Jonathan Siddharth
@Jonsid
Founder and CEO, Turing.
Accelerating superintelligence to drive real economic progress.
4.9K Following 8.1K Followers
To get to ASI we likely need auto-meta-research, not just auto-research.
Auto-research hill climbs within the current recipe. Minimize pretraining loss, maximize post-training evals.
Auto-meta-research defines new objectives. An outer loop that searches across paradigms. Outside deep learning, maybe even outside gradient descent. Not just scaling transformers + RL.
The inner loop optimizes the recipe. The outer loop questions the recipe.
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Turing's focus right now:
1.Automate AI research
2.Automate engineering
3.Automate knowledge work
4.Automate scientific discovery
The models come from frontier labs. The training signal comes from us. Data, evals, and RL environments built from real workflows. Hard enough that today's best models still fail.
AI research sits at the top for a reason. Automate that, and everything below it compounds.
If you're training models toward any of these four and need environments that don't saturate, DM me.
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