Tarun Chitra
@pinged
Part II: AI Doom 🐰 🕳️ ~~~ You may need to see a clinician for AI Doomer-itis if you have any of the following symptoms: - Hate querying (opposite of vibe coding): Finding queries where the LLM is wrong to make yourself feel better - Thoughts of future generations unable to do an integral without internet
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Tarun Chitra
@pinged
When OpenAI O1 came out, my friend Adam (who works on that team) was freaking out about how the world is over because no one will do contest math/IOI stuff anymore I initially was skeptical — after all, ChatGPT could barely answer my "2^29" interview question without puking — but once it came out I was shocked — I could basically one-shot prompt the model into giving me full proofs for papers I was writing Suddenly, hours of work took 30s and a bit of a salty attitude (I always got better performance when I told the model that the proof better be good enough to get into JAMS/ICML/etc.)
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Tarun Chitra
@pinged
This initial realization in October 2024 that things I thought language models could never do — i.e. prove complex properties from very little text — was a real eye opener to me. Yes, getting it to make TikZ or beamer diagrams for me and/or to make sure I get Python/Rust syntax right was *cool* but it didn't feel like higher cognitive power. Yet being able to do full zero-shot learning [theorists have called "oneshotting" zero shot learning for the last 20+ years] 7 years after people wrote abstracts like the following (tl;dr: "zero shot learning is impossible") blew my mind Suddenly, epistemic security seemed to be failing — the model could explain itself without requiring me to understand how it could explain itself. That is, I can figure out when its wrong (like crypto or trading) from how it tells me it reasoned (much like invalidating a ZKP if an aggregation step or Fiat-Shamir fails)
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Tarun Chitra
@pinged
This shook me to my core — I spent years starting in the frequentist philosophy of "anything that you can learn must have an asymptotic theory otherwise you don't know if it exists" to begrudgingly becoming a Bayesian ("who cares about infinite samples?") to accepting reasoning models — where you can accept the "proof of knowledge" generated by the object while being unable to know any possible algorithm for _why_ the proof is correct. There is sense that you can check the proof (e.g. it generates a frequentist proof) but the model itself lacks even Bayesian properties (there is no "prior" for predict next token, only simulation of a context). This felt paradoxical in a manner akin to Russell's Paradox: "the set of explanations generated by a reasoning model cannot itself be described by a reasoning model" and violated every mathematical totem, axiom, and belief I had held near and dear
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Tarun Chitra
@pinged
When OpenAI O3-mini came out, I suddenly found myself going from using a language model for (maybe) an hour a week when writing data science code or on-chain data queries to interrogating my prior papers to figure out if I made mistake in proofs or missed some obvious "lay-up" corollaries This is the type of stuff that takes months of labor, often times with multiple research collaborators who you have to get in sync with. Here, I could "wake and query" — wake up with a shower thought ("why does the non-linear Feynman-Kac equation work?") and ask my "collaborator" O3 and get a 90-95% answer from a very low entropy string
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