Machine Learning Street Talk / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
18 published records
01 / prediction
“Right? And I think sort of doing these connections is gonna be very fruitful down the line.”
02 / belief
“While on others, like ARC AGI 2, like this whole sort of semantic evolution seems to be more efficient. So I think ideally we we can get a system that can automatically in some sense decide whether or not it wants to take like a programmatic approach in settings where it's actually feasible and easier to to bootstrap off, or it takes the semantic approach of evolving instructions or like LLM driven input output mappings.”
03 / belief
“I think that you subscribe to the slightly different idea that that we need to be far more open ended and we need to be using evolutionary algorithms and so on.”
04 / belief
“I think something that's critical is that we as humans try to interact with these systems as early as possible in order to actually, like, have influence and ownership over, like, this development process.”
05 / belief
“I think like with the AI scientists in Schinka, we're really trying to to make sure that we can sort of apply the collective intelligence of all of us to to shape how this might look in the future.”
06 / belief
“Exactly. So Sicana has been around for now, like, almost 2 years, like, 1 in 3 quarters, I would say.”
07 / belief
“I think going forward for the bigger innovations and so on, for now, you still need humans, but we're sort of at the GPT 1 moment of of making this sort of a reality and potentially in 10 years, this is gonna look very, very different once the sort of also the infrastructure for it has been built up.”
08 / belief
“I think a lot of sort of analogies from evolution transfer to scientific research, right, in the sense that we traverse a tree of different ideas or different experiments and then in the paper we report 1 path through that tree.”
09 / evaluation
“And I think language models with the right sort of evolutionary hardness are extremely powerful in terms of scaling up to to to make discoveries. And, yeah, I think Jeremy as well as the AlphaEvolve paper as well as sort of work we've done on, like, the Daven Gudel machine, for example, shows that this sort of stepping stone accumulation plus iterative verification and collecting sort of information and evidence from the real world real synthetic evaluator is really important for that.”
10 / recommendation
“Right? So, yeah, I think we need to get the pacing of all of this right and we need to do much more exploration in human machine interfaces, UI UX design and how to make sure that humans sort of fill or feel fulfilled during this experience.”
11 / evaluation
“Right? And I think, actually, like, even though, like, large frontier language models are extreme, like, let's say, black boxes or it's very hard to get a full mechanistic understanding of them, the outputs can be.”
12 / prediction
“I think we discussed this during the last podcast we did where we are using LLMs to design objective functions, and back then, we did it for preference optimization and post training.”
13 / evaluation
“These are problems that have been solved before in in part or in whole, which means when you look at the epistemic tree, many of the building blocks for solving them are very high up in the tree.”
14 / prediction
“Right? So I think going forward, it's gonna be really important to not only sort of do open ended, let's say, optimization of solutions, but sort of do the co evolution of problem and solution together in order to collect even more diverse stepping stones and to really kick off this this open ended process.”
15 / evaluation
“Right? So we're using neural networks because they're incredibly flexible and they understand a lot of things about the world, but they don't have the kind of constraints that we want.”
16 / evaluation
“I think 1 thing that was very interesting about the circle packing problem, sort of also coming back to the problem problem that I discussed initially was that originally, we we used a formulation where the correctness is checked with, like, a very tiny amount of slack.”
17 / preference
“Because you need to leverage like human creativity in this process as well, I think.”
18 / evaluation
“It could it could in fact happen. But I think, like, for all of these things, we started out sort of with the, let's say, most intuitive algorithmic component that we had, and UCB was 1 that really did the job here.”