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The ability to train a model on the experiences of many parallel copies of itself is somewhat analogous to the invention of writing. Both dramatically expand the amount of knowledge that can be accumulated by a single mind.

cramming more cognition into integrated circuits openai.com/index/gpt-4o-m…

What are the most important things for policymakers to do on AI right now? There are two: - Secure the leading labs - Create energy abundance in the US The Grand Bargain for AI - let's dig in...🧵

Google DM graded their own status quo as sub-SL3 (~SL-2). It would take SL-3 to stop cybercriminals or terrorists, SL-4 to stop North Korea, and SL-5 to stop China. We're not even close to on track - and Google is widely believed to have the best security of the AI labs!

How well can LLM agents complete diverse tasks compared to skilled humans? Our preliminary results indicate that our baseline agents based on several public models (Claude 3.5 Sonnet and GPT-4o) complete a proportion of tasks similar to what humans can do in ~30 minutes. 🧵

On average, when agents can do a task, they do so at ~1/30th of the cost of the median hourly wage of a US bachelor’s degree holder. One example: our Claude 3.5 Sonnet agent fixed bugs in an ORM library at a cost of <$2, while the human baseline took >2 hours.

I am awe struck at the rate of progress of AI on all fronts. Today's expectations of capability a year from now will look silly and yet most businesses have no clue what is about to hit them in the next ten years when most rules of engagement will change. It's time to

Vinod Khosla The pace is incredibly fast

Why Leopold Aschenbrenner didn't go into economic research. From dwarkeshpatel.com/p/leopold-asch… This was a great listen, and Leopold Aschenbrenner is underrated

I’ve been doing a lot of listening about AI and not much writing for the past few months and it’s convinced me the framing of doomers vs optimists is substantially mistaken as to the real stakes and crux of the argument. slowboring.com/p/what-the-ai-…

Why we should pass the ENFORCE Act, and let BIS do its job city-journal.org/article/keepin…

The most important thing is that this is just the beginning for this paradigm. Scaling works, there will be more models in the future, and they will be much, much smarter than the ones we're giving access to today.

Today, I’m excited to share with you all the fruit of our effort at OpenAI to create AI models capable of truly general reasoning: OpenAI's new o1 model series! (aka 🍓) Let me explain 🧵 1/

OpenAI o1 is trained with RL to “think” before responding via a private chain of thought. The longer it thinks, the better it does on reasoning tasks. This opens up a new dimension for scaling. We’re no longer bottlenecked by pretraining. We can now scale inference compute too.

what it looks like when deep learning is hitting a wall:

OpenAI's o1 "broke out of its host VM to restart it" in order to solve a task. From the model card: "the model pursued the goal it was given, and when that goal proved impossible, it gathered more resources [...] and used them to achieve the goal in an unexpected way."

Leopold Aschenbrenner’s SITUATIONAL AWARENESS predicts we are on course for Artificial General Intelligence (AGI) by 2027, followed by superintelligence shortly thereafter, posing transformative opportunities and risks. This is an excellent and important read :

The “compressed 21st century”: a great essay on what it might look like to make 100 years of progress in 10 years post AGI (if all goes well). Favorite phrase: “a country of geniuses in a datacenter”

When will AI systems be able to carry out long projects independently? In new research, we find a kind of “Moore’s Law for AI agents”: the length of tasks that AIs can do is doubling about every 7 months.

Humans are remarkably adaptable. We foraged and farmed, we built factories and spaceships, we wrote prayers, plays, poems, novels, and code. And now? Now we created this.