown a photograph of a corporate boardroom, the software analyzes the metadata associated with the image for signs that it has been modified. Several major AI companies including OpenAI, Google, and Meta now add digital signatures to AI-generated images, providing a solid way to confirm their inauthenticity. However, not all tools provide such stamps, and open source image generators can be configured not to. Metadata can also be easily manipulated.

GetReal Labs also uses several AI models, trained to distinguish between real and fake images and video, to flag likely forgeries. Other tools, a mix of AI and traditional forensics, help a user scrutinize an image for visual and physical discrepancies, for example highlighting shadows that point in different directions despite having the same light source, or that do not appear to match the object that cast them.

Lines drawn on different objects shown in perspective will also reveal if they converge on a common vanishing point, as would be the case in a real image.

Other startups that promise to flag deepfakes rely heavily on AI, but Farid says manual forensic analysis will also be crucial to flagging media manipulation. “Anybody who tells you that the solution to this problem is to just train an AI model is either a fool or a liar,” he says.

The need for a reality check extends beyond Fortune 500 firms. Deepfakes and manipulated media are already a major problem in the world of politics, an area Farid hopes his company’s technology could do real good. The WIRED Elections Project is tracking deepfakes used to boost or trash political candidates in elections in India, Indonesia, South Africa, and elsewhere. In the United States, a fake Joe Biden robocall was deployed last January in an effort to dissuade people from turning out to vote in the New Hampshire Presidential primary. Election-related “cheapfake” videos, edited in misleading ways, have gone viral of late, while a Russian disinformation unit has promoted an AI-manipulated clip disparaging Joe Biden.

Vincent Conitzer, a computer scientist at Carnegie Mellon University in Pittsburgh and coauthor of the book Moral AI, expects AI fakery to become more pervasive and more pernicious. That means, he says, there will be growing demand for tools designed to counter them.

“It is an arms race,” Conitzer says. “Even if you have something that right now is very effective at catching deepfakes, there's no guarantee that it will be effective at catching the next generation. A successful detector might even be used to train the next generation of deepfakes to evade that detector.”

GetReal Labs agrees it will be a constant battle to keep up with deepfakery. Ted Schlein, a cofounder of GetReal Labs and a veteran of the computer security industry, says it may not be long before everyone is confronted with some form of deepfake deception, as cybercrooks become more conversant with the technology and dream up ingenious new scams. He adds that manipulated media is a top topic of concern for many chief security officers. “Disinformation is the new malware,” Schlein says.

With significant potential to poison political discourse, Farid notes that media manipulation can be considered a more challenging problem. “I can reset my computer or buy a new one,” he says. “But the poisoning of the human mind is an existential threat to our democracy.”

[meret118]

The top five targeted industries are technology (Bad Bots comprise 76% of its internet traffic); gaming (29% of traffic); social media (46%), e-commerce (65%), and financial services (45%). If a bot fails in its purpose, there is a growing tendency for the criminals to switch to human operated fraud farms. Arkose estimates there were more than 3 billion fraud farm attacks in H1 2023. These fraud farms appear to be located primarily in Brazil, India, Russia, Vietnam, and the Philippines.

The growth in the prevalence of Bad Bots is likely to increase for two reasons: the arrival and general availability of artificial intelligence (primarily gen-AI), and the increasing business professionalism of the criminal underworld with new crime-as-a-service (CaaS) offerings.

From Q1 to Q2, intelligent bot traffic nearly quadrupled. “Intelligent [bots] employ sophisticated techniques like machine learning and AI to mimic human behavior and evade detection,” notes the report (PDF). “This makes them skilled at adaptation as they target vulnerabilities in IoT devices, cloud services, and other emerging technologies.” They are widely used, for example, to circumvent 2FA defense against phishing.

Separately, the rise of artificial intelligence may or may not relate to a dramatic rise in ‘scraping’ bots that gather data and images from websites. From Q1 to Q2, scraping increased by 432%. Scraping social media accounts can gather the type of personal data that can be used by gen-AI to mass produce compelling phishing attacks. Other bots could then be used to deliver account takeover emails, romance scams, and so on. Scraping also targets the travel and hospitality sectors.

More at the link.

[wedesignyouny]

Optimizing Insurance with Data Science Insights - Dataforce

Key Highlights

Data science is transforming the insurance industry through advanced analytics and AI integration.

Enhancing fraud detection and improving risk assessment are vital applications of data science in insurance.

Personalizing customer experiences and boosting engagement with data-driven strategies are key focus areas.

Overcoming challenges like data privacy concerns and talent gap is crucial for successful data science implementation in insurance.

Future trends in insurance data science include the rise of AI and machine learning in policy customization and leveraging big data for market analysis.

Introduction

The insurance industry, including auto insurance, is entering a new age of data in the insurance domain. Data science, driven by artificial intelligence (AI), is changing how insurance companies operate. This change is making the industry more focused on data, leading to better risk assessments, customized customer experiences, and an increased risk in smoother operations. This blog looks at how data science is changing the insurance world and what it could mean for the future.

The Evolution of Data Science in the Insurance Sector

The insurance sector has always worked with data. But, in the past, they only focused on simple numbers and past trends in life insurance. Now, with data science, they can look at big and complex data much better. This change helps insurance companies to go beyond old methods and enhance their product offerings through various use cases. They can now use better models to check risks, spot fraud, and know what customers need.

Bridging the Gap: Data Professionals and Insurance Innovations

Insurance companies are now bringing together data science and real-life use through predictive analysis, particularly in the realm of insurance data analytics. They do this by hiring data experts who know about both insurance and data analytics. These experts can use data analytics to tackle tough business issues, including finding new market chances and relevant products, better pricing plans, and improving risk management. They use business intelligence to help make smart decisions and improve how insurance works.

Transforming Insurance Through Data Analytics and AI Integration

The use of AI, especially machine learning, is changing how insurance works in important ways:

Automated Underwriting: AI can look at a lot of data to see risk levels. It helps make underwriting decisions quickly and efficiently.

Fraud Detection: Machine learning helps find fake claims by spotting patterns and odd things that people might miss.

Predictive Modeling: With data science, insurers can predict future events. This includes things like customer drop-off or how likely claims are to happen.

This use of AI is not to replace human skills. Instead, it supports insurance experts, helping them make smarter decisions.

Key Areas Where Data Science is Revolutionizing Insurance

Let’s look at how data science is changing the insurance field. Data science is improving how insurance companies work and opening up new opportunities. It helps in better fraud detection and makes customer interactions more personal. Overall, data science is changing how insurance companies operate and connect with their policyholders.

Enhancing Fraud Detection with Advanced Data Models

Insurance fraud is a big problem. It costs a lot for insurers and their customers. Data science can help to fight fraud by using smart data models. These can find patterns that show fraudulent activities:

Anomaly Detection: Data analysis can spot strange patterns in insurance claims. For example, a sudden rise in claims or higher amounts could suggest fraud.

Network Analysis: By looking at links between policyholders, providers, and others, insurers can find fraud networks or are working together.

Predictive Modeling: Data-driven models can help insurers figure out how likely a claim is to be fraudulent. This helps them focus their investigations better.

Improving Risk Assessment through Predictive Analytics

Data science changes how we assess risks using predictive analytics. These tools help insurers better estimate the chance of future events, like accidents, illnesses, or natural disasters.

Personalized Risk Profiles: Insurers now create risk profiles for each person. They look at personal behavior, lifestyle choices, and where someone lives, instead of just using general demographic data.

Dynamic Pricing: Predictive models help insurers change insurance costs quickly. They adjust premiums based on factors that change, like driving habits tracked through telematics or health information from wearables.

Proactive Risk Management: Insurers can spot risks before they happen. This way, they can help customers reduce risks, stop potential losses, and improve safety overall.

Data Science’s Role in Personalizing Customer Experiences

In today’s tough market, insurance companies need to give a personalized customer experience. Customers now expect services and products made just for them. Data science plays a key role in helping insurance companies understand what each customer wants and needs.

Tailoring Insurance Products with Customer Data Insights

Data science helps insurance companies provide better products to their customers. They can now focus on making insurance products that fit specific groups of people instead of just offering the same products to everyone.

Customer Segmentation: By looking at customer data, insurers can divide their customers into different groups. These groups are based on similar traits, like risk levels, lifestyle choices, or financial goals.

Personalized Product Recommendations: Insurers can use data to suggest the best insurance products for each customer based on their unique profile.

Customized Policy Features: Insights from data allow insurance companies to create flexible policy options that meet the needs of individual customers.

Boosting Customer Engagement with Data-Driven Strategies

Data science helps insurance companies improve how they engage with customers and build better relationships. Here are some ways they do this:

Proactive Communication: Insurers can look at customer data to understand what customers might need. This way, they can reach out to them with helpful info, advice, or special offers.

Personalized Customer Support: With data insights, insurance companies can change their support to fit each person’s needs and past experiences. This helps make customers happier.

Targeted Marketing Campaigns: Data-driven marketing lets companies send messages and offers that are more relevant to different groups of customers, making their campaigns more effective.

These methods not only boost customer satisfaction but also give insurance companies a competitive edge.

Overcoming Challenges in Data Science Application in Insurance

The potential of data science in the insurance business is huge. However, companies face challenges that they must tackle to enjoy these benefits fully. Data security and privacy are key worries. There is also a need for trained data scientists who know the insurance industry well.

Navigating Data Privacy and Security Concerns

As insurance companies gather and study more personal data, it is very important to deal with privacy and security issues.

Data Security Measures: It is key to have strong security measures in place to keep customer information safe from unauthorized access and cyber threats.

Compliance with Regulations: Insurance companies need to follow laws about data protection, like GDPR or CCPA, to ensure they handle data responsibly.

Transparency and Trust: Being open with customers about how their data is collected, used, and protected is vital. This builds trust and supports good data practices.

Addressing the Talent Gap in Data Science for Insurance

There is a bigger demand for data scientists who know a lot about the insurance sector. Filling this gap is important for companies that want to use data science well.

Attracting and Keeping Talent: To draw in and keep the best data science talent, companies need to offer good pay and chances for growth.

Training the Current Team: Insurance companies can put money into training programs to help their workers gain the skills they need for a data-focused job.

Working Together: Teaming up with universities or training groups can help solve the skills gap and open doors to more qualified job candidates.

Future Trends: The Next Frontier in Insurance Data Science

Data science is changing and will bring new and exciting uses in the insurance field. The ongoing progress of AI, along with very large sets of data, will change the industry even more.

The Rise of AI and Machine Learning in Policy Customization

AI and machine learning are expected to play an even greater role in personalizing insurance policies:

AI-Powered Policy Customization: AI algorithms can create highly customized insurance policies that consider individual risk factors, lifestyle choices, and even behavioral data.

Real-Time Policy Adjustments: AI can facilitate real-time adjustments to insurance policies based on changing customer needs or risk profiles.

Predictive Risk Prevention: AI-powered systems can proactively identify and mitigate potential risks by analyzing data from various sources, including IoT devices and wearables.

Future Trend

Description

AI-Driven Chatbots

Provide 24/7 customer support, answer policy questions, and assist with claims filing.

Blockchain for Claims Processing

Enhance the security and transparency of claims processing by creating tamper-proof records.

Drone Technology in Risk Assessment

Used to assess property damage, particularly in remote or hard-to-reach areas.

Leveraging Big Data for Comprehensive Market Analysis

Insurance companies are using big data analytics more and more. This helps them understand market trends, customer behavior, and what their competitors are doing.

Competitive Analysis: Big data analytics help insurers track their competitors. This includes what products they offer and how they price them. This way, insurers can spot chances in the market.

Market Trend Prediction: By looking at large amounts of data, insurers can guess future market trends. This might be about new risks, what customers want, or changes in rules. With this knowledge, they can change their plans early.

New Product Development: Insights from big data can help create new insurance products. These products meet changing customer needs and include options like usage-based insurance, micro-insurance, and on-demand insurance.

Conclusion

In conclusion, data science is changing the insurance industry. It helps find fraud, improves how risks are assessed, and makes customer experiences better. With AI and machine learning, companies can create more personalized policies and do better market analysis. There are some challenges, like keeping data private and not having enough skilled workers. Still, the future of insurance will rely on using big data insights. By accepting data science ideas, the insurance sector will become more efficient and focused on the customer. It is important to stay updated, adjust to new technologies, and see how data science can transform how insurance is done.

[nathaniacolver]

weird studying hack for academic papers - time how long it takes you to get through an article.

i have to read 19 academic articles on the uses of machine learning to detect toxic language (fake news, hateful/offensive/abusive speech, etc.), and i SWEAR my productivity went up as soon as i needed myself to be ACCURATELY TIMED.

like yes i'm writing this post on the clock rn but for the past 3 hours, i'll be darned if i don't make sure that i ACCURATELY TIME MYSELF!!!! and it legit prompted me to work faster like what is this imaginary race i've made up

[fenway03]

Ah, the irony of you ending on this fake anti-capitalism note – right after spouting the most capitalist bullshit that could be spouted in this context.

You see, this quote of yours, ...

Artists have always copied each other, and now programmers copy artists.

… this is you being a capitalism bootlicker par excellence. And also, this is you being rather clueless about both art in general and the technology behind GenAI models in particular.

The process of one human artist "copying" or getting inspired by another involves (among other things) two key components: feeling and time.

The "feeling" component fuels this whole process, whereas the "time" component adds a sense of urgency and value.

An artist doesn't just randomly pick the color palette of another artist, the brushwork of a second, and the preferred motif of a third, then mashes it all together and ta-daah: new art!

Instead, when we draw inspiration from others while creating our own art, we "copy" those parts and styles of existing works that specifically speak to us, the parts that make us feel joy, sorrow, hope, despair, and everything in between. And the reason why we create our own art is because we want to share with the world how we feel. We're looking for a connection, for people who feel like us, for someone who gets us so we'll feel less alone.

A machine doesn't have any of those feelings. It doesn't understand the joy of devouring a strawberry sundae on a hot summer day, and it doesn't know the pain of seeing the light leave a loved one's eyes on a foggy November night. It doesn't need the company of another machine to feel whole. It simply doesn't feel.

And an AI art machine isn't constrained by time either. You could "freeze" its code at any particular moment, copy it to another set of hardware, and let it continue its work as if nothing happened. You could also copy this code to a hundred additional sets of hardware, and then you have a hundred machines performing the same work.

You can't do that with a human artist.

It takes time to learn a craft. It takes time to perfect it. It takes time to teach everything you know to those following in your footsteps so they can continue your work. And when you run out of time, you can't just take a snapshot of your brain, implant it into another body, and then live and create for another decade. That's why an original Warhol sells for $195 million, while a poster of the same piece printed in bulk sells for $19.50.

Feeling and time – that's what defines human art.

When "programmers copy artists", as you so ignorantly put it, none of the above process happens. In fact, those "programmers" don't really copy any artists at all. And they shouldn't even be called "programmers", because a key differentiator of GenAI is that it does not need a program in the traditional sense anymore. This is not how GenAI works.

In very, very simple terms, GenAI is based on a bunch of math and a shitton of hardware and data. When a model is being trained, it doesn't look at a Picasso and say "oh, that's an oddly interesting way to draw human faces!" Instead, the artwork gets analyzed pixel by pixel, first to detect very simple shapes and edges, then to detect certain combinations of shapes (e.g. those that look like an eye), then to detect even more complex shapes (e.g. two eyes, a nose, and a mouth that result in a face), and then even more complex shapes and relationships – until the model has detected and learned a specific combination of features that are often found in Picasso's art. And then you can let the model analyze an image it hasn't seen before, and it will tell you with a certain level of certainty whether or not that is a Picasso, too (i.e., whether the image contains all the patterns typically found in a Picasso). And that's also how you can let AI generate a picture that looks like a Picasso.

This whole process is just a lot of math (and computing power). There is no creativity. No feeling. No artist getting inspired by other artists while trying to express something meaningful about the world we live in.

Once the model has learned the typical patterns found in a Picasso, it can share this knowledge with another model – essentially within an instant –, and then the second model can identify and generate fake Picassos for you with the same level of certainty. It doesn't even need to learn anything anymore. [Again, this is a very simplified explanation.]

And this is what makes every capitalist's pulse soar: a "worker" that doesn't require costly training or years of experience, doesn't need sleep or breaks, won't get less productive with age, and could be cloned quite easily at low cost when the business needs to be scaled to meet growing demand.

"Art" generated by AI is a highly commodified business process. The only thing this kind of AI really creates is more money in a small number of capitalists' bank accounts. Because, as mentioned, it takes a shitton of hardware and infrastructure to train the foundational models. That's why the usual suspects (Google/Alphabet, Microsoft, etc.) dominate the field and can generate revenue every time some downstream developer uses the respective model for whatever AI application they want to build (from chatbots to image/video generators and so much more).

This also means (for now) that we should have a good laugh and walk away whenever someone argues along the lines of "but these new AI tools make art accessible to everyone; they remove the gatekeepers; now everybody can create whatever and whenever they want!" It could be argued that the opposite is true.

Now there are even more powerful gatekeepers. Filthy-rich millionaires and billionaires whose mass production tools make it even harder for artists to earn a living with their craft. And they have an army of bootlickers who run around spouting bullshit like "artists have always copied each other, and now programmers copy artists." Bootlickers who engage in victim-blaming and casually recommend that "you should unionize and demand that your labor is compensated fairly". (Gee, thanks, this has never been tried before and will surely be an easy solution.) Bootlickers who put the onus on those who can barely pay their bills instead of being mad at the rich assholes who exploit them. Bootlickers who pseudo-intelligently proclaim that "this is not a new phenomenon" while acting in a way that upholds the status quo.

The true grift here is not Glaze (regardless of its usefulness or lack thereof) – it's the "GenAI art is art" con that you have fallen for.

Since you're such a fan of AI, let's ask ChatGPT for advice:

You've already ticked all the boxes for sentence #1 and #2. Wake up before you'll become a case study for sentence #3.

the darling Glaze “anti-ai” watermarking system is a grift that stole code/violated GPL license (that the creator admits to). It uses the same exact technology as Stable Diffusion. It’s not going to protect you from LORAs (smaller models that imitate a certain style, character, or concept)

An invisible watermark is never going to work. “De-glazing” training images is as easy as running it through a denoising upscaler. If someone really wanted to make a LORA of your art, Glaze and Nightshade are not going to stop them.

If you really want to protect your art from being used as positive training data, use a proper, obnoxious watermark, with your username/website, with “do not use” plastered everywhere. Then, at the very least, it’ll be used as a negative training image instead (telling the model “don’t imitate this”).

There is never a guarantee your art hasn’t been scraped and used to train a model. Training sets aren’t commonly public. Once you share your art online, you don’t know every person who has seen it, saved it, or drawn inspiration from it. Similarly, you can’t name every influence and inspiration that has affected your art.

I suggest that anti-AI art people get used to the fact that sharing art means letting go of the fear of being copied. Nothing is truly original. Artists have always copied each other, and now programmers copy artists.

Capitalists, meanwhile, are excited that they can pay less for “less labor”. Automation and technology is an excuse to undermine and cheapen human labor—if you work in the entertainment industry, it’s adapt AI, quicken your workflow, or lose your job because you’re less productive. This is not a new phenomenon.

You should be mad at management. You should unionize and demand that your labor is compensated fairly.

[teemify]

Real Estate Revolution: 7 Powerful Ways AI Transforms the Industry!

From Guesswork to Intelligence: A Realtor’s Journey with AI in Real Estate

It was a humid Monday morning when Meera, a 32-year-old real estate agent in Kochi, stared at her spreadsheet of unsold properties. Despite her relentless effort — calls, site visits, newspaper listings — something felt outdated. Clients wanted more than square footage and location; they wanted personalization, insights, speed.

That’s when Meera decided to give Artificial Intelligence a shot.

The First Spark: Smarter Listings Her journey began with AI-enabled property portals. Instead of static listings, these platforms learned about the buyer. One client who initially searched for 2BHK apartments near schools was also shown gated villas near tech parks — because the AI recognized his browsing habits and income patterns. He booked a site visit the same day.

For the first time, Meera saw how AI could understand buyers better than they understood themselves — a game-changer in real estate.

Numbers That Spoke Volumes A week later, Meera used an AI-based pricing tool. She uploaded photos, property age, area, nearby amenities — and out came an estimated market value, rental potential, and price flexibility range. She was stunned.

Earlier, price negotiation felt like a gamble. Now, with machine learning models tracking market behavior, seasonality, and neighborhood demand, she could speak numbers with confidence. The AI wasn’t just helping her sell — it was helping her sell smart.

A Tireless Assistant Her next discovery was an AI-powered chatbot on her agency’s website. This digital assistant could answer 80% of the queries — location, EMI estimates, floor plans, virtual tours — while Meera focused on high-value clients.

When she closed her first deal fully online through that assistant, she didn’t feel replaced — she felt relieved.

It was clear: in the modern world of real estate, AI wasn’t about automation — it was about augmentation.

Beyond Sales: Helping Builders and Developers Meera soon found herself working with a local builder. “Where should we build next?” he asked. She turned to AI’s predictive analytics.

The software processed traffic trends, land prices, school zones, office expansion zones, and even social media buzz. Within days, it suggested three micro-locations ripe for growth.

For Meera, this wasn’t just a sale. It was real estate strategy at a whole new level.

Paperwork? Streamlined. Another problem: paperwork. KYC, lease drafts, buyer IDs — all of it piled up. AI document verification tools began scanning, flagging inconsistencies, and storing them securely. No more late-night proofreading or legal hiccups.

In real estate, where legal missteps can mean massive losses, this felt like having a lawyer and a filing clerk rolled into one.

Trust Built on AI What about fraud? Meera had lost a client two months back to a fake listing on a rival site. Now, AI fraud detection scanned listings for duplicate images, inconsistent metadata, and red flags in seller profiles. It brought a new sense of security — for both agents and clients.

In a field like real estate, trust is everything. And AI helped her build it.

The Bigger Picture Soon, Meera was onboarding NRI clients — people who couldn’t visit properties but wanted real estate investments in Kerala. AI tools helped her translate trends, localize insights, and provide virtual walkthroughs. Geography no longer limited her sales.

For deeper insight into how AI is transforming the future, click the link below:

Conclusion: A New Era Begins Today, Meera doesn’t chase leads with flyers or wait endlessly for callbacks. She lets Teemify’s intelligent agent handle the patterns — automating follow-ups, sorting qualified leads, and even scheduling property walkthroughs — while she focuses on building real relationships. Her revenue is up. Her hours are better. And her clients? More satisfied than ever.

AI didn’t change what she did in real estate. Teemify changed how she did it — intelligently, efficiently, and effortlessly.

[hurriedlyicyrebel]

AI in Fraud Detection: How Smart Technology Fights Financial Crime

Fraud is a growing threat in today’s digital world. As more people shop, bank, and do business online, fraudsters are getting smarter. But thankfully, so is technology. I’ve seen how AI in fraud detection is changing the game—spotting suspicious activity in real time and protecting businesses and customers before damage is done.

What Is AI in Fraud Detection?

AI in fraud detection uses machine learning, pattern recognition, and real-time data analysis to identify unusual behavior. It’s faster and more accurate than traditional systems because it learns over time, adapting to new threats automatically.

Example:

If a customer who always shops in London suddenly makes a purchase in Tokyo five minutes later, AI systems can instantly flag this as suspicious.

Why Is AI Better Than Traditional Methods?

Old fraud detection tools often rely on fixed rules, like: “Flag all transactions over $5,000.” But AI can go deeper.

Key Advantages:

Learns from patterns over time

Reduces false positives (blocking real customers)

Works in real-time

Detects complex fraud involving multiple accounts or steps

How Does AI Detect Fraud?

AI fraud detection usually involves:

Data Collection – Gathering transaction history, device info, location, etc.

Pattern Analysis – Using models to understand normal behavior

Anomaly Detection – Flagging anything that looks out of the ordinary

Risk Scoring – Assigning a threat level to each transaction

Action – Blocking, alerting, or requesting extra verification

Common Use Cases

IndustryAI ApplicationExampleBankingCredit card fraud, identity theftBlocking a hacked account in real timeE-commerceFake transactions, refund fraudDetecting bots or unusual ordersInsuranceClaims fraud detectionFlagging fake accident reportsTelecomSubscription and identity fraudCatching SIM card swapping

Tools and Technologies

Popular tools used for AI-based fraud detection include:

SAS Fraud Management

FICO Falcon Platform

IBM Trusteer

Kount

Darktrace

Amazon Fraud Detector

Most of these use advanced machine learning models, including decision trees, neural networks, and clustering algorithms.

Challenges in AI-Based Fraud Detection

Even with AI, there are a few things to keep in mind:

Data Quality: Poor data leads to poor results

Privacy Concerns: Sensitive customer data must be protected

Evolving Threats: Fraudsters adapt quickly, so models must be updated

Bias Risks: AI must be trained fairly to avoid unfair profiling

The Future of AI in Fraud Detection

AI is expected to become even more powerful through:

Deep Learning for better pattern recognition

Behavioral Biometrics (how you type or move your mouse)

Natural Language Processing to detect fake documents or conversations

Collaborative AI where companies share threat data securely

Final Thoughts

AI in fraud detection is not just a trend—it’s a necessity. With smarter tools, businesses can stay one step ahead of cybercriminals, reduce losses, and keep customers safe. As threats evolve, AI is proving to be the most reliable guard at the gate.

[feralcauldronvigilante]

Faceswap AI Generator: Revolutionizing Digital Content Creation

In the rapidly evolving world of artificial intelligence, Faceswap has emerged as one of the most fascinating and widely used applications. A Faceswap AI generator leverages deep learning algorithms to seamlessly replace one person’s face with another in images or videos. This technology, once considered a futuristic concept, is now accessible to millions, thanks to advancements in AI and machine learning.

From entertainment and social media to film production and digital marketing, Faceswap is transforming how we interact with visual content. But how does it work? What are its applications, and what ethical concerns does it raise? This article explores the mechanics, uses, and implications of Faceswap AI generators.

How Does a Faceswap AI Generator Work?

A Faceswap AI generator relies on deep neural networks, particularly Generative Adversarial Networks (GANs), to analyze and manipulate facial features. Here’s a simplified breakdown of the process:

Face Detection – The AI scans the input image or video frame to identify faces using landmark detection.

Feature Extraction – Key facial features (eyes, nose, mouth, jawline) are mapped to ensure accurate alignment.

Face Replacement – The source face is superimposed onto the target face while adjusting lighting, skin tone, and expressions for realism.

Blending & Refinement – The AI refines edges and blends textures to make the swap appear natural.

Popular Faceswap tools like DeepFaceLab, FaceSwap Live, and Reface use these techniques to produce high-quality results.

Applications of Faceswap AI Generators

1. Entertainment & Social Media

Faceswap is widely used for memes, viral videos, and filters on platforms like TikTok, Instagram, and Snapchat. Users can swap faces with celebrities, fictional characters, or friends, creating humorous and engaging content.

2. Film & Dubbing Industry

Movie studios use Faceswap for de-aging actors (e.g., in The Irishman) or replacing actors in post-production. It also aids in multilingual dubbing by syncing lip movements to different languages.

3. Digital Marketing & Advertising

Brands leverage Faceswap for personalized ads, allowing customers to "try on" products virtually (e.g., makeup, glasses). This boosts engagement and sales.

4. Education & Training

Medical students use Faceswap to simulate patient interactions, while corporate trainers create immersive role-playing scenarios.

5. Privacy Protection

Some applications anonymize individuals in videos by replacing faces, useful for whistleblowers or confidential interviews.

Ethical Concerns & Misuse of Faceswap Technology

Despite its benefits, Faceswap AI generators raise significant ethical and security issues:

Deepfake Misinformation – Malicious actors use Faceswap to create fake news, revenge porn, or fraudulent videos of public figures.

Identity Theft & Scams – Criminals can impersonate individuals for financial fraud or social engineering attacks.

Consent & Privacy Violations – Unauthorized face-swapping infringes on personal rights, leading to legal disputes.

Governments and tech companies are developing detection tools and regulations to combat misuse. Platforms like Facebook and Twitter now flag AI-generated content to prevent deception.

Future of Faceswap AI Generators

As AI improves, Faceswap technology will become even more realistic and accessible. Future developments may include:

Real-Time High-Quality Swapping – Instantaneous Faceswap in live streams or video calls.

Enhanced Customization – More control over facial expressions, age, and style.

Stronger Ethical Safeguards – Better authentication to prevent deepfake abuse.

The line between real and synthetic media will blur, making responsible usage crucial.

[knowinglyvoidwraith]

AI Video Model: The Future of Content Creation

The rapid advancement of artificial intelligence has revolutionized multiple industries, and video production is no exception. The emergence of AI video models has transformed how content is created, edited, and distributed. These models leverage deep learning algorithms to generate, enhance, and automate video production processes, making them faster, more efficient, and accessible to a broader audience.

In this article, we will explore the capabilities of AI video models, their applications, benefits, and the potential challenges they present.

What is an AI Video Model?

An AI video model is a machine learning system designed to understand, generate, and manipulate video content. These models are trained on vast datasets of video footage, allowing them to recognize patterns, predict sequences, and even create entirely new videos from scratch.

Key Technologies Behind AI Video Models

Generative Adversarial Networks (GANs) – Used for creating realistic video frames by pitting two neural networks against each other.

Transformers – Enable long-range sequence prediction, improving video coherence and continuity.

Neural Rendering – Enhances video quality by simulating real-world lighting and textures.

Diffusion Models – Generate high-quality video frames by progressively refining noise into structured images.

These technologies allow AI video models to perform tasks such as deepfake generation, automated video editing, and real-time video synthesis.

Applications of AI Video Models

1. Content Creation & Marketing

Brands and creators use AI video models to generate promotional videos, advertisements, and social media content without extensive manual editing. AI can analyze trends and produce optimized videos tailored to specific audiences.

2. Film & Entertainment

In the film industry, AI video models assist in special effects, scene restoration, and even virtual actor creation. Directors can preview scenes before shooting, reducing production costs.

3. Education & Training

AI-generated videos enhance e-learning by creating interactive tutorials, simulations, and personalized training materials. This makes complex subjects easier to understand through visual storytelling.

4. Gaming & Virtual Reality

Game developers use AI video models to generate dynamic in-game cutscenes and realistic NPC animations, improving immersion. VR environments also benefit from AI-generated real-time video enhancements.

5. Security & Surveillance

AI-powered video analysis improves surveillance systems by detecting anomalies, recognizing faces, and predicting potential threats in real time.

Benefits of Using AI Video Models

1. Cost & Time Efficiency

Traditional video production requires expensive equipment and lengthy editing processes. AI video models automate many tasks, reducing costs and accelerating workflows.

2. Scalability

Businesses can generate thousands of personalized videos for marketing campaigns without additional human effort.

3. Enhanced Creativity

AI tools provide new ways to experiment with visual effects, animations, and storytelling techniques that were previously time-consuming or impossible.

4. Accessibility

Small creators and startups can now produce high-quality videos without needing professional editing skills or large budgets.

Challenges & Ethical Concerns

Despite their advantages, AI video models raise several concerns:

1. Deepfakes & Misinformation

AI-generated videos can be used to create convincing fake content, leading to potential misuse in spreading false information or impersonating individuals.

2. Job Displacement

Automation in video production may reduce the need for human editors, animators, and other professionals in the industry.

3. Bias in Training Data

If AI models are trained on biased datasets, they may produce discriminatory or inaccurate content.

4. Legal & Copyright Issues

AI-generated videos often use existing footage for training, raising questions about intellectual property rights.

The Future of AI Video Models

As AI video models continue to evolve, we can expect:

Hyper-Realistic AI-Generated Films – Entire movies could be produced with minimal human intervention.

Interactive & Personalized Videos – Viewers may influence video narratives in real time.

Improved Ethical Safeguards – Detection tools and regulations will likely emerge to combat deepfake misuse.

[brandprotectionsonline]

Smart Trademark Watch Services to Remain Safe

Today’s world runs on the internet, which alone makes a brand’s identity its most important treasure. Brands are declaring their presence through trademarks that include names, logos, slogans, and even sounds that are unique to them. Notwithstanding, it's challenging to assert and manage the rights in such marks and at the same time avoid the risks of unauthorized use of identical or very similar marks as the global online economy is expanding so fast. 

The companies, however, use smart trademark watch services as a strategy to secure themselves and hardly ever fall victims to unauthorized use. In other words, they take control of brand protection and seek preventive measures. Nowadays, even the traditional word mark sounds are marked for protection. Furthermore, as the online market grows, trademarks are in danger of becoming counterfeit and thus, are the thefts. To counter these risks, brands have come up with smart trademark watch services which are a mechanism to ensure that the marks are observed, not used wrongly and lastly, if need be, appropriate measures are taken hierarchically beyond the national.

Purpose of Trademark Watch Services

A symbol is an instrument that inspires organization and consumer loyalty, and brands. It is a true fact that by the misuse of trademarks lots of people become victims of fraud. This content is available for those who want to read about the best ways to distinguish the best protected marks from fake ones. It provides graphic examples of how to detect logo poisoning.

WIPO has released its data suggesting that the intellectual property in the world is growing quite rapidly. New reports come in every year and the number of records always climbs up to new heights of which the data for 2016 reached its fever pitch at 3 million. From then on we have gradually increased the volume to 7.9 million records in 2017.

In addition to such marketing methods as flyers, brochures, catalog, and pamphlets, the companies are using direct marketing extensively. A very important part of these services is getting full reports including accurate rude language, swear words, adult content, and other issues, which can affect the brand anyway at all.

How Smart Trademark Watch Services Function

The smart trademark watch services as compared to the traditional methods involve the use of automation, artificial intelligence, and real-time data analysis tools. They are designed to simply monitor intellectual property (IP) assets that are scattered across digital and physical realms.

1. Automated Trademark Monitoring

A wide range of trademark databases such as the global registries like USPTO, EUIPO, WIPO, and local IP offices are being checked frequently. The alerts are fired off when there are identical or confusingly similar trademarks found.

Having this proactive surveillance, companies can easily start opposition procedures or contact the infringing parties even before the damage has been done.

2. AI-Powered Similarity Detection

Among the features of modern trademark watch tools are the matching algorithms which are AI-based that comprise but not limit to the word matches. In addition, machine learning is applied to analyze visual trademarks, phonetic similarities, and stylized logos.

The bywords of the set up are early warning, which implies that if possible risks emerge before they differ from the business malicious activities, the brand keeps safe.

3. Domain Name Monitoring

Domains are checked for which are getting registered close to the trademarks. The registration of the domains "brndname.com" and "yourbrand-shop.net" is the kind that can raise alert signals. This method has already allowed many cases of cyber squatters and fake web stores to be stopped.

4. Marketplace and Social Media Monitoring

Online marketplaces and social platforms are watched for unauthorized sellers or users who use the brand’s name or trademarks. The process of removing content can be automated through such systems.

Leading platforms like Amazon, Alibaba, eBay, Facebook, Instagram, and TikTok are professionally checked to detect and erase fake activity on a regular basis.

Benefits of Using Smart Trademark Watch Services

A trademark watch service revolves around the concept of vigilance in not only the technical sense but is also a shrewd investment in the brand's future and legal protection. The following are some of the main benefits of such services:

Early Detection of Trademark Infringement

In many legal systems, there is only a limited period for declaring one's opposition to similar trademarks. If a business misses this period, litigation is likely to be the only solution and it is always very expensive. Trademark watch services give an assurance that an infringement is recognized within a certain time interval and effective measures can be taken on time.

Protection Against Financial Losses

Marketplace infringements can lead to brand dilution and loss of sales, especially if copy products come into play. Using these services, businesses are able up to 70% faster fraud discovery and possible infringement cases from disreputable profiteers who deliberately mimic products and significantly devalue brands.

Reputation and Customer Trust Retention

Consumers who accidentally buy fake products may unintentionally be the reason for the wrong perception of the original brand and may spread it. This will cause damage to the brand reputation. The immediate termination of infringing activity will not only result in a loss of consumer confidence but it also helps mitigate by preventing the increase in the number of affected customers.

Global Coverage and Scalable Monitoring

Trademark watch solutions featuring AI and machine learning technologies are for the protection of the brand, no matter where the trademark is being used and in which language.

Who Are the Most Common Users of Trademark Watch Services?

Although every company with trademarks on the books can get advantages, there are some areas that are particularly exposed to the risks of trademark abuse:

Fashion and Luxury Goods – Counterfeits and fake listings are very common.

Pharmaceuticals – Unauthorized use can cause serious health risks to people.

Technology and Software – Apps, tools, and platforms are repeatedly being faked.

Consumer Electronics – Copycat accessories and devices are widespread in the world market.

FMCG and Beauty Brands - trademarks are being infringed on social media and packaging.

One trick is to persuade the startups and SMEs to subscribe to watch services to shield their emerging brand identities.

The Right Method of Identifying a Trademark Watch Partner

Many providers assure high-quality trademark protection services, yet the number that provides the same extent of monitoring, speed, and research is not the same. The following elements are important while picking a provider:

Access to a trademark database on the global market

AI-driven risk analysis and alerts

Marketplace and domain name monitoring

Custom reporting and analytics

Integration with legal IP teams

Companies like Cor search, MarkMonitor, Red Points, and Brand Shield are secure and in no doubt, they can efficiently handle large volumes of watch alerts.

Conclusion

In the current competitive status, passive defense is not sufficient. Brands need to take the first step to ensure their intellectual property and clients’ loyalty. Through smart trademark watch services, enterprises are becoming able to not only observe the threats but also to protect their rights, and even construct a more solid foundation for the entrepreneurship of future generations (Memory, Machauer, Garnevska, & Shim, 2020).

While digital risks are changing, the utilization of clever, large-scale and automated surveillance tools will be seen not only as a compliance requirement but also as an innovative strategy in the 21st century.

[hannahciara]

Why Smart Business Owners Are Replacing Their Money Counter Walmart Models with Professional Lynde Ordway Machines

Handling cash should speed up your business, not slow it down with jams, miscounts, or frequent replacements. While many small and mid-sized business owners turn to a money counter from Walmart for convenience, what they often miss is long-term reliability and professional-grade accuracy.

This is where Lynde Ordway cash counters set a new standard.

In this article, we’ll compare Walmart money counters with Lynde Ordway’s precision-built machines across speed, accuracy, durability, and business-ready features—helping you decide what’s truly best for your bottom line.

🔍 The Real Cost of a Money Counter from Walmart

Buying a money counter at Walmart can seem like a quick fix. After all, it’s affordable and easily available. But these budget machines often fall short when you need to:

Count large volumes of cash daily

Detect counterfeit bills accurately

Handle mixed denominations with precision

Avoid downtime from constant jams

For industrial businesses, casinos, retailers, and banks—accuracy and uptime matter. A few hundred dollars saved upfront can result in thousands lost to inefficiency or error.

✅ Why Lynde Ordway Cash Counters Outperform Walmart Models

1. Speed That Matches Real-World Demands

Walmart models typically process 600–900 bills/minute.

Lynde Ordway professional counters handle up to 1,200+ bills per minute with batch sorting, denomination recognition, and non-stop reliability.

2. Built-In Counterfeit Detection

Most budget counters use only UV light detection.

Lynde Ordway offers multi-layered counterfeit detection (UV, MG, IR, and size checks), ensuring maximum security for your business.

3. Commercial-Grade Durability

Walmart counters are made for occasional use.

Lynde Ordway’s machines are engineered for daily, high-volume environments, minimizing breakdowns and maintenance costs.

📊 Comparison Chart: Walmart Money Counter vs. Lynde Ordway Cash Counter

Feature

Money Counter Walmart

Lynde Ordway Cash Counter

Counting Speed

600–900 bills/min

1,200+ bills/min

Counterfeit Detection

UV only

UV, MG, IR, Size Detection

Mixed Denomination Handling

Basic/No

Yes

Durability (Daily Use)

Low

High

Warranty & Support

Limited

Full U.S.-based Support

Business Suitability

Low to Medium Volume

Medium to High Volume

🧠 Real Business Use Cases

Retail Chain Owner, Ohio:

“Our old Walmart money counter jammed every few days. Since switching to a Lynde Ordway cash counter, we save nearly 2 hours daily and haven’t had a single error in months.”

Vape Shop Manager, Texas:

“The built-in counterfeit detection paid for itself within weeks. It caught two fake $50s we’d have otherwise accepted.”

💡 Key Features That Matter to Industrial Business Owners

Advanced counterfeit detection – stay protected from fraud

Quiet operation – ideal for customer-facing counters

User-friendly LCD display – fast learning curve for staff

Batching & adding functions – improves cash drawer organization

High-capacity hopper – no need to reload constantly

🤔 FAQ: Should I Really Upgrade from My Walmart Counter?

Q: Isn’t a money counter from Walmart good enough for small businesses? A: It depends on your cash volume. If you process hundreds of bills daily or handle cash in different denominations, a professional money counter will save you time, prevent mistakes, and protect against counterfeit losses.

Q: What makes Lynde Ordway different from other professional brands? A: Lynde Ordway specializes in commercial-grade cash management. Their machines are used by financial institutions, retail chains, and industrial operations across the U.S. With top-tier support and durability, they outperform entry-level competitors in every way.

📈 Final Thoughts: Go Beyond Entry-Level. Invest in Performance.

If you're serious about cash handling efficiency, it's time to upgrade. While a money counter from Walmart may suit very occasional use, growing businesses require accuracy, speed, and durability that only a Lynde Ordway cash counter can provide.

👉 Ready to Upgrade?

Explore Lynde Ordway’s full line of professional money counters designed for U.S. businesses who value speed, security, and support.

[tccicomputercoaching]

Cybersecurity Threats to Watch Out For in 2025

The digital landscape is like a double-edged sword: offering new ways of connectivity and new vistas of innovation, while on the other side harboring a constantly mutating set of threats and increasingly complex attacks. As we approach 2025, we also see cybersecurity threats growing in complexity and reach. Thus, looking into these emerging threats should not merely be an interest for IT professionals, but something each person and organization involved in the online world should be aware of. 

In the hands of cybercriminals, new technologies like Artificial Intelligence (AI) and Machine Learning (ML) are used to launch increasingly sophisticated and hence difficult to resist attacks. Learning what the top security issues of 2025 will be is the first in laying out necessary defenses.

Why Vigilance is Crucial in 2025

AI-Powered Attacks: Threat actors are using AI to make phishing smarter, malware more evasive and brute-force attacks faster.

Expanded Attack Surface: More devices (IoT), cloud services, and remote work setups mean more entry points for cybercriminals.

Sophisticated Social-Engineering: Attacks are becoming highly personalized and convincing, thereby being harder to detect.

Data Is Gold: Both individual and corporate data remains the Lucifer for theft, extortion, and manipulation.Lucifer prime target

Here are the top Cybersecurity Threats to Watch Out For in 2025:

1. AI-Powered Phishing and Social Engineering

The generic scam emails will be a thing of the past. In 2025, AI will revolutionize extremely sophisticated and bespoke phishing campaigns. The vast data lakes will be churned by AI to create messages that resemble trusted contacts, sound more convincing, and adapt in real-time, creating an impasse for the human end users in separating legitimate from malicious.

What to do: Promote enhanced employee awareness through AI-based phishing simulation, employ strong email filters, and intensify the mantra of "verify, don't trust."

2. Evolving Ransomware 3.0 (Data Exfiltration & Double Extortion)

Ransomware isn't just about encrypting data anymore. Attackers will increasingly focus on exfiltrating sensitive data before encryption. This "double extortion" tactic means they demand payment not only to decrypt your data but also to prevent its public release or sale on the dark web.

What to do: Implement robust data backup and recovery plans (following the 3-2-1 rule), deploy advanced endpoint detection and response (EDR) solutions, and strengthen network segmentation.

3. Supply Chain Attacks on the Rise

Targeting a single, vulnerable link in a software or service supply chain allows attackers to compromise multiple organizations downstream. As seen with past major breaches, this method offers a high return on investment for cybercriminals, and their sophistication will only grow.

What to do: Implement stringent vendor risk management, conduct regular security audits of third-party suppliers, and ensure software integrity checks.

4. IoT and Edge Device Vulnerabilities

The proliferation of Internet of Things (IoT) devices (smart homes, industrial sensors, medical devices) creates a massive, often insecure, attack surface. Many IoT devices lack strong security features, making them easy targets for botnets, data theft, or even physical disruption.

What to do: Secure all IoT devices with strong, unique passwords, segment IoT networks, and ensure regular firmware updates. Implement strong network security protocols.

5. Deepfakes and AI-Generated Misinformation

Advancements in AI make it possible to create highly realistic fake audio, video, and images (deepfakes). These can be used for sophisticated spear-phishing attacks, corporate espionage, market manipulation, or even to spread widespread disinformation campaigns, eroding trust and causing financial damage.

What to do: Implement robust identity verification protocols, train employees to be highly skeptical of unsolicited requests (especially via video/audio calls), and rely on verified sources for information.

6. Cloud Security Misconfigurations

While cloud providers offer robust security, misconfigurations by users remain a leading cause of data breaches. As more data and applications migrate to the cloud, improperly configured storage buckets, identity and access management (IAM) policies, or network settings will continue to be prime targets.

What to do: Adopt cloud security best practices, implement continuous monitoring tools, and conduct regular audits of cloud configurations.

Fortifying Your Digital Defenses

So, putting in a multi-layer defense model would do in order to be an active response to those cybersecurity threats in 2025. From the perspective of the individual, this encompasses strong passwords, MFA, software updates on a regular basis, and a little basic cybersecurity awareness. Organizations, on the other hand, would look at investing in good security infrastructure, ongoing employee training, threat intelligence, and possibly, ethical hacking exercises.

Cybersecurity Training in Ahmedabad could be your next area of interest in order to keep updating yourself and your team on fighting the said contemporary threats. The future is digital; securing it is the prerogative of every individual.

Contact us

Location: Bopal & Iskcon-Ambli in Ahmedabad, Gujarat

Call now on +91 9825618292

Visit Our Website: http://tccicomputercoaching.com/

[jamtechtechnologies]

What Is the Difference Between AI and Generative AI

Artificial Intelligence (AI) is reshaping industries, powering everything from chatbots and voice assistants to fraud detection and self-driving cars. But in recent years, a powerful subfield of AI has gained momentum: Generative AI.

While both terms are often used interchangeably, there’s a clear distinction between AI and Generative AI in terms of function, purpose, and output.

In this article, we’ll explore what AI is, what Generative AI is, and the key differences between them, along with real-world examples.

What Is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a broad field of computer science focused on creating systems that can perform tasks that normally require human intelligence.

These tasks include:

Learning from data (Machine Learning)

Recognizing patterns (Computer Vision)

Understanding language (Natural Language Processing)

Making decisions (Expert Systems)

Examples of AI:

Google Maps using real-time traffic predictions

Siri or Alexa understanding voice commands

Netflix recommending movies based on viewing history

Spam filters in your email

What Is Generative AI?

Generative AI is a subset of AI that focuses on creating new content, such as text, images, code, music, and even video. Unlike traditional AI, which is designed to analyze or classify existing data, Generative AI learns from existing data to generate something new and original.

Examples of Generative AI:

ChatGPT generating human-like conversations

DALL·E creating images from text prompts

GitHub Copilot writing programming code

Runway or Sora by OpenAI generating video content

Key Differences Between AI and Generative AI

Feature

AI (Artificial Intelligence)

Generative AI

Definition

Broad field of simulating human intelligence

Subfield focused on creating new content

Goal

Automate decision-making, classification, tasks

Generate text, images, music, or code

Examples

Fraud detection, recommendation engines, search

ChatGPT, DALL·E, Bard, Claude

Output Type

Predictions, classifications, decisions

Creative or synthetic content

Learning Type

Supervised or reinforcement learning

Often uses unsupervised or transformer-based learning

Interaction Style

Analyzes and reacts to input

Responds and generates novel outputs

How Are They Connected?

Generative AI is a subset of AI. Think of AI as the umbrella, and Generative AI as a specialized branch under it.

While all Generative AI is AI, not all AI is generative.

AI = Make decisions, predictions, analyze

Generative AI = Create new data, content, or responses

Real-World Applications

AI in Business:

Chatbots for customer service

Predictive analytics in marketing

Fraud detection in finance

Personalized shopping experiences

Generative AI in Business:

Writing marketing copy

Creating social media graphics

Generating product descriptions

Assisting developers with code generation

Is Generative AI More Risky?

Generative AI comes with unique challenges such as:

Misinformation (fake news, deepfakes)

Bias and hallucination in generated content

Copyright concerns (generated images, music)

However, ethical frameworks and safety tools are being developed to ensure responsible use of Generative AI.

Conclusion

So, what is the difference between AI and Generative AI?

AI helps machines think, act, and make decisions like humans.

Generative AI helps machines create like humans—writing text, generating art, or composing music.

Both are revolutionizing how we work, live, and create—but Generative AI is taking automation to a new level by blending creativity with computation.