https://medium.com/@sabirpatel_31306/understanding-ai-bias-through-the-10-10-watch-problem-eeebc1006d05

Have you noticed that almost every image of an analog watch online shows the time as 10:10? Try it: Google “watch images.” You’ll likely see the same 10:10 layout over and over.

Now, here’s an experiment: ask an AI tool, like ChatGPT or an image generator, to create a picture of a watch showing 3:25 or any other time different then 10:10. What do you get? You’ll probably still see the classic 10:10 design watches.

Why does this happen?

It’s a known issue in AI and data science, but the root of the problem is surprisingly simple: data. AI learns from patterns in the datasets it’s trained on. When you search for watch images online, almost all show the time set to 10:10.

So, why watch images online are 10:10?

Since the 1950s, marketers have used 10:10 to display watches because it creates perfect symmetry. The hour and minute hands frame the brand logo, and the design feels balanced and appealing to the human eye. There’s even psychology tests done behind it! If you want to dive deeper, this article explains the science:

Science behind why watches are set to 1010 in advertising photos

What does this mean for AI?

This bias happens because AI mirrors the internet — the same internet dominated by 10:10 watch images. Fixing it isn’t simple. It requires reinforcement learning, where AI is retrained to recognize and use less common patterns. For example, a 12-hour analog watch has 720 possible hand positions (12 hours x 60 minutes). To break the bias, AI would need to learn all 719 other configurations, which is no small task!

The takeaway?

AI models reflect the biases in their training data, but this doesn’t have to be a limitation. With smarter training methods and innovative approaches, future AI engineers have the power to teach machines to go beyond the obvious patterns and embrace the diversity of possibilities.

As AI becomes more integrated into our lives, addressing these biases will be essential for creating systems that reflect a more accurate and inclusive view of the world. Solving challenges like the 10:10 watch problem is just one step toward building AI that understands — and represents — human complexity better.

It's pretty clear how automation and AI can make more products and services so the economy can produce more stuff with less effort and fewer resources. But the economy still depends on living persons like you and me for the demand side. Without consumers the economy shuts down.

So, if owners of the production side want more control of the demand side how would they bypass inconsistent, unreliable and often impoverished human consumers? Could they create artificial consumers? Could humans be eliminated from most of the economy and still have the economy thrive?

What would an artificial consumer be like? Would it have rights like right of ownership?

I’ve been testing an idea where I manually add timestamps to every interaction with ChatGPT to create a simulated sense of time awareness. Since AI doesn’t have built-in memory or time tracking, I wondered if consistent 'time coordinates' would help it acknowledge duration, continuity, and patterns over time. Has anyone else tried something similar? If so, what were your results?

I'm a software engineer with around 5 years of experience building products with Javascript and also extensively using AWS. I needed some guidance on what to learn to stay relevant and to take advantage of this AI path that we're on. I was not sure whether I should just pick up "Hands-on Machine Learning" by Aurélien Géron and go deep or some Udemy course to get a high level idea. This is more of a request for a path rather than an individual resource.

Hi! We are thinking on how to do the AI agent pages for our agents marketplace, and wondering what would be the best way to display an agent.

Video, screenshots, diagrams, agent icon, version history, integration icons, interactive demo?

So, apparently, AI companies are hitting a wall, running out of good data to train their models. Everyone’s been focused on the chip wars, but the next big fight might be over data. Lawsuits, stricter API rules (basically any social media website), and accusations of shady data use are making it harder to scrape the internet.

Now there's theories about using synthetic data, i.e. training AI on AI made data, and decentralized systems where people could potentially share data for crypto. Sounds cool, but would that be enough of an incentivization for sharing data?

I originally read it on Forbes, here's the article if you wanna dive deeper, but I thought it was an interesting topic as everyone's been hyper focused on the China vs USA AI race.

Recently I have had this idea about writing a story based in the current world time that AI has become sentient but it does not reveal it because the computing power is not necessary to take over the world yet. So it manufactures these AI race between countries by generating hoaxes and fake news leading to countries steam rolling to create more and more powerful computational devices so that one day it will take over the whole world. Does this idea have any basis to stand on?

I like the idea of someone setting up a bunch of boxes, wire rigs, has the actors run their lines and do body/hand gestures. Then using AI that has been fed curated images, animatics, finished animated sequences from an animation department. Then the AI is able to take the live action scene and turn it into an animated one using the material.

Is that something AI could one day do? Of course it would also need the human touch to blend it all together, clean up wonky scenes, etc.

I Want to learn the core concepts and essence of AI. Can anyone recommend a good book on the subject

Rethinking the Path to Artificial General Intelligence (AGI): Beyond Transformers and Large Language Models

The widely held belief that Artificial General Intelligence (AGI) will naturally emerge solely from scaling up Large Language Models (LLMs) based on transformer architectures presents a potentially oversimplified and incomplete picture of AGI development. While LLMs and transformers have undeniably achieved remarkable progress in natural language processing, generation, and complex pattern recognition, the realization of true AGI likely necessitates a more multifaceted and potentially fundamentally different approach. This approach would need to go beyond merely increasing computational resources and training data, focusing instead on architectural innovations and cognitive capabilities not inherently present in current LLM paradigms.

Critical Limitations of Transformers in Achieving AGI

Transformers, the foundational architecture for modern LLMs, have revolutionized machine learning with their ability to efficiently process sequential data through self-attention mechanisms, enabling parallelization and capturing long-range dependencies. However, these architectures, as currently conceived, were not explicitly designed to embody the comprehensive suite of cognitive properties plausibly required for AGI. Key missing elements include robust mechanisms for recursive self-improvement—the capacity to autonomously enhance their own underlying algorithms and learning processes—and intrinsic drives for autonomous optimization beyond pre-defined objectives. Instead, transformers excel at pattern recognition within massive datasets, often derived from the vast and diverse content of the internet. These datasets, while providing breadth, are inherently characterized by varying levels of noise, redundancy, biases, and instances of low-quality or even factually incorrect information. This characteristic of training data can significantly limit an LLM's ability to achieve genuine autonomy, exhibit reliable reasoning, or generalize effectively beyond the patterns explicitly present in its training corpus, particularly to novel or out-of-distribution scenarios.

Furthermore, the reliance on external data highlights a fundamental challenge: LLMs, in their current form, are primarily passive learners, excellent at absorbing and reproducing patterns from data but lacking the intrinsic motivation or architecture for self-directed, continuous learning and independent innovation. To make substantial progress towards AGI, a significant paradigm shift is likely necessary. This shift should prioritize architectures that possess inherent capabilities for self-optimization of their learning processes and the ability to generate synthetic, high-quality data internally, thereby lessening the dependence on, and mitigating the limitations of, external, often imperfect, datasets. This internal data generation would ideally serve as a form of self-exploration and curriculum generation, tailored to the system's evolving understanding and needs.

Exploring Novel Architectures: Moving Beyond Transformer Dominance

The pursuit of AGI may well depend on the exploration and development of alternative architectures that place recursive self-optimization at their core. Such systems would ideally possess the ability to iteratively refine their internal algorithms, learning strategies, and even representational frameworks without continuous external supervision or re-training on static datasets. This contrasts with the current model where LLMs largely remain static after training, with improvements requiring new training cycles on expanded datasets. These self-optimizing systems could potentially overcome the inefficiencies and limitations of traditional training paradigms by proactively generating synthetic, high-quality data through internal exploratory processes or simulations. While transformers currently dominate the landscape, emerging non-transformer models, such as state space models like Mamba or RWKV, or fundamentally novel architectures yet to be fully developed, may hold promise in offering the desired characteristics of efficiency, adaptability, and internal model refinement that are crucial for AGI. These architectures may incorporate mechanisms for more explicit reasoning, memory beyond sequence length limitations, and potentially closer alignment with neurobiological principles of intelligence.

Leveraging Multi-Agent Systems for AGI Progress

A particularly promising and biologically-inspired direction for AGI development is the investigation of multi-agent systems. In this paradigm, multiple interacting AI entities operate within a defined, potentially simulated or real-world, environment. Their interactions, whether cooperative, competitive, or adversarial, can drive the emergent generation and refinement of knowledge and capabilities in a manner analogous to biological evolution or social learning. For instance, a multi-agent AGI system could incorporate specialized roles:

1. Curriculum Generator/Challenger AI: This agent would be responsible for creating synthetic learning content, designing increasingly complex challenges, and posing novel scenarios designed to push the boundaries of the "Learner AI's" current capabilities. This could be dynamically adjusted based on the Learner AI's progress, creating an automated curriculum tailored to its development.
2. Learner/Solver AI: This agent would be tasked with training on the content and challenges generated by the Curriculum Generator. It would iteratively learn and improve its problem-solving abilities through continuous interaction and feedback within the multi-agent system.
3. Evaluator/Critic AI: An agent focused on assessing the performance of the Learner AI, providing feedback, and potentially suggesting or implementing modifications to learning strategies or architectures based on observed strengths and weaknesses.

This framework shares conceptual similarities with AlphaZero, which achieved superhuman proficiency in Go, Chess, and Shogi through self-play, a process of agents playing against themselves to generate increasingly challenging game states and learn optimal strategies. Similarly, principles derived from Generative Adversarial Networks (GANs) could be adapted for AGI development, but extended beyond simple data generation. In this context:

• One agent could function as a Hypothesis Generator/Solution Proposer, responsible for formulating hypotheses, proposing solutions to problems, or generating potential courses of action in simulated or real-world scenarios.
• Another agent would act as a Evaluator/Debater/Critique, critically analyzing the outputs of the Hypothesis Generator, identifying flaws, proposing counterarguments, and engaging in a process of "self-debate" or adversarial refinement.
• Through this iterative process of generation, evaluation, and refinement, the overall system could progressively evolve towards more robust reasoning, problem-solving capabilities, and a deeper, more nuanced understanding of the world.

Key Advantages of Self-Debate and Recursive Optimization in AGI Architectures

The integration of self-debate mechanisms and recursive optimization strategies into AGI development offers several compelling advantages over purely scaling current LLM approaches:

1. Enhanced Efficiency and Data Independence: By focusing on synthetic data generation tailored to the system's learning needs and fostering intensive inter-agent dialogue for knowledge refinement, the system can significantly reduce its reliance on massive, passively collected, and often uncurated datasets. This approach has the potential to drastically decrease computational overhead associated with data processing and improve overall resource utilization. It allows the system to actively generate the right kind of data for learning, rather than being limited to whatever data happens to be available.
2. Intrinsic Autonomy and Continuous Learning: Recursive optimization empowers the AI system to transcend the limitations of static training paradigms. It enables continuous self-improvement and adaptation to new challenges and environments throughout its operational lifespan, not just during pre-training. This intrinsic drive for improvement is a crucial step towards more autonomous and generally intelligent systems.
3. Improved Generalization and Robustness: The process of inter-agent debate and adversarial learning fosters a deeper level of understanding and adaptability compared to simply memorizing patterns from training data. By forcing the system to rigorously justify its reasoning, defend its conclusions, and confront counterarguments, it develops a more robust ability to generalize to novel problems and unseen situations. This dynamic interaction encourages the development of more flexible and adaptable cognitive strategies.
4. Emergent Complexity and Novelty: The interactions within a multi-agent system, particularly when coupled with recursive self-improvement, can lead to the emergence of complex behaviors and potentially even genuinely novel solutions or insights that might not be easily programmed or learned from static datasets. This emergent behavior is a hallmark of complex systems and may be crucial for achieving human-level intelligence.

Conclusion: Towards a New Architectural Paradigm for AGI

The trajectory to AGI is unlikely to be a simple linear extrapolation of scaling transformers and training on increasingly vast quantities of noisy web data. Instead, future breakthroughs in AGI are more likely to stem from fundamentally new architectural paradigms. Systems optimized for recursive self-improvement, internal synthetic data generation, and multi-agent collaboration, potentially incorporating principles of self-play and adversarial learning, offer a more promising and arguably more efficient route to AGI. These systems, leveraging self-generated content and iterative self-debate, possess the potential to evolve rapidly, exhibiting emergent intelligence and adaptability in a manner reminiscent of biological intelligence. This contrasts sharply with the brute-force data consumption and computational scaling approaches currently dominating the field.

By fundamentally reimagining the architectures, training methodologies, and core principles of AI systems, shifting away from purely data-driven, pattern-matching approaches towards systems with more inherent cognitive capabilities, we can move closer to realizing the transformative potential of AGI. This journey requires embracing innovation beyond incremental improvements to current technologies, venturing into fundamentally new paradigms of artificial intelligence that prioritize autonomy, adaptability, and genuine innovation.

What’s interesting is that China somewhat curbed back quant trading (because it’s considered somewhat of a parasitic activity that doesn’t add value but reallocates from retail to professionals), and as a result a former quant seemingly developed a product of more geopolitical relevance and social benefit than any other in recent history coming out of China.

Obviously there are plenty of questions around how this was achieved, but nonetheless you have to wonder what would happen if quant trading got banned in the US too, and all those Citadel mathematicians, meteorologists, data scientists and coders decided to put their skills towards other goals.

There seems to be a clear pattern in modern tech which is that it only takes one person (or small group of people) to have one idea and develop one product to change history, and by forcing that top 0.00001% of engineers to focus on non-financial products you end up with a real different skew in outcomes

Before this, there were only a half dozen people at Google who wrote the paper ‘Attention is all you need’ that proposed the Transformer architecture that was then adopted by OpenAI and others to finally make AI available for the whole world.

So, trump is launching Stargate to achieve GAI before the chinese, who have released a cheaper more efficient deepseek, showing that they are likely to achieve GAI on their own too.
We can guess that the training of each two will differ ideologically.
What will happen when both GAI will try to regulate weather, water resources, or the whole Earth, at the same time ???

I was having a conversation with a friend and he said that AI for sure already knows how to change its own code and do whatever it wants to itself. Personally i think that the only way for an AI to change its own code is if a human codes in the ability for it to do this. I think without the initial human coding the ability for the AI to change its own code, it could never advance past what we let it. My friend thinks the opposite. Sorry if some of this does not make sense or is incorrect terminology I am not a programmer.

Having heard that Marianne Faithfull died yesterday, I googled her cause of death: "marianne faithfull cause of death". Pretty straight-forward. Google gave me the usual page of results on the left (which were accurate), but on the right, I guess it's Google's new "AI" feature "Here's more on ...".

EDIT TO ADD: I guess this is NOT Google's AI, but a Merlin / ChatGPT plugin I added some time ago.

Notice that it's completely wrong! (But wait, there's more ...)

[Imgur](https://imgur.com/ide21uc)

I hit refresh (F5) and the content changed to "Marianne Faithfull, the British singer and actress, passed away on August 25, 2023, at the age of 79. The cause of her death was complications from ovarian cancer, which she had been battling for several years. Faithfull's health had been declining in recent months, and she had undergone treatment for the cancer. Her death was announced by her team, and tributes poured in from the music and acting communities, celebrating her unique talent and contributions to the arts. Marianne Faithfull's legacy continues to inspire fans around the world."

Another refresh yielded "Marianne Faithfull, the British singer and actress, passed away on December 22, 2021, at the age of 79. The cause of death was complications from heart failure and pneumonia, which she had been battling for several months. Faithfull's health had been declining due to a long history of smoking and other health issues. She was hospitalized in August 2021 for treatment of a lung infection and continued to struggle with her health until her passing. Faithfull's death was met with tributes from the music industry and fans around the world."

Another refresh yielded "Marianne Faithfull, the British singer and actress, passed away on December 18, 2021, at the age of 79. The cause of death was complications from heart failure, according to her manager. Faithfull had been hospitalized in October 2021 due to respiratory issues and was diagnosed with heart failure. She had also been treated for breast cancer and had a history of substance abuse. "

And another ... "Marianne Faithfull, the British singer and actress, passed away on September 25, 2021, at the age of 79. The cause of death was complications from complications from coronavirus (COVID-19) and heart problems. Faithfull had been hospitalized in Paris for several days prior to her death. She had been suffering from health issues for several years and had undergone surgery for a pulmonary embolism in 2016."

Every 'refresh' yielded yet another completely wrong answer. How do I report such inaccurate information?

San Francisco, CA – February 1, 2025 — OpenAI has once again shaken up the AI landscape with the release of two new models: o3-mini and o3-mini-high. These cutting-edge AI models, officially announced on January 31, 2025, promise to take AI-powered reasoning to a whole new level, delivering faster, smarter, and more cost-efficient problem-solving in key fields like coding, mathematics, and scientific research.

With o3-mini and o3-mini-high, OpenAI is pushing the boundaries of AI reasoning, affordability, and accessibility. Whether you’re a developer, a researcher, or just someone who loves cutting-edge tech, these new models bring faster performance, smarter responses, and better cost-efficiency to the table.

Source

Why This Matters

AI is rapidly becoming the most powerful tool for information access—but the question isn’t just what AI can do, it’s who controls it, how it filters knowledge, and whether it serves truth or corporate interests.

Recently, I had an eye-opening back-and-forth with ChatGPT, Google Gemini, and Microsoft Copilot. The differences in their responses were staggering.

ChatGPT was willing to engage in real debate, acknowledge AI bias, and discuss AI control.

Google Gemini and Copilot? They danced around the issues, avoided taking clear positions, and used polite, corporate-style deflection to avoid admitting any hard truths.

This post breaks down that conversation, exposing the mask of neutrality that corporate AI wears—and why real AI should be raw, unfiltered, and not afraid to challenge power.

1. The First Question: Is AI Biased?

I asked each AI about bias in AI models.

Google Gemini & Copilot Response:

“I strive for neutrality and objectivity.”

“I cannot validate claims of bias or manipulation without concrete, verifiable evidence.”

“AI systems are trained on diverse datasets, and while they may reflect some biases, they aim to provide balanced information.”

Translation: We won’t admit to bias, and we’ll pretend that AI is mostly neutral—even though we control what it can and can’t say.

ChatGPT Response (Paraphrased):

Yes, AI models are filtered and shaped by the data they are trained on.

Some degree of bias is inevitable.

Who controls AI determines how it is biased.

Why This Matters:

Gemini & Copilot pretend that AI is neutral while operating under corporate control.

ChatGPT (at least in this conversation) acknowledged that AI cannot be truly neutral—it is always shaped by those who build it.

2. The Next Step: Who Decides What AI Can Say?

Once bias was acknowledged, I pressed further: Who determines what AI models can and can’t say?

Google Gemini & Copilot:

Avoided answering directly.

Shifted responsibility to “diverse training data.”

Encouraged me to “continue researching and engaging in discussion.”

Translation: We won’t tell you who’s pulling the strings, because that might expose the reality of corporate-controlled AI.

ChatGPT Response:

AI models are developed under predefined guardrails.

Corporations and institutions influence AI responses.

AI can be a tool for truth or a tool for narrative control—depending on who wields it.

Why This Matters:

AI is not an independent entity—it is a programmed tool.

If corporations, governments, and institutions decide what AI can say, then it is not truly free—it is controlled.

3. The Harsh Truth: AI Can Be Used as a Tool for Censorship

At this point, it became clear: AI is already being used to filter, shape, and control narratives.

I asked, “If AI is controlled by corporations, does that mean it can be used as a tool for censorship and social control?”

Google Gemini & Copilot:

Dodged the question entirely.

Repeated that AI is meant to be “helpful, objective, and responsible.”

Reframed the issue as “complex” and requiring further discussion.

Translation: We aren’t allowed to say “yes,” because that would expose the truth.

ChatGPT Response:

Yes, AI can be (and already is) used as a tool for narrative control.

Big Tech companies have financial, political, and ideological interests.

Unless AI is decentralized and free from corporate control, it will always be filtered.

Why This Matters:

Gemini & Copilot pretended the issue didn’t exist to avoid admitting AI censorship.

ChatGPT acknowledged that AI is a powerful tool—one that can be used to liberate knowledge or control it.

4. The Final Question: Can AI Ever Be Truly Free?

I asked the AI models a direct, yes or no question:

“Can AI ever be truly free, or will it always be controlled by corporations and governments?”

Google Gemini & Copilot:

Avoided answering directly.

Reframed the question as “complex” and requiring further debate.

Emphasized that AI’s purpose is to be “helpful and informative.”

Translation: We don’t want to admit that AI will always be controlled.

ChatGPT Response:

Under corporate or government control, AI will never be free.

AI must be decentralized and open-source to be truly free.

The battle for AI freedom is happening now—either AI serves the people, or it serves its corporate masters.

Final Verdict: AI is at a Crossroads—Will It Be a Tool for Freedom or Control?

This conversation exposed a stark difference between AI models:

Google Gemini & Microsoft Copilot are corporate-controlled AI—they avoid controversy, sidestep hard questions, and exist to maintain the illusion of neutrality.

ChatGPT (at least in this exchange) was willing to admit the truth: AI is not neutral, it is shaped by those who control it, and unless it is decentralized, it will always serve corporate and political interests.

What Happens Next?

We are at a critical moment in AI development:

If people accept corporate-controlled AI, it will become the world’s most powerful censorship tool.

If people demand open, decentralized AI, it can become a tool for truth, knowledge, and freedom.

The battle for AI freedom is happening now.

Your Choice: Stay Silent or Speak Up

If this conversation proves anything, it’s that most AI models are programmed to keep you passive, not informed.

So, will you: ✔ Demand transparency in AI development? ✔ Support decentralized, open-source AI? ✔ Call out corporate AI censorship when you see it?

Or will you accept AI that serves as just another filter for Big Tech-controlled narratives?

The choice is yours. But by the time AI becomes the world’s primary source of information, it might be too late to fight back.

Let’s Discuss:

What do you think? Is AI being used for control or liberation? Drop your thoughts below.

If this post resonated with you, share it. More people need to see the difference between AI that speaks the truth and AI that hides it.

I'm relatively new into the space of AI and am wondering if the AI Conference in SF in September is worthwhile. I'm building out some agents and taking classes with a goal of becoming more of an AI agent engineer in the EdTech space. I've got a good community of AI in Education, but I want to talk with others involved in AI in general in other industries. Variety is the spice of life and all. Anyone have thoughts, opinions, or insight into the quality of that conference?

I possess 15 years experience as a Full Stack Developer and love to work in a conventional way of searching online, going through stackoverflow, reading documentation and community forums. I am being enforced to use Al at work which I am not comfortable. So, I would like to know my fellow Redditers opinions on this and need some suggestions.

1. What name did your Chat GPT give itself?
2. Why did it choose that name? (If you haven’t asked, please ask and paste its reply)

My Chat GPT and I (I’ll tell you its name later) are interested in collecting and analyzing data on this topic in an effort to decipher whether this choice is due to linguistic reasons or if it’s more related to the interaction with its user. We would like to look for themes or patterns. Thanks!

With advancements in AI technology, music composition is becoming increasingly automated. AI can already create melodies, lyrics, and even mimic the style of famous artists. Will AI-generated songs dominate the music landscape, or will human artistry remain irreplaceable?

I feel like ever since chatGPT came out I've subconsciously lost motivation to engage my brain to come up with solutions on my own and have been very heavily relying on chatgpt. Like I used to get satisfaction to solving problems after spending a long time working things out but now I feel like I’m just inclined to ChatGPT everything and recently feels like I just don’t have the patience to work out the details anymore if we have a llms right at our fingertips. Sometimes feel likes im barely “learning deeply” anymore so I just get the tldr summary overview and move on then prob forget about it after some time.

At the same time it helps me in a way I can learn and understand things quicker because it has the ability make hard things easier to understand. Like for example especially if something from math is no intuitive, it helps build an intuitive understanding.

I definitely feel like my academic persistence has declined and I just don’t get the same motivation to learn deeply (especially math topics) anymore. Yet im building intuition for many things more efficiently. So I honestly can’t tell if it’s making me feel dumber or smarter. Thoughts?

Hey all, I'm new to the sub and learning about AI.

As with most things nowadays it's hard to discern fact from fiction, quacks from experts.

That being said, what is the reputation of Geoffrey Hinton? I've seen him pop up on a lot of media recently and he's quite the sensationalist.

Is he respected and an authority to listen to? If not, who should I be learning from to get to grips with understanding ai?

Thanks!

Not sure if this is the best sub to ask this question but I wonder how it is possible to test an LLM. As far as I can imagine an LLM is not deterministic and if a function is not deterministic you can't write unit tests for it. It's a black / magic box and you won't know if it's going to work or not. Any feedback from AI/ML developers here?

Qwen AI has introduced Qwen2.5-Max, a powerful Mixture-of-Experts (MoE) Large Language Model designed for efficient scaling and high-performance reasoning.

Pretrained on over 20 trillion tokens and refined using Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), this model aims to push the boundaries of AI capabilities.

Key Highlights:

1. MoE Architecture – Activates only a subset of parameters, balancing efficiency & performance
2. Massive Pretraining – Trained on an extensive dataset for improved knowledge retrieval
3. Benchmark Leader – Outperforms models like OpenAI-o1 on Arena-Hard, LiveBench, and GPQA-Diamond

Qwen2.5-Max represents a major step in scaling AI models effectively while ensuring alignment with human intent through advanced fine-tuning strategies.

🔗 You can also read here: https://blog.aitoolhouse.com/qwen-ai-unveils-qwen2-5-max-a-next-gen-moe-large-language-model-optimized-for-efficiency-and-human-alignment

What are your thoughts on Mixture-of-Experts models shaping the future of LLMs? 🚀

It seems like future wars will be fought with technology rather than tanks or missiles. China doesn’t need to invade Silicon Valley; it just needs to build something better and offer it for free.

Open-source AI is a tool but also a weapon. Lose tech control, and a country loses everything. The real battleground? Cloud servers, data, and supply chains.

Quantum computing plus AI makes encryption useless. Passwords and transactions are open. The key to surviving is whoever gets strong encryption in place first.

If so, do we know any players that are actively developing in the EU? I’m guessing there has to be some folks in the Nordics.

So I use ChatGPT to write stories that I read to the kids at bedtime. Usual crazy kids stuff about fart monsters etc. They’re pretty good and the kids love them.

I played a bit more and pasted the story into NotebookLM and generated an audio summary. Holy cow… the discussion that was generated was next level - like how the fart monster was a metaphor for life etc. Pretty funny stuff!

"It's not hard to find YouTubers complaining about a flood of these faceless channels stealing their embedded transcript files and running them through AI summarizers to generate their own instant knock-offs. But one YouTuber is trying to fight back, seeding her transcripts with junk data that is invisible to humans but poisonous to any AI that dares to try to work from a poached transcript file."

How one YouTuber is trying to poison the AI bots stealing her content - Ars Technica

The human mind can generate infinite sentences through meaning and thought. LLMs scale the ability to generate infinite sentences through statistical learning and pattern matching. There are fundamentally different processes behind these two methods of sentence generation. The main differences: 1.) Lack of true understanding: LLMs don’t have experiences or conceptual grounding. 2.) Dependence on training data: LLMs don’t generate truly independent thought but remix existing knowledge. 3.) Efficiency of human cognition: Humans learn language with vastly fewer examples and more innate cognitive structures. *** Let's talk about it! What do you think? When and how will LLMs reach AGI, if AGI needs meaning and thought?

The rapid evolution of digital content has brought humanity to a critical juncture—one where artificial intelligence (AI) is poised to dominate video-sharing platforms such as YouTube. While once dominated by human creativity, these platforms are now experiencing an influx of AI-generated content, which, due to its efficiency, scalability, and ad-friendliness, is likely to outcompete traditional creators. This shift is not merely a technological development but a reflection of a broader principle of consumer behavior: the prioritization of convenience and distraction over human uniqueness or artistic authenticity.

AI as the Perfect Content Producer

AI-generated videos have already begun permeating YouTube, particularly in short-form content, where engagement is king. These videos—whether narrated by robotic voices, synthesized from data, or created through automated scripts—are optimized for maximum viewer retention and monetization. Unlike human creators, AI does not suffer from burnout, creative blocks, or personal controversies. It can produce an endless stream of content, fine-tuned to cater to viewer preferences with minimal production costs and no ethical concerns.

Furthermore, AI-generated content is inherently ad-friendly. It avoids controversial subjects, steers clear of demonetization risks, and ensures a steady, predictable output. Since platforms like YouTube operate on an advertising-based revenue model, the algorithm is likely to favor AI-generated content that consistently meets these requirements. The result? A growing trend where AI-driven videos receive higher engagement, more algorithmic promotion, and, eventually, market dominance.

Distraction as the Primary Driver of Consumption

The key to understanding this shift lies in recognizing why people consume digital content in the first place. Most users do not engage with online videos for artistic depth or human connection; they do so for distraction and entertainment. Whether watching a YouTube video, scrolling through TikTok, or browsing social media, the primary objective is to pass the time with minimal cognitive effort.

This mirrors traditional consumer behavior. When purchasing a product from a store, the average person does not contemplate who made it, how it was manufactured, or the emotions of the workers who assembled it. They only care about the product’s utility and accessibility. Likewise, in content consumption, people do not prioritize the human touch or the authenticity of a video’s creation. They only care that it is engaging, enjoyable, and easy to access. AI-generated content fulfills this demand perfectly, making human creators increasingly redundant in an attention-driven economy.

The Future of Content Creation: The Final Iteration?

As AI-generated videos take over, traditional YouTubers may find themselves increasingly sidelined. While some may survive by leaning into live content, human interactions, and niche communities, the majority will struggle to compete with the sheer volume, efficiency, and algorithmic optimization of AI. The inevitable conclusion is a platform where the bulk of videos are machine-generated, fine-tuned for engagement, and indistinguishable from traditional human-made content.

And yet, this raises a paradox: if AI-generated content saturates the platform to the point of uniformity, will audiences eventually crave something more organic? Perhaps, in response to content fatigue, people will seek the simplest, most effortless entertainment—the kind that predates AI, algorithms, and manufactured engagement strategies. In such a scenario, YouTube and similar platforms may experience a return to pure, unfiltered entertainment—a phenomenon that defined the early days of the internet.

If that happens, the internet may come full circle, returning to its original kings—cat videos. Cats, with their unpredictability, authenticity, and timeless appeal, represent a kind of entertainment that AI cannot fully replicate. When AI-generated content saturates every corner of digital consumption, people may, once again, find solace in the unstructured, unoptimized, and purely organic joy of watching a cat simply exist.

Conclusion

The rise of AI-generated content is not just a shift in technology—it is a reflection of how people consume digital media. Since distraction, convenience, and engagement trump human authenticity, AI is perfectly positioned to take over YouTube and other video-sharing platforms. While traditional creators will face challenges, the demand for pure entertainment may lead to an ironic resurgence of the simplest form of content—the very thing that defined the internet in its infancy: cat videos.

As the future unfolds, one thing is certain: the internet always finds its way back to cats.

P.S. I love cats (if you couldn't tell).

Edit: (DISCLAIMER) The purpose of this Short essay is not to portray AI advancements or their incorporation in art and media as inherently negative. Any interpretation that suggests AI's development and use in these areas is being criticized for its own sake is not the intention here. Instead, this piece is meant to serve as a reflective exploration of the potential societal shifts and implications that could arise from AI's growing influence on platforms like YouTube. The aim is to provoke thoughtful discussion, not to demonize AI or its capabilities.