I'm writing a bunch of articles on the topic of the Implausibility of intelligent explosion. I'm presenting here a bunch of arguments and would like to know more about what people think about this.

Please note, that these are just 3 points I made in one of my articles. The article is really big to be put here. Here's the original article: https://medium.com/aiguys/scale-wont-turn-llms-into-agi-or-superintelligence-75be01ed9471?sk=8f3d7d0e8ba978d7f66838ee7064263f

The Environment Puts A Hard Limit On Individual Intelligence

Intelligence isn’t a superpower. Exceptional intelligence alone doesn’t guarantee exceptional power over circumstances. While higher IQ generally correlates with social success up to a point, this breaks down at the extremes. Studies show that an IQ of 130 can lead to more success than an IQ of 70, but there’s no evidence that an IQ of 170 brings more impact than an IQ of 130. Many impactful scientists, like Richard Feynman and James Watson, had IQs in the 120s or 130s, similar to many average scientists.

The utility of intelligence stalls because real-world achievement depends on more than just cognitive ability. Our environment limits how effectively we can use our intelligence. Historically and currently, environments often don’t allow high-intelligence individuals to fully develop or use their potential. For example, someone with high potential 10,000 years ago would have faced limited opportunities compared to today.

Stephen Jay Gould noted that many talented individuals have lived and died in challenging circumstances without realizing their potential. Similarly, an AI with a superhuman brain in a human body might not develop greater capabilities than a smart contemporary human. If high IQ alone led to exceptional achievements, we would see more high-IQ individuals solving major problems, which we don’t.

Intelligence Is External And Lies In Civilizational Growth

Intelligence isn’t just about our brains — our bodies, senses, and environment also shape how much intelligence we can develop. Importantly, our brains are only a small part of our total intelligence. We rely heavily on cognitive prosthetics that extend our problem-solving abilities: smartphones, laptops, Google, books, mathematical notation, programming, and most fundamentally, language. These tools aren’t just knowledge sources; they are external cognitive processes, non-biological ways to run thought and problem-solving algorithms across time, space, and individuals. Most of our cognitive abilities reside in these tools.

Humans alone are more or less similar to apes, but civilization, with its accumulated knowledge and external systems, elevates us. When a scientist makes a breakthrough, much of the problem-solving happens through computers, collaboration with other researchers, notes, and mathematical notation. Their individual cognitive work is just one part of a larger, collective process.

Discoveries often happen through exploring the unknown. The invention of computers was only possible after the discovery of vacuum tubes, which weren’t originally intended for that purpose. Similarly, even a super-intelligent machine can’t predict which innovations will lead to new breakthroughs. Resources on Earth are limited, and the more a machine tries to achieve a goal, the more it might waste resources and fail.

In summary, intelligence is situational and depends heavily on external tools and collective knowledge. Individual brains, no matter how advanced, are only a small part of the cognitive equation. Super-intelligent machines won’t necessarily lead to endless innovations due to resource constraints and the unpredictability of discovery.

Individual AI Won’t Scale No Matter How Smart It Gets

A single human brain, on its own, is not capable of designing a greater intelligence than itself. This is a purely empirical statement: out of billions of human brains that have come and gone, none has done so. Clearly, the intelligence of a single human, over a single lifetime, cannot design intelligence, or else, over billions of trials, it would have already occurred.

And if the machines are going to be very different than human intelligence, then we wouldn’t even know how to evaluate them, even if we build them, they’ll be operating in a completely different world. And the bigger question is, how do we design an intelligent system that is fundamentally different than ours?

And let’s say for the argument's sake, machines suddenly have an intelligence explosion. But even that would be based on the priors from human data, these machines are not suddenly going to go to different galaxies and talk to aliens and gather a completely new form of data. In that case, the only possibility is that somehow these machines have no priors, and if that’s the case, then the scaling laws we keep talking about have nothing to contribute to intelligence. Intelligence can’t be in isolation without the priors of humans.

Billions of brains, accumulating knowledge and developing external intelligent processes over thousands of years, implement a system — civilization — which may eventually lead to artificial brains with greater intelligence than that of a single human. It is civilization as a whole that will create superhuman AI, not you, nor me, nor any individual. A process involving countless humans, over timescales we can barely comprehend. A process involving far more externalized intelligence — books, computers, mathematics, science, the internet — than biological intelligence.

Will the superhuman AIs of the future, developed collectively over centuries, have the capability to develop AI greater than themselves? No, no more than any of us can. Answering “yes” would fly in the face of everything we know — again, remember that no human, nor any intelligent entity that we know of, has ever designed anything smarter than itself. What we do is, gradually, collectively, build external problem-solving systems that are greater than ourselves.

However, future AIs, much like humans and the other intelligent systems we’ve produced so far, will contribute to our civilization, and our civilization, in turn, will use them to keep expanding the capabilities of the AIs it produces. AI, in this sense, is no different than computers, books, or language itself: it’s a technology that empowers our civilization. The advent of superhuman AI will thus be no more of a singularity than the advent of computers, books, or language. Civilization will develop AI, and just march on. Civilization will eventually transcend what we are now, much like it has transcended what we were 10,000 years ago. It’s a gradual process, not a sudden shift.

In this case, you may ask, isn’t civilization itself the runaway self-improving brain? Is our civilizational intelligence exploding? No.

Simply put, No system exists in a vacuum, especially not intelligence, nor human civilization.

I've been researching how LinkedIn's detection actually works and it's freaking me out a little. They're not just counting clicks anymore, the system builds a behavioral baseline per account. I mean, how long your sessions run, how fast you scroll and how long you hover on a profile before hitting connect and even your typing rhythm when you write messages. When a bot takes over, that fingerprint doesn't match. And even tools with randomized delays are getting flagged, because the randomization itself has patterns that real humans never produce. So is there a durable strategy here or are we watching a slow death for this whole space?

Hey guy, I am newbie on this deep learning sub. I found this video.

We created Leval-S, a new way to measure gender bias in LLMs. It’s private, independent, and designed to reveal how models behave in the wild by preventing data contamination.

It evaluates how LLMs associate gender with roles, traits, intelligence, and emotion using controlled paired prompts.

🧠 Full results + leaderboard: https://www.levalhub.com

Top model: GPT-4.5 (94%)

Worst model: GPT-4o mini (30%)

Why it matters:

• AI is already screening resumes, triaging patients, guiding hiring

• Biased models = biased decisions

We’d love your feedback and ideas for what you want measured next.

So here’s the deal: I needed a 3D icon ASAP. No idea where to get one. Making it myself? Too long. Stock images? Useless, because I needed something super specific.

I tried a bunch of AI tools, but they either spat out garbage or lacked proper detail. I was this close to losing my mind when I found 3D Icon on AiMensa.

Typed in exactly what I wanted.

Few seconds later – BOOM. Clean, detailed 3D icon, perfect proportions, great lighting.

But I wasn’t done. I ran it through Image Enhancer to sharpen the details, reduce noise, and boost quality. The icon looked even cleaner.

Then, for the final touch, I removed the background in literally two clicks.  Uploaded it to Background Remover.

Hit the button – done. No weird edges.. Just a perfect, isolated icon ready to drop into a presentation or website.

I seriously thought I’d be stuck on this for hours, but AI took care of it in minutes. And the best part? It actually understands different styles and materials, so you can tweak it to fit exactly what you need.

This might be my new favorite AI tool.

Honestly, using a Mac with Apple Silicon sucks for learning deep learning. I bought an m1pro 14-inch Mac for my MSc in Ai, and throughout the course, it always consistently reduced productivity.

Basic issues like removing .cuda() from codes and adding the device as "mps" as all the assignments are designed for intel (/amd) based GPUs. To shit like spending four days trying to make use of Apple's GPU on an assignment only to find out the pytorch lib has issues with some specific fucking tiny piece of shit function, OR working 3hrs on designing a model and 8hrs on training it to output an audio file only to get "UNSUPPORTED HARDWARE".

When you ask professors or any teaching staff they are also blind with this.

Fuck you my past self on thinking m1pro would be amazing. Should have bought a fcking Chromebook to connect to the university computer or even google colab.

How come LLM responds in constant time even for polynomial or exponential problems?

Approximating a plan from memory is not reasoning or planning. A plan is not a plan that doesn’t work 100% of the time, it is just an idea. The formal planning and logic needs that plan should be verifiable, and this is something LLMs can never do on their own. They can never verify their own responses 100% of the time and that’s why they are idea generation machines.

These are the main reasons for the believers in LLMs' reasoning and planning capabilities.

1. LLMs can Plan And Reason, and that’s why they are good at code generation.
2. What about the emergence capabilities of LLMs?
3. What about Chain-of-thought, ReACT, and other agentic frameworks?
4. In-context learning surely helps
5. What if we finetuned LLMs with successful plans in the domain?
6. But LLMs won a silver medal in the Math Olympiad and are reaching close to human performance even in the ARC-AGI challenge
7. But LLMs can self-critique and that surely increases the performance

Check out the Original Blog: https://medium.com/aiguys/llms-still-cant-plan-and-reason-1026919225fb?sk=e00da7e84f7059e205bedcd7ba952d3e

1. They retrieve code, and they improve upon it because they are trained on different versions of GitHub branches and thus they appear to improve code when asked to debug.

2. One of the biggest claims that were made about emergence was that somehow these models automatically learned the language they were not even trained on.

Later on, we discovered that the training data already had that language present in it, but we just didn’t know about it. We have literally no clue as to what kind of information is actually available on the internet, we just think this can’t be present and when LLMs pick up those, we call them emergent.

3. Confirmed by Chain of Thought Author

• Diminishing returns

• No out-of-distribution generalization

• Doesn’t accurately capture the implicit algorithm

1. https://arxiv.org/pdf/2405.13966

2. https://arxiv.org/html/2406.11201v1

6. Trying out over 6k Python programs, validating the result of each program, and then reaching a meager of 50%. That's not planning.

7. There exist formal notions of correctness for these domains that allow us to automatically check both the (binary) verification and the critique generated by LLMs. Such verification is not possible in style-based/qualitative tasks (Eg: writing a good essay, a good screenplay, etc). And that’s exactly the reason why people are so confused.

Hi guys, This is my first ever computer, I am working on Conversational AI, and this will be used for training my models and for 3d CAD.

Specifications:
GPU: Quadro RTX6000

CPU: AMD 3900x

RAM: 32gb DDR4 3600mhz ( Might upgrade to 64gb later )

Storage: 1TB Samsung 970 Pro ( will add more storage + HDD for backup later)

Hey everyone,

Standard transformers operate strictly in flat Euclidean space with continuous activations. While effective, this creates challenges when modeling hierarchical tree-structured data and incurs significant energy overhead.

I built Hyper-transformer to explore a hybrid architecture integrating non-Euclidean geometry and neuromorphic spiking dynamics directly into transformer layers.

Key Technical Details:

• Hyperbolic Space Representation: Uses Poincaré and Lorentz manifold projections to capture hierarchical relationships with lower embedding dimensions.

• Spiking Neural Dynamics: Employs Leaky Integrate-and-Fire (LIF) spiking neurons for sparse, event-driven activation states.

• Hybrid Attention: Combines Riemannian distance metrics with attention mechanisms to handle mixed-geometry representations.

• Clean, modular PyTorch implementation designed for ablation studies.

I will drop the full GitHub repository link in the comments below! Would love to hear thoughts from anyone experimenting with non-Euclidean deep learning, manifold learning, or SNNs.

repo link : https://github.com/Griffith-7/Hyper-transformer.git

Been running an AI trading agent connected through Alpaca as part of our Enton.ai experiments.

Goal: see if an LLM-driven reasoning layer + RL allocation model can trade like a disciplined quant, not a gambler. • Starting balance: $100,000 • Current balance: $102,892.63 (+1.32%)

The setup: • Analysis Agent: transformer-based model parsing market data + news embeddings • Signal Agent: reinforcement learning (reward = Sharpe-style ratio, volatility penalty) • Execution Agent: natural-language trade translation → Alpaca API

We’re not optimizing for “to the moon” returns — just stable, explainable performance.

Curious what others think about: • RL tuning for risk-adjusted reward • Integrating market state embeddings into transformer memory • Multi-agent coordination methods (autonomous finance architecture)

Screenshot attached for transparency. Always open to collab ideas.

I am using chatgpt for while and from Sometime I am using gpt and deepseek both just to compare who gives better output, and most of the time they almost write the same code, how is that possible unless they are trained on same data or the weights are same, does anyone think same.

I'm an independent researcher. I developed a closed-form stability metric Φ = I×ρ - α×S that tells you at epoch 1 whether an architecture will train successfully — no need to run full training.

How it works: compute three values from early training signals (identity preservation, temporal coherence, output entropy), plug into one equation, check if Φ > 0.25. That's it.

Results on 660+ architectures:

- 99.7% precision identifying non-viable architectures

- Works at epoch 1

- 80-95% compute savings by killing dead-end architectures early

- No training required for the metric itself

- Same formula works across all architectures tested

This isn't just a neural network trick. The same formula with the same threshold also works on:

- Quantum circuits (445 qubits, 3 IBM backends, 83% error reduction)

- Mechanical bearings and turbofan engines (100% accuracy)

- Cardiac arrhythmia detection (AUC 0.90)

- LLM behavioral drift detection (3 models up to 2.7B params)

All real data. Zero synthetic. Code is public.

Code repo: https://github.com/Wise314/quantum-phi-validation

Portfolio overview: https://github.com/Wise314/barnicle-ai-systems

Full framework paper: https://doi.org/10.5281/zenodo.18684052

Cross-domain paper: https://doi.org/10.5281/zenodo.18523292

Happy to discuss methodology.

Hi everyone,

We're working on creating a dataset of screen recordings of people performing 1 million actions on their computers so that we can train a Large Action Model that can control computers.

This is part of the Ethereum HackFS hackathon and we are building mechanisms to anonymize the data client side and store the redacted data using decentralized storage in a way that the data contributors own benefit from models trained with the data.

There already exist 2 datasets that could be used for LAM training:

1. WorkArena https://arxiv.org/pdf/2403.07718

2. WebLinx https://mcgill-nlp.github.io/weblinx/

But both of these are very small datasets.

They also include telemetry but we believe that we can train LAMs with only video recordings (like how a human can watch a YouTube tutorial and recreate the action on their device). This seems like Tesla's self driving on video rather than needing LiDAR)

What we need help with is defining the 1 million actions in this dataset. It should be a representative dataset across all the ways a human can use a computer. What would you like this dataset to contain that would enable you to use it / work on LAM research ?

Contributions, questions and advice welcome!

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Hey AI enthusiasts! 🚀

I've got a beast of a setup at my disposal for the next 30 days: 8 NVIDIA L40 GPUs, 1.5 TB of RAM, and a ton of storage. Instead of letting this power sit idle, I'm eager to collaborate with the community to train a Large Language Model (LLM) from scratch or work on any groundbreaking AI project you've been itching to try.

If you've got code, ideas, or ongoing projects that could benefit from this hardware, let's team up and create something amazing. Whether you're a researcher, developer, or hobbyist, I'm open to all levels of collaboration.

Drop a comment or DM me if you're interested. Let's push the boundaries of AI together! 🤖💡

AI #MachineLearning #LLM #Collaboration #GPU

Im analysing data using python but it seems to be much slower (taking days for just parsing and iterating through a dataset, 7.3 million rows, 7 columns ) though the dataset is large, could some one recommend a better language and libraries or someway I could improve the performance of python. I'm using pandas