The best day of my life. I love you ❤️
Driving around (where else?) SF dropping off some ✨special packages ✨. Favorite stop so far:
Self-recommending!
What is intelligence? What will it take to create AGI? What happens once we succeed?
The Scaling Era: An Oral History of AI, 2019–2025 by @dwarkesh_sp and @gleech explores the questions animating those at the frontier of AI research.
It’s out today:
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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.
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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”
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Machines of Loving Grace: my essay on how AI could transform the world for the better
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 :
@leopoldasch
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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."
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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.
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@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.
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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/
Why we should pass the ENFORCE Act, and let BIS do its job
Why Leopold Aschenbrenner didn't go into economic research.
From
This was a great listen, and @leopoldasch is underrated
@vkhosla The pace is incredibly fast
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 rethink/transform every business in the next decade. Read "" by @leopoldasch. I buy his assertion only a few hundred people know what is happening.
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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.
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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. 🧵
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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!
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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...🧵
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