New blogpost: Why I think automated research is the means, not just the end, for training superintelligent AI systems.
In pointing models at scientific discovery, we will have to achieve the capabilities today's LLMs lack:
- long-horizon palnning
- continual adaptation
- reasoning about uncertainty
- information-efficient learning
- and creative exploration.
Some of these capabilities may emerge from large-scale training. Others will will require changes in how we implement and train AI systems. I don't yet know how exactly such a training loop would look. So consider this post a conjecture.
But science offers a few unique properties at its foundation:
- large open data
- verifiability
- truth-seeking (instead of power-seeking) incentives.
And thus I think scientific discovery is the ideal successor to internet-scale pretraining. It's not just an application, it maybe the means to building what we're missing. Maybe that's why we have @openai
@GoogleDeepMind @periodiclabs @futurehouse etc. all focusing on it.
In pointing models at scientific discovery, we will have to achieve the capabilities today's LLMs lack: - long-horizon palnning - continual adaptation - reasoning about uncertainty - information-efficient learning - and creative exploration.
Some of these capabilities may emerge from large-scale training. Others will will require changes in how we implement and train AI systems. I don't yet know how exactly such a training loop would look. So consider this post a conjecture.
But science offers a few unique properties at its foundation: - large open data - verifiability - truth-seeking (instead of power-seeking) incentives.
And thus I think scientific discovery is the ideal successor to internet-scale pretraining. It's not just an application, it maybe the means to building what we're missing. Maybe that's why we have @openai @GoogleDeepMind @periodiclabs @futurehouse etc. all focusing on it.