Mark Huang, co-founder of Gradient.ai, discusses catastrophic forgetting and degradation when fine-tuning foundation models on domain-specific datasets.
Insight
Huang: True AI agents require measurable probability improvements per node
“It's like on each stage of the node, you're gonna have to see a marginal improvement in the probability of success for that particular workload because of non-determinism.”
Opinion
Huang: Google's internal AI tooling was far superior to competitors
“Google was using AI for systems before everybody else too, right? They invented a transformer, and their internal set of tooling was just so far superior to everything else. Like, it's really hard for people to go back after seeing that.”
Insight
Huang: RAG versus fine-tuning is fundamentally just meta-learning
“And like, at the end of the day, it's just all meta-learning, right? Like, all we want is, like, the best meta learning workflow or meta learning setup possible to be able to adapt the model to do anything.”
Assertion Open · timeframe May 2025
Huang: PoSE breaks down on needle-in-a-haystack at 500k tokens
“It does start to break down a little bit more on the longer, longer context. So, like, 500,000 to a million it appeared that it doesn't hold as well specifically for, like, needle in the haystack.”
Insight
Huang: Adding one billion tokens cannot teach trillion-token models new knowledge
“All models these days are now double-digit trillions, right? So it's kind of a drop in the bucket if you really think I can just put, you know, a billion tokens in there, and I actually think that the model's gonna truly learn new Information.”
Opinion
Huang: Model merging is polluting open LLM leaderboards
“That is extremely interesting from the developer community, and I want to see more of it except it is, to a certain extent, kind of polluting the leaderboards these days, because it's so targeted, and like, now you can kind of game the metric by just finding a…”