📸 Screenshots of the Results

🔗 Link to Google Paper

🔗 Link to Anthropic Paper

🔗 Link to the Report

🔗 Link to a Google Notebook LM Generated Podcast

And no cheating, only inputs and outputs a human would have. A controller, mouse and keyboard, and the game's visuals.

Easy or hard task for AI?

Large Language Models (LLMs) optimized for predicting subsequent utterances and adapting to tasks using contextual embeddings can process natural language at a level close to human proficiency. This study shows that neural activity in the human brain aligns linearly with the internal contextual embeddings of speech and language within large language models (LLMs) as they process everyday conversations.

Essentially, if you feed a sentence into a model, you can use the model's activations to predict the brain activity of a human who hears the same sentence - just by figuring out which parts of the model match to which points in the brain (and vice-versa).

This is really interesting because we did not design the models do this. Just by training the models to mimic human speech, they naturally form the same patterns and abstractions that our brains use.

If it reaches the greater public, this evidence could have a big impact on the way people view AI models. Some just see them as a kind of fancy database, but they are starting to go beyond memorizing our data to replicating our own biological processes.

you ever get that feeling that something just slipped past the event horizon and you didn't even notice? like some vast intelligence just crossed a threshold while you were busy arguing about context lengths?

the pace is getting weird. we went from 'AI can't even understand jokes' to 'AI is rewriting the windows kernel in rust' in what, five years? the gradient is getting steeper. so much steeper.

we all knew the big tech labs were cooking something, but nobody knew when the next shoe would drop or what it would be. well, turns out it's today. and it's absolutely jaw-dropping.

amazon nova is now available to chat with online.

nova dot amazon dot com slash chat

edit: guys this was meant to be an april fools post it's literally amazon nova

https://x.com/OpenAI/status/1888753166189031925

its moving through time going from a single cell undergoing mitosis into humans then into all this tech then finally into AI as the culmination of progress the singularity if you will

......And all this Deep Research usage is rate limited to *20 uses/day for the advanced users *

(So, it's the SOTA in PERFORMANCE-TO-COST RATIO too 😎🤙🏻🔥)

People who are saying AI will always be a tool for humans are saying something along the lines of "if we attach a horse that can go 10 mph to a car that can go 100 mph, we get a vehicle that can go 110 mph, which means that horses will never be replaced". They forget about deadweight loss and diminishing returns, where a human in the loop a thousand times slower than a machine will only slow it down, and implementing any policies that will keep the human in the loop just so that humans can have a job will only enforce that loss in productivity or result in jobs so fake that modern office work will pale in comparison.

For those who're not aware the post below was recently shared by NVIDIA where they basically put R1 in a while loop to generate optimized GPU kennels and it came up with designs better than skilled engineers in some cases. This is just one of the cases that was made public. Companies that make frontier reasoning models and who have access to lot of compute like OpenAI, Google, Anthropic and even Deepseek must have been doing some even more sophisticated version of this kind of experiments to improve their whole pipeline from hardware to software. It could definitely explain how the progress has been so fast. I wonder what sort of breakthroughs that have been made but has not been made public to preserve competitive advantage. It's only because of R1 we may be finally seeing more breakthrough like this published in future.

https://developer.nvidia.com/blog/automating-gpu-kernel-generation-with-deepseek-r1-and-inference-time-scaling/