#neural network datasets

[d0nutzgg]

Tonight I am hunting down venomous and nonvenomous snake pictures that are under the creative commons of specific breeds in order to create one of the most advanced, in depth datasets of different venomous and nonvenomous snakes as well as a test set that will include snakes from both sides of all species. I love snakes a lot and really, all reptiles. It is definitely tedious work, as I have to make sure each picture is cleared before I can use it (ethically), but I am making a lot of progress! I have species such as the King Cobra, Inland Taipan, and Eyelash Pit Viper among just a few! Wikimedia Commons has been a huge help!

I'm super excited.

Hope your nights are going good. I am still not feeling good but jamming + virtual snake hunting is keeping me busy!

[sapientsapiens]

Finalized on a dataset for the 1st Capstone project at #mlzoomcamp led by Alexey Grigorev @DataTalksClub .

[river-taxbird]

There is no such thing as AI.

How to help the non technical and less online people in your life navigate the latest techbro grift.

I've seen other people say stuff to this effect but it's worth reiterating. Today in class, my professor was talking about a news article where a celebrity's likeness was used in an ai image without their permission. Then she mentioned a guest lecture about how AI is going to help finance professionals. Then I pointed out, those two things aren't really related.

The term AI is being used to obfuscate details about multiple semi-related technologies.

Traditionally in sci-fi, AI means artificial general intelligence like Data from star trek, or the terminator. This, I shouldn't need to say, doesn't exist. Techbros use the term AI to trick investors into funding their projects. It's largely a grift.

What is the term AI being used to obfuscate?

If you want to help the less online and less tech literate people in your life navigate the hype around AI, the best way to do it is to encourage them to change their language around AI topics.

By calling these technologies what they really are, and encouraging the people around us to know the real names, we can help lift the veil, kill the hype, and keep people safe from scams. Here are some starting points, which I am just pulling from Wikipedia. I'd highly encourage you to do your own research.

Machine learning (ML): is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without needing to be explicitly told what to do by any human-developed algorithms. (This is the basis of most technologically people call AI)

Language model: (LM or LLM) is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. (This would be your ChatGPT.)

Generative adversarial network (GAN): is a class of machine learning framework and a prominent framework for approaching generative AI. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. (This is the source of some AI images and deepfakes.)

Diffusion Models: Models that generate the probability distribution of a given dataset. In image generation, a neural network is trained to denoise images with added gaussian noise by learning to remove the noise. After the training is complete, it can then be used for image generation by starting with a random noise image and denoise that. (This is the more common technology behind AI images, including Dall-E and Stable Diffusion. I added this one to the post after as it was brought to my attention it is now more common than GANs.)

I know these terms are more technical, but they are also more accurate, and they can easily be explained in a way non-technical people can understand. The grifters are using language to give this technology its power, so we can use language to take it's power away and let people see it for what it really is.

[reachartwork]

how neural networks work: there's 94 seconds of audio coded inside this 2kb image but you've had to been trained on the right dataset to hear it (source)

[shirecorn]

so I binged all of your stuff I could find about your Skyscraper gods lore, and I'm curious - how, if at all, do creatures like changelings, the sirens/the other realm in general, and Discord fit into your AU?

Discord is actually from another universe! He is not a giant god thing, but he came to equestria and decided he liked it here and he could really mess this place up. After being imprisoned in stone for a thousand years and witnessing the civilization from a town square (I forget where he was put tbh) he became both super angry and somewhat paradoxically attached to this reality. When he was unstoned he got super vengeful. But then meeting fluttershy and befriending her helped him become even more attached to this place. It's not his home, though, and he only comes around to help save it because his friends are here.

Also he does not have a set appearance and shifts his shape with every step and gesture. Its a bit dizzying to watch, like the early examples of neural network stuff from 2015. Like you're looking at a tiger through an "AI" (I HATE that we call this BS ai, we should go back to saying neural net and machine learning, or better yet Machine Bias Training) anyway looking at a tiger but through a filter of an AI that has been trained to see horses everywhere. And by the time the filter has switched to a dataset of tigers, his actual form has turned into a snake.

This is my vision and I have done like. 10 drawings in order to capture it but the video editing to get there is beyond my current skillset so I keep procrastinating on starting

---

Changelings are insect-related creatures and are as far from mammals as you can get. I need to do a couple more drawings before I'm satisfied because I came up with a new concept after the first art, and just need to draw it.

---

is the other realm equestria girls? I dont watch EQ so I dont know how to fit it. I know there are sirens in a flashback in the pony show, in uhh Season 7 episode 25 or 26

[argumate]

so if you create a frickin' enormous neural network with randomised weights and then train it with gradient descent on a frickin' enormous dataset then aren't you essentially doing evolution on a population of random functions, genetic programming style?

except of course the functions can hook into each other, so it's more sophisticated than your average genetic programming arrangement...

[edutainer2022]

I'm tinkering again with the idea of the Tracies making the life of Counterterrorism/Special Ops difficult. This bit is probably more pertinent to UNREQUITED. Tempers fly. More context could be found, but is not strictly necessary, in PERSON OF INTEREST TOO and COUP DE GRÂCE. All the thanks to @janetm74 for bearing with me.

DA 18*

"So, given the circumstances, I have to INSIST on protective custody."

*baffled murmur all around*

"With the GDF?! What do you mean?!"

"I mean, MISTER Tracy, that you need to close operations and sit tight, till we catch him."

"ABSOLUTELY NOT!"

"Scott, wait! If the threat is legit..."

"I just spent half an hour briefing you how legit it is, Ms. Kyrano!"

"If the threat is legit we can invoke the lock down of the Tracy Island and take it from there."

"I'm afraid, none of the Tracy properties hold up to my team's security standards."

"EXCUSE ME!"

*indignant murmur all around*

"Well, let's see - your secure venues and systems have been breached and overtaken on multiple occasions by an international crime ring leader Belah Gaat, his know accomplice, wanted for acts of terrorism, the Mechanic, and a Cognitive Transformer Model, aka Eos, which, in turn, had been compromised on at least two recorded occasions. Not exactly a stellar track record of security protocols."

"How do you know about Eos?!"

"Dr. Simpson?"

"The neural network, incorporating elements of an open source code Dr. Tracy left behind on Harvard servers a while ago, has been trained on curated datasets, before escaping containment. The model has been accessing data and training in unsupervised conditions ever since. Last known hosting - Thunderbird Five server."

"Thank you! But I'm still more worried about breaches in perimeter on the Island and Grand Roca ranch."

*blue glare* *jade glare*

"You DO NOT suggest there's a mole in the IR!!!"

"Now, now! I'm sure Captain here makes no such allegations."

"No, Colonel Casey. I'd still rather be safe than sorry."

"Do you stipulate my security protocols are insufficient?"

"Let's see... No extra recon, Ms. Kyrano: how many assault points are in this room?"

"Door, windows, ventilation, possibly the holo drive or carry on devices."

"Very good. But not good enough."

*blue glare* *jade glare*

"Of those present, at least Colonel Casey and myself are carrying firearms."

*gasps*

"Everyone in this room has been extensively vetted!"

"You see, Ms. Kyrano, the problem is you keep thinking like a bodyguard. To protect Mister Tracy, you need to start thinking like an assassin."

"I'm sitting right here!"

"No. You're not. I just shot you point blank."

*toppled chair clanking* *glare off*

"This is ridiculous! I will NOT shut down IR and cower just because there's possibly a psycho from Bereznik on the loose!"

*blue glare down*

"Caramba, Scott! Stop being so obtuse AGAIN and LISTEN! He doesn't want your money, he doesn't want your tech, he doesn't give a damn about your Thunderbirds, he doesn't want to make a statement about your father's legacy! He wants you to suffer and he wants you GONE! And he will get to you, by any means necessary. By ANY means! Do you think he'd care how many brothers he'd have to eliminate first?!"

*glare around*

*heavy breathing*

*hard swallow*

"What do you suggest?"

"Obrigada mãe Maria! For starters, let's get going! Right now I'm the only person I trust with your life."

*jade glare*

"Well, I DON'T!"

"Good, Ms. Kyrano! Now you're really learning something."

----

* highest level of Danger Assessment

[neurospring]

History and Basics of Language Models: How Transformers Changed AI Forever - and Led to Neuro-sama

I have seen a lot of misunderstandings and myths about Neuro-sama's language model. I have decided to write a short post, going into the history of and current state of large language models and providing some explanation about how they work, and how Neuro-sama works! To begin, let's start with some history.

Before the beginning

Before the language models we are used to today, models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks) were used for natural language processing, but they had a lot of limitations. Both of these architectures process words sequentially, meaning they read text one word at a time in order. This made them struggle with long sentences, they could almost forget the beginning by the time they reach the end.

Another major limitation was computational efficiency. Since RNNs and LSTMs process text one step at a time, they can't take full advantage of modern parallel computing harware like GPUs. All these fundamental limitations mean that these models could never be nearly as smart as today's models.

The beginning of modern language models

In 2017, a paper titled "Attention is All You Need" introduced the transformer architecture. It was received positively for its innovation, but no one truly knew just how important it is going to be. This paper is what made modern language models possible.

The transformer's key innovation was the attention mechanism, which allows the model to focus on the most relevant parts of a text. Instead of processing words sequentially, transformers process all words at once, capturing relationships between words no matter how far apart they are in the text. This change made models faster, and better at understanding context.

The full potential of transformers became clearer over the next few years as researchers scaled them up.

The Scale of Modern Language Models

A major factor in an LLM's performance is the number of parameters - which are like the model's "neurons" that store learned information. The more parameters, the more powerful the model can be. The first GPT (generative pre-trained transformer) model, GPT-1, was released in 2018 and had 117 million parameters. It was small and not very capable - but a good proof of concept. GPT-2 (2019) had 1.5 billion parameters - which was a huge leap in quality, but it was still really dumb compared to the models we are used to today. GPT-3 (2020) had 175 billion parameters, and it was really the first model that felt actually kinda smart. This model required 4.6 million dollars for training, in compute expenses alone.

Recently, models have become more efficient: smaller models can achieve similar performance to bigger models from the past. This efficiency means that smarter and smarter models can run on consumer hardware. However, training costs still remain high.

How Are Language Models Trained?

Pre-training: The model is trained on a massive dataset to predict the next token. A token is a piece of text a language model can process, it can be a word, word fragment, or character. Even training relatively small models with a few billion parameters requires terabytes of training data, and a lot of computational resources which cost millions of dollars.

Fine-tuning: After pre-training, the model can be customized for specific tasks, like answering questions, writing code, casual conversation, etc. Fine-tuning can also help improve the model's alignment with certain values or update its knowledge of specific domains. Fine-tuning requires far less data and computational power compared to pre-training.

The Cost of Training Large Language Models

Pre-training models over a certain size requires vast amounts of computational power and high-quality data. While advancements in efficiency have made it possible to get better performance with smaller models, models can still require millions of dollars to train, even if they have far fewer parameters than GPT-3.

The Rise of Open-Source Language Models

Many language models are closed-source, you can't download or run them locally. For example ChatGPT models from OpenAI and Claude models from Anthropic are all closed-source.

However, some companies release a number of their models as open-source, allowing anyone to download, run, and modify them.

While the larger models can not be run on consumer hardware, smaller open-source models can be used on high-end consumer PCs.

An advantage of smaller models is that they have lower latency, meaning they can generate responses much faster. They are not as powerful as the largest closed-source models, but their accessibility and speed make them highly useful for some applications.

So What is Neuro-sama?

Basically no details are shared about the model by Vedal, and I will only share what can be confidently concluded and only information that wouldn't reveal any sort of "trade secret". What can be known is that Neuro-sama would not exist without open-source large language models. Vedal can't train a model from scratch, but what Vedal can do - and can be confidently assumed he did do - is fine-tune an open-source model. Fine-tuning a model on additional data can change the way the model acts and can add some new knowledge - however, the core intelligence of Neuro-sama comes from the base model she was built on. Since huge models can't be run on consumer hardware and would be prohibitively expensive to run through API, we can also say that Neuro-sama is a smaller model - which has the disadvantage of being less powerful, having more limitations, but has the advantage of low latency. Latency and cost are always going to pose some pretty strict limitations, but because LLMs just keep geting more efficient and better hardware is becoming more available, Neuro can be expected to become smarter and smarter in the future. To end, I have to at least mention that Neuro-sama is more than just her language model, though we only talked about the language model in this post. She can be looked at as a system of different parts. Her TTS, her VTuber avatar, her vision model, her long-term memory, even her Minecraft AI, and so on, all come together to make Neuro-sama.

Wrapping up - Thanks for Reading!

This post was meant to provide a brief introduction to language models, covering some history and explaining how Neuro-sama can work. Of course, this post is just scratching the surface, but hopefully it gave you a clearer understanding about how language models function and their history!

[chryza]

Idk tho I kinda liked AI art as an artist like I wish there was a generator from a clean dataset because it was fun to get ideas and things that I would draw or incorporate into other ideas for the fun of it. It was hardly a replacement for the actual work of art or writing but as an experimental step towards the future of technology as it relates to art I find it fascinating. Especially once you start looking into the really experimental stuff like people generating faces while telling the computer to slowly subtract faces from its input and watching the computer produce an idea of a human without the correct parameters. Highlighting why the program kept going back to the same features over and over despite being given vastly different prompts. I was truly so excited for the future of neural network artwork and it’s all been just….destroyed.

[biopractify]

How AI is Being Used to Predict Diseases from Genomic Data

Introduction

Ever wonder if science fiction got one thing right about the future of healthcare? Turns out, it might be the idea that computers will one day predict diseases before they strike. Thanks to Artificial Intelligence (AI) and genomics, we’re well on our way to making that a reality. From decoding the human genome at lightning speeds to spotting hidden disease patterns that even experts can’t see, AI-powered genomics is revolutionizing preventative care.

This article explores how AI is applied to genomic data, why it matters for the future of medicine, and what breakthroughs are on the horizon. Whether you’re a tech enthusiast, a healthcare professional, or simply curious about the potential of your own DNA, keep reading to find out how AI is rewriting the rules for disease prediction.

1. The Genomic Data Boom

In 2003, scientists completed the Human Genome Project, mapping out 3.2 billion base pairs in our DNA. Since then, genomic sequencing has become faster and more affordable, creating a flood of genetic data. However, sifting through that data by hand to predict diseases is nearly impossible. Enter machine learning—a key subset of AI that excels at identifying patterns in massive, complex datasets.

Why It Matters:

Reduced analysis time: Machine learning algorithms can sort through billions of base pairs in a fraction of the time it would take humans.

Actionable insights: Pinpointing which genes are associated with certain illnesses can lead to early diagnoses and personalized treatments.

2. AI’s Role in Early Disease Detection

Cancer: Imagine detecting cancerous changes in cells before a single tumor forms. By analyzing subtle genomic variants, AI can flag the earliest indicators of diseases such as breast, lung, or prostate cancer. Neurodegenerative Disorders: Alzheimer’s and Parkinson’s often remain undiagnosed until noticeable symptoms appear. AI tools scour genetic data to highlight risk factors and potentially allow for interventions years before traditional symptom-based diagnoses. Rare Diseases: Genetic disorders like Cystic Fibrosis or Huntington’s disease can be complex to diagnose. AI helps identify critical gene mutations, speeding up the path to diagnosis and paving the way for more targeted treatments.

Real-World Impact:

A patient’s entire genomic sequence is analyzed alongside millions of others, spotting tiny “red flags” for diseases.

Doctors can then focus on prevention: lifestyle changes, close monitoring, or early intervention.

3. The Magic of Machine Learning in Genomics

Supervised Learning: Models are fed labeled data—genomic profiles of patients who have certain diseases and those who do not. The AI learns patterns in the DNA that correlate with the disease.

Unsupervised Learning: This is where AI digs into unlabeled data, discovering hidden clusters and relationships. This can reveal brand-new biomarkers or gene mutations nobody suspected were relevant.

Deep Learning: Think of this as AI with “layers”—neural networks that continuously refine their understanding of gene sequences. They’re especially good at pinpointing complex, non-obvious patterns.

4. Personalized Medicine: The Future is Now

We often talk about “one-size-fits-all” medicine, but that approach ignores unique differences in our genes. Precision Medicine flips that on its head by tailoring treatments to your genetic profile, making therapies more effective and reducing side effects. By identifying which treatments you’re likely to respond to, AI can save time, money, and—most importantly—lives.

Pharmacogenomics (the study of how genes affect a person’s response to drugs) is one area booming with potential. Predictive AI models can identify drug-gene interactions, guiding doctors to prescribe the right medication at the right dose the first time.

5. Breaking Down Barriers and Ethical Considerations

1. Data Privacy

Genomic data is incredibly personal. AI companies and healthcare providers must ensure compliance with regulations like HIPAA and GDPR to keep that data safe.

2. Algorithmic Bias

AI is only as good as the data it trains on. Lack of diversity in genomic datasets can lead to inaccuracies or inequalities in healthcare outcomes.

3. Cost and Accessibility

While the price of DNA sequencing has dropped significantly, integrating AI-driven genomic testing into mainstream healthcare systems still faces cost and infrastructure challenges.

6. What’s Next?

Realtime Genomic Tracking: We can imagine a future where your genome is part of your regular health check-up—analyzed continuously by AI to catch new mutations as they develop.

Wider Disease Scope: AI’s role will likely expand beyond predicting just one or two types of conditions. Cardiovascular diseases, autoimmune disorders, and metabolic syndromes are all on the list of potential AI breakthroughs.

Collaborative Ecosystems: Tech giants, pharmaceutical companies, and healthcare providers are increasingly partnering to pool resources and data, accelerating the path to life-changing genomic discoveries.

7. Why You Should Care

This isn’t just about futuristic research; it’s a glimpse of tomorrow’s medicine. The more we rely on AI for genomic analysis, the more proactive we can be about our health. From drastically reducing the time to diagnose rare diseases to providing tailor-made treatments for common ones, AI is reshaping how we prevent and treat illnesses on a global scale.

Final Thoughts: Shaping the Future of Genomic Healthcare

AI’s impact on disease prediction through genomic data isn’t just a high-tech novelty—it’s a turning point in how we approach healthcare. Early detection, faster diagnosis, personalized treatment—these are no longer mere dreams but tangible realities thanks to the synergy of big data and cutting-edge machine learning.

As we address challenges like data privacy and algorithmic bias, one thing’s certain: the future of healthcare will be defined by how well we harness the power of our own genetic codes. If you’re as excited as we are about this transformative journey, share this post, spark discussions, and help spread the word about the life-changing possibilities of AI-driven genomics.

[jcmarchi]

Hyperrealistic Deepfakes: A Growing Threat to Truth and Reality

New Post has been published on https://thedigitalinsider.com/hyperrealistic-deepfakes-a-growing-threat-to-truth-and-reality/

Hyperrealistic Deepfakes: A Growing Threat to Truth and Reality

In an era where technology evolves at an exceptionally fast pace, deepfakes have emerged as a controversial and potentially dangerous innovation. These hyperrealistic digital forgeries, created using advanced Artificial Intelligence (AI) techniques like Generative Adversarial Networks (GANs), can mimic real-life appearances and movements with supernatural accuracy.

Initially, deepfakes were a niche application, but they have quickly gained prominence, blurring the lines between reality and fiction. While the entertainment industry uses deepfakes for visual effects and creative storytelling, the darker implications are alarming. Hyperrealistic deepfakes can undermine the integrity of information, erode public trust, and disrupt social and political systems. They are gradually becoming tools to spread misinformation, manipulate political outcomes, and damage personal reputations.

The Origins and Evolution of Deepfakes

Deepfakes utilize advanced AI techniques to create incredibly realistic and convincing digital forgeries. These techniques involve training neural networks on large datasets of images and videos, enabling them to generate synthetic media that closely mimics real-life appearances and movements. The advent of GANs in 2014 marked a significant milestone, allowing the creation of more sophisticated and hyperrealistic deepfakes.

GANs consist of two neural networks, the generator and the discriminator, working in tandem. The generator creates fake images while the discriminator attempts to distinguish between real and fake images. Through this adversarial process, both networks improve, leading to the creation of highly realistic synthetic media.

Recent advancements in machine learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have further enhanced the realism of deepfakes. These advancements allow for better temporal coherence, meaning synthesized videos are smoother and more consistent over time.

The spike in deepfake quality is primarily due to advancements in AI algorithms, more extensive training datasets, and increased computational power. Deepfakes can now replicate not just facial features and expressions but also minute details like skin texture, eye movements, and subtle gestures. The availability of vast amounts of high-resolution data, coupled with powerful GPUs and cloud computing, has also accelerated the development of hyperrealistic deepfakes.

The Dual-Edged Sword of Technology

While the technology behind deepfakes has legitimate and beneficial applications in entertainment, education, and even medicine, its potential for misuse is alarming. Hyperrealistic deepfakes can be weaponized in several ways, including political manipulation, misinformation, cybersecurity threats, and reputation damage.

For instance, deepfakes can create false statements or actions by public figures, potentially influencing elections and undermining democratic processes. They can also spread misinformation, making it nearly impossible to distinguish between genuine and fake content. Deepfakes can bypass security systems that rely on biometric data, posing a significant threat to personal and organizational security. Additionally, individuals and organizations can suffer immense harm from deepfakes that depict them in compromising or defamatory situations.

Real-World Impact and Psychological Consequences

Several high-profile cases have demonstrated the potential for harm from hyperrealistic deepfakes. The deepfake video created by filmmaker Jordan Peele and released by BuzzFeed showed former President Barack Obama appearing to say derogatory remarks about Donald Trump. This video was created to raise awareness about the potential dangers of deepfakes and how they can be used to spread disinformation.

Likewise, another deepfake video depicted Mark Zuckerberg boasting about having control over users’ data, suggesting a scenario where data control translates to power. This video, created as part of an art installation, was intended to critique the power held by tech giants.

Similarly, the Nancy Pelosi video in 2019, though not a deepfake, points out how easy it is to spread misleading content and the potential consequences. In 2021, a series of deepfake videos featuring actor Tom Cruise went viral on TikTok, demonstrating the power of hyperrealistic deepfakes to capture public attention and go viral. These cases illustrate the psychological and societal implications of deepfakes, including the erosion of trust in digital media and the potential for increased polarization and conflict.

Psychological and Societal Implications

Beyond the immediate threats to individuals and institutions, hyperrealistic deepfakes have broader psychological and societal implications. The erosion of trust in digital media can lead to a phenomenon known as the “liar’s dividend,” where the mere possibility of content being fake can be used to dismiss genuine evidence.

As deepfakes become more prevalent, public trust in media sources may diminish. People may become skeptical of all digital content, undermining the credibility of legitimate news organizations. This distrust can aggravate societal divisions and polarize communities. When people cannot agree on basic facts, constructive dialogue and problem-solving become increasingly difficult.

In addition, misinformation and fake news, amplified by deepfakes, can deepen existing societal rifts, leading to increased polarization and conflict. This can make it harder for communities to come together and address shared challenges.

Legal and Ethical Challenges

The rise of hyperrealistic deepfakes presents new challenges for legal systems worldwide. Legislators and law enforcement agencies must make efforts to define and regulate digital forgeries, balancing the need for security with the protection of free speech and privacy rights.

Making effective legislation to combat deepfakes is complex. Laws must be precise enough to target malicious actors without hindering innovation or infringing on free speech. This requires careful consideration and collaboration among legal experts, technologists, and policymakers. For instance, the United States passed the DEEPFAKES Accountability Act, making it illegal to create or distribute deepfakes without disclosing their artificial nature. Similarly, several other countries, such as China and the European Union, are coming up with strict and comprehensive AI regulations to avoid problems.

Combating the Deepfake Threat

Addressing the threat of hyperrealistic deepfakes requires a multifaceted approach involving technological, legal, and societal measures.

Technological solutions include detection algorithms that can identify deepfakes by analyzing inconsistencies in lighting, shadows, and facial movements, digital watermarking to verify the authenticity of media, and blockchain technology to provide a decentralized and immutable record of media provenance.

Legal and regulatory measures include passing laws to address the creation and distribution of deepfakes and establishing dedicated regulatory bodies to monitor and respond to deepfake-related incidents.

Societal and educational initiatives include media literacy programs to help individuals critically evaluate content and public awareness campaigns to inform citizens about deepfakes. Moreover, collaboration among governments, tech companies, academia, and civil society is essential to combat the deepfake threat effectively.

The Bottom Line

Hyperrealistic deepfakes pose a significant threat to our perception of truth and reality. While they offer exciting possibilities in entertainment and education, their potential for misuse is alarming. To combat this threat, a multifaceted approach involving advanced detection technologies, robust legal frameworks, and comprehensive public awareness is essential.

By encouraging collaboration among technologists, policymakers, and society, we can mitigate the risks and preserve the integrity of information in the digital age. It is a collective effort to ensure that innovation does not come at the cost of trust and truth.

[mariacallous]

How will AI be used in health care settings?

Artificial intelligence (AI) shows tremendous promise for applications in health care. Tools such as machine learning algorithms, artificial neural networks, and generative AI (e.g., Large Language Models) have the potential to aid with tasks such as diagnosis, treatment planning, and resource management. Advocates have suggested that these tools could benefit large numbers of people by increasing access to health care services (especially for populations that are currently underserved), reducing costs, and improving quality of care.

This enthusiasm has driven the burgeoning development and trial application of AI in health care by some of the largest players in the tech industry. To give just two examples, Google Research has been rapidly testing and improving upon its “Med-PaLM” tool, and NVIDIA recently announced a partnership with Hippocratic AI that aims to deploy virtual health care assistants for a variety of tasks to address a current shortfall in the supply in the workforce.

What are some challenges or potential negative consequences to using AI in health care?

Technology adoption can happen rapidly, exponentially going from prototypes used by a small number of researchers to products affecting the lives of millions or even billions of people. Given the significant impact health care system changes could have on Americans’ health as well as on the U.S. economy, it is essential to preemptively identify potential pitfalls before scaleup takes place and carefully consider policy actions that can address them.

One area of concern arises from the recognition that the ultimate impact of AI on health outcomes will be shaped not only by the sophistication of the technological tools themselves but also by external “human factors.” Broadly speaking, human factors could blunt the positive impacts of AI tools in health care—or even introduce unintended, negative consequences—in two ways:

If developers train AI tools with data that don’t sufficiently mirror diversity in the populations in which they will be deployed. Even tools that are effective in the aggregate could create disparate outcomes. For example, if the datasets used to train AI have gaps, they can cause AI to provide responses that are lower quality for some users and situations. This might lead to the tool systematically providing less accurate recommendations for some groups of users or experiencing “catastrophic failures” more frequently for some groups, such as failure to identify symptoms in time for effective treatment or even recommending courses of treatment that could result in harm.  

If patterns of AI use systematically differ across groups. There may be an initial skepticism among many potential users to trust AI for consequential decisions that affect their health. Attitudes may differ within the population based on attributes such as age and familiarity with technology, which could affect who uses AI tools, understands and interprets the AI’s output, and adheres to treatment recommendations. Further, people’s impressions of AI health care tools will be shaped over time based on their own experiences and what they learn from others.

In recent research, we used simulation modeling to study a large range of different of hypothetical populations of users and AI health care tool specifications. We found that social conditions such as initial attitudes toward AI tools within a population and how people change their attitudes over time can potentially:

Lead to a modestly accurate AI tool having a negative impact on population health. This can occur because people’s experiences with an AI tool may be filtered through their expectations and then shared with others. For example, if an AI tool’s capabilities are objectively positive—in expectation, the AI won’t give recommendations that are harmful or completely ineffective—but sufficiently lower than expectations, users who are disappointed will lose trust in the tool. This could make them less likely to seek future treatment or adhere to recommendations if they do and lead them to pass along negative perceptions of the tool to friends, family, and others with whom they interact.

Create health disparities even after the introduction of a high-performing and unbiased AI tool (i.e., that performs equally well for all users). Specifically, when there are initial differences between groups within the population in their trust of AI-based health care—for example because of one group’s systematically negative previous experiences with health care or due to the AI tool being poorly communicated to one group—differential use patterns alone can translate into meaningful differences in health patterns across groups. These use patterns can also exacerbate differential effects on health across groups when AI training deficiencies cause a tool to provide better quality recommendations for some users than others.

Barriers to positive health impacts associated with systematic and shifting use patterns are largely beyond individual developers’ direct control but can be overcome with strategically designed policies and practices.

What could a regulatory framework for AI in health care look like?

Disregarding how human factors intersect with AI-powered health care tools can create outcomes that are costly in terms of life, health, and resources. There is also the potential that without careful oversight and forethought, AI tools can maintain or exacerbate existing health disparities or even introduce new ones. Guarding against negative consequences will require specific policies and ongoing, coordinated action that goes beyond the usual scope of individual product development. Based on our research, we suggest that any regulatory framework for AI in health care should accomplish three aims:

Ensure that AI tools are rigorously tested before they are made fully available to the public and are subject to regular scrutiny afterward. Those developing AI tools for use in health care should carefully consider whether the training data are matched to the tasks that the tools will perform and representative of the full population of eventual users. Characteristics of users to consider include (but are certainly not limited to) age, gender, culture, ethnicity, socioeconomic status, education, and language fluency. Policies should encourage and support developers in investing time and resources into pre- and post-launch assessments, including:

pilot tests to assess performance across a wide variety of groups that might experience disparate impact before large-scale application

monitoring whether and to what extent disparate use patterns and outcomes are observed after release

identifying appropriate corrective action if issues are found.

Require that users be clearly informed about what tools can do and what they cannot. Neither health care workers nor patients are likely to have extensive training or sophisticated understanding of the technical underpinnings of AI tools. It will be essential that plain-language use instructions, cautionary warnings, or other features designed to inform appropriate application boundaries are built into tools. Without these features, users’ expectations of AI capabilities might be inaccurate, with negative effects on health outcomes. For example, a recent report outlines how overreliance on AI tools by inexperienced mushroom foragers has led to cases of poisoning; it is easy to imagine how this might be a harbinger of patients misdiagnosing themselves with health care tools that are made publicly available and missing critical treatment or advocating for treatment that is contraindicated. Similarly, tools used by health care professionals should be supported by rigorous use protocols. Although advanced tools will likely provide accurate guidance an overwhelming majority of the time, they can also experience catastrophic failures (such as those referred to as “hallucinations” in the AI field), so it is critical for trained human users to be in the loop when making key decisions.

Proactively protect against medical misinformation. False or misleading claims about health and health care—whether the result of ignorance or malicious intent—have proliferated in digital spaces and become harder for the average person to distinguish from reliable information. This type of misinformation about health care AI tools presents a serious threat, potentially leading to mistrust or misapplication of these tools. To discourage misinformation, guardrails should be put in place to ensure consistent transparency about what data are used and how that continuous verification of training data accuracy takes place.

How can regulation of AI in health care keep pace with rapidly changing conditions?

In addition to developers of tools themselves, there are important opportunities for unaffiliated researchers to study the impact of AI health care tools as they are introduced and recommend adjustments to any regulatory framework. Two examples of what this work might contribute are:

Social scientists can learn more about how people think about and engage with AI tools, as well as how perceptions and behaviors change over time. Rigorous data collection and qualitative and quantitative analyses can shed light on these questions, improving understanding of how individuals, communities, and society adapt to shifts in the health care landscape.

Systems scientists can consider the co-evolution of AI tools and human behavior over time. Building on or tangential to recent research, systems science can be used to explore the complex interactions that determine how multiple health care AI tools deployed across diverse settings might affect long-term health trends. Using longitudinal data collected as AI tools come into widespread use, prospective simulation models can provide timely guidance on how policies might need to be course corrected.

[sak-shi]

Python Libraries to Learn Before Tackling Data Analysis

To tackle data analysis effectively in Python, it's crucial to become familiar with several libraries that streamline the process of data manipulation, exploration, and visualization. Here's a breakdown of the essential libraries:

 1. NumPy

   - Purpose: Numerical computing.

   - Why Learn It: NumPy provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

   - Key Features:

     - Fast array processing.

     - Mathematical operations on arrays (e.g., sum, mean, standard deviation).

     - Linear algebra operations.

 2. Pandas

   - Purpose: Data manipulation and analysis.

   - Why Learn It: Pandas offers data structures like DataFrames, making it easier to handle and analyze structured data.

   - Key Features:

     - Reading/writing data from CSV, Excel, SQL databases, and more.

     - Handling missing data.

     - Powerful group-by operations.

     - Data filtering and transformation.

 3. Matplotlib

   - Purpose: Data visualization.

   - Why Learn It: Matplotlib is one of the most widely used plotting libraries in Python, allowing for a wide range of static, animated, and interactive plots.

   - Key Features:

     - Line plots, bar charts, histograms, scatter plots.

     - Customizable charts (labels, colors, legends).

     - Integration with Pandas for quick plotting.

 4. Seaborn

   - Purpose: Statistical data visualization.

   - Why Learn It: Built on top of Matplotlib, Seaborn simplifies the creation of attractive and informative statistical graphics.

   - Key Features:

     - High-level interface for drawing attractive statistical graphics.

     - Easier to use for complex visualizations like heatmaps, pair plots, etc.

     - Visualizations based on categorical data.

 5. SciPy

   - Purpose: Scientific and technical computing.

   - Why Learn It: SciPy builds on NumPy and provides additional functionality for complex mathematical operations and scientific computing.

   - Key Features:

     - Optimized algorithms for numerical integration, optimization, and more.

     - Statistics, signal processing, and linear algebra modules.

 6. Scikit-learn

   - Purpose: Machine learning and statistical modeling.

   - Why Learn It: Scikit-learn provides simple and efficient tools for data mining, analysis, and machine learning.

   - Key Features:

     - Classification, regression, and clustering algorithms.

     - Dimensionality reduction, model selection, and preprocessing utilities.

 7. Statsmodels

   - Purpose: Statistical analysis.

   - Why Learn It: Statsmodels allows users to explore data, estimate statistical models, and perform tests.

   - Key Features:

     - Linear regression, logistic regression, time series analysis.

     - Statistical tests and models for descriptive statistics.

 8. Plotly

   - Purpose: Interactive data visualization.

   - Why Learn It: Plotly allows for the creation of interactive and web-based visualizations, making it ideal for dashboards and presentations.

   - Key Features:

     - Interactive plots like scatter, line, bar, and 3D plots.

     - Easy integration with web frameworks.

     - Dashboards and web applications with Dash.

 9. TensorFlow/PyTorch (Optional)

   - Purpose: Machine learning and deep learning.

   - Why Learn It: If your data analysis involves machine learning, these libraries will help in building, training, and deploying deep learning models.

   - Key Features:

     - Tensor processing and automatic differentiation.

     - Building neural networks.

 10. Dask (Optional)

   - Purpose: Parallel computing for data analysis.

   - Why Learn It: Dask enables scalable data manipulation by parallelizing Pandas operations, making it ideal for big datasets.

   - Key Features:

     - Works with NumPy, Pandas, and Scikit-learn.

     - Handles large data and parallel computations easily.

Focusing on NumPy, Pandas, Matplotlib, and Seaborn will set a strong foundation for basic data analysis.

[apexbyte]

What is artificial intelligence (AI)?

Imagine asking Siri about the weather, receiving a personalized Netflix recommendation, or unlocking your phone with facial recognition. These everyday conveniences are powered by Artificial Intelligence (AI), a transformative technology reshaping our world. This post delves into AI, exploring its definition, history, mechanisms, applications, ethical dilemmas, and future potential.

What is Artificial Intelligence? Definition: AI refers to machines or software designed to mimic human intelligence, performing tasks like learning, problem-solving, and decision-making. Unlike basic automation, AI adapts and improves through experience.

Brief History:

1950: Alan Turing proposes the Turing Test, questioning if machines can think.

1956: The Dartmouth Conference coins the term "Artificial Intelligence," sparking early optimism.

1970s–80s: "AI winters" due to unmet expectations, followed by resurgence in the 2000s with advances in computing and data availability.

21st Century: Breakthroughs in machine learning and neural networks drive AI into mainstream use.

How Does AI Work? AI systems process vast data to identify patterns and make decisions. Key components include:

Machine Learning (ML): A subset where algorithms learn from data.

Supervised Learning: Uses labeled data (e.g., spam detection).

Unsupervised Learning: Finds patterns in unlabeled data (e.g., customer segmentation).

Reinforcement Learning: Learns via trial and error (e.g., AlphaGo).

Neural Networks & Deep Learning: Inspired by the human brain, these layered algorithms excel in tasks like image recognition.

Big Data & GPUs: Massive datasets and powerful processors enable training complex models.

Types of AI

Narrow AI: Specialized in one task (e.g., Alexa, chess engines).

General AI: Hypothetical, human-like adaptability (not yet realized).

Superintelligence: A speculative future AI surpassing human intellect.

Other Classifications:

Reactive Machines: Respond to inputs without memory (e.g., IBM’s Deep Blue).

Limited Memory: Uses past data (e.g., self-driving cars).

Theory of Mind: Understands emotions (in research).

Self-Aware: Conscious AI (purely theoretical).

Applications of AI

Healthcare: Diagnosing diseases via imaging, accelerating drug discovery.

Finance: Detecting fraud, algorithmic trading, and robo-advisors.

Retail: Personalized recommendations, inventory management.

Manufacturing: Predictive maintenance using IoT sensors.

Entertainment: AI-generated music, art, and deepfake technology.

Autonomous Systems: Self-driving cars (Tesla, Waymo), delivery drones.

Ethical Considerations

Bias & Fairness: Biased training data can lead to discriminatory outcomes (e.g., facial recognition errors in darker skin tones).

Privacy: Concerns over data collection by smart devices and surveillance systems.

Job Displacement: Automation risks certain roles but may create new industries.

Accountability: Determining liability for AI errors (e.g., autonomous vehicle accidents).

The Future of AI

Integration: Smarter personal assistants, seamless human-AI collaboration.

Advancements: Improved natural language processing (e.g., ChatGPT), climate change solutions (optimizing energy grids).

Regulation: Growing need for ethical guidelines and governance frameworks.

Conclusion AI holds immense potential to revolutionize industries, enhance efficiency, and solve global challenges. However, balancing innovation with ethical stewardship is crucial. By fostering responsible development, society can harness AI’s benefits while mitigating risks.

[xublimetech]

The Future of AI: What’s Next in Machine Learning and Deep Learning?

Artificial Intelligence (AI) has rapidly evolved over the past decade, transforming industries and redefining the way businesses operate. With machine learning and deep learning at the core of AI advancements, the future holds groundbreaking innovations that will further revolutionize technology. As machine learning and deep learning continue to advance, they will unlock new opportunities across various industries, from healthcare and finance to cybersecurity and automation. In this blog, we explore the upcoming trends and what lies ahead in the world of machine learning and deep learning.

1. Advancements in Explainable AI (XAI)

As AI models become more complex, understanding their decision-making process remains a challenge. Explainable AI (XAI) aims to make machine learning and deep learning models more transparent and interpretable. Businesses and regulators are pushing for AI systems that provide clear justifications for their outputs, ensuring ethical AI adoption across industries. The growing demand for fairness and accountability in AI-driven decisions is accelerating research into interpretable AI, helping users trust and effectively utilize AI-powered tools.

2. AI-Powered Automation in IT and Business Processes

AI-driven automation is set to revolutionize business operations by minimizing human intervention. Machine learning and deep learning algorithms can predict and automate tasks in various sectors, from IT infrastructure management to customer service and finance. This shift will increase efficiency, reduce costs, and improve decision-making. Businesses that adopt AI-powered automation will gain a competitive advantage by streamlining workflows and enhancing productivity through machine learning and deep learning capabilities.

3. Neural Network Enhancements and Next-Gen Deep Learning Models

Deep learning models are becoming more sophisticated, with innovations like transformer models (e.g., GPT-4, BERT) pushing the boundaries of natural language processing (NLP). The next wave of machine learning and deep learning will focus on improving efficiency, reducing computation costs, and enhancing real-time AI applications. Advancements in neural networks will also lead to better image and speech recognition systems, making AI more accessible and functional in everyday life.

4. AI in Edge Computing for Faster and Smarter Processing

With the rise of IoT and real-time processing needs, AI is shifting toward edge computing. This allows machine learning and deep learning models to process data locally, reducing latency and dependency on cloud services. Industries like healthcare, autonomous vehicles, and smart cities will greatly benefit from edge AI integration. The fusion of edge computing with machine learning and deep learning will enable faster decision-making and improved efficiency in critical applications like medical diagnostics and predictive maintenance.

5. Ethical AI and Bias Mitigation

AI systems are prone to biases due to data limitations and model training inefficiencies. The future of machine learning and deep learning will prioritize ethical AI frameworks to mitigate bias and ensure fairness. Companies and researchers are working towards AI models that are more inclusive and free from discriminatory outputs. Ethical AI development will involve strategies like diverse dataset curation, bias auditing, and transparent AI decision-making processes to build trust in AI-powered systems.

6. Quantum AI: The Next Frontier

Quantum computing is set to revolutionize AI by enabling faster and more powerful computations. Quantum AI will significantly accelerate machine learning and deep learning processes, optimizing complex problem-solving and large-scale simulations beyond the capabilities of classical computing. As quantum AI continues to evolve, it will open new doors for solving problems that were previously considered unsolvable due to computational constraints.

7. AI-Generated Content and Creative Applications

From AI-generated art and music to automated content creation, AI is making strides in the creative industry. Generative AI models like DALL-E and ChatGPT are paving the way for more sophisticated and human-like AI creativity. The future of machine learning and deep learning will push the boundaries of AI-driven content creation, enabling businesses to leverage AI for personalized marketing, video editing, and even storytelling.

8. AI in Cybersecurity: Real-Time Threat Detection

As cyber threats evolve, AI-powered cybersecurity solutions are becoming essential. Machine learning and deep learning models can analyze and predict security vulnerabilities, detecting threats in real time. The future of AI in cybersecurity lies in its ability to autonomously defend against sophisticated cyberattacks. AI-powered security systems will continuously learn from emerging threats, adapting and strengthening defense mechanisms to ensure data privacy and protection.

9. The Role of AI in Personalized Healthcare

One of the most impactful applications of machine learning and deep learning is in healthcare. AI-driven diagnostics, predictive analytics, and drug discovery are transforming patient care. AI models can analyze medical images, detect anomalies, and provide early disease detection, improving treatment outcomes. The integration of machine learning and deep learning in healthcare will enable personalized treatment plans and faster drug development, ultimately saving lives.

10. AI and the Future of Autonomous Systems

From self-driving cars to intelligent robotics, machine learning and deep learning are at the forefront of autonomous technology. The evolution of AI-powered autonomous systems will improve safety, efficiency, and decision-making capabilities. As AI continues to advance, we can expect self-learning robots, smarter logistics systems, and fully automated industrial processes that enhance productivity across various domains.

Conclusion

The future of AI, machine learning and deep learning is brimming with possibilities. From enhancing automation to enabling ethical and explainable AI, the next phase of AI development will drive unprecedented innovation. Businesses and tech leaders must stay ahead of these trends to leverage AI's full potential. With continued advancements in machine learning and deep learning, AI will become more intelligent, efficient, and accessible, shaping the digital world like never before.

Are you ready for the AI-driven future? Stay updated with the latest AI trends and explore how these advancements can shape your business!