Lead systems engineer, senior research scientist, and former technical founder (B2C) with 13 years of experience.
Background in quantum computing, digital watermarking, AI data, and computational biology.
Well-versed in algorithm development and translation into production, quantum machine learning, quantum applications, data governance, cloud infrastructure, security/cryptography, HIPAA, full-stack engineering.
--
Looking for founding engineer, technical leadership, or staff/principle engineering roles where innovation meets genuine impact.
Using a PRNG and a seed, you can have paeudo-randomness and determinism. The pseudo-randomness is the key piece, it’s random to the extent that it cannot be distinguished from true randomness in polynomial time classically.
I don’t find the recent spate of blog posts and systems delegating and combining LLMs to get better performance particularly interesting. Especially given that anyone who’s taken an ML 101 course has learned about ensemble methods.
While an LLM isn’t what you’d traditionally consider a weak learner, the theorems on learning systems clearly point to them being so in this context. The feigned surprise at combining them to yield better results seems disingenuous.
Even so, the work to predict which models are best suited for which task, how to delegate, and how to combine their outputs is interesting, especially if you’re placing a cost minimization objective on it. That said, this isn’t too far off from what many AI labs are already doing.
The entirety of "agents" and tool calls is a process of combining LLMs to get better results. Is it the same LLM in many cases? Yes. But it doesn't have to be.
It's the natural move that happened after people realized you couldn't throw away half a century of AI research.
Most of these focus on costs. But it is simply the case that the one-shot output did not scale for harder problems on workflows.
--
Lead systems engineer, senior research scientist, and former technical founder (B2C) with 13 years of experience.
Background in quantum computing, digital watermarking, AI data, and computational biology.
Well-versed in algorithm development and translation into production, quantum machine learning, quantum applications, data governance, cloud infrastructure, security/cryptography, HIPAA, full-stack engineering.
--
Looking for founding engineer, technical leadership, or staff/principle engineering roles where innovation meets genuine impact.
Languages: Rust, TypeScript, Node.js, Python, PostgreSQL, Redis.