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
10 published records
01 / belief
“I think the right way to think about this is it's a starting point for biological research.”
02 / evaluation
“I mean, AlphaFold itself is this kind of, I guess, I now say landmark in AI and science, but it's really about how do we use AI to solve problems that humans can't, that are really hard, that we go and we do years long experiments.”
03 / preference
“But after AlphaFold 1 and then kind of all the protein specific bits were kind of wrapped around the machine learning. And so the I would say alpha fold 2 was, let's build the science instead of building the science of image recognition and then applying it to proteins because, you know, human visual the human visual system is exactly what we needed to fold proteins is not something true.”
04 / preference
“In my own research on on drug discovery, so I use Alpha 4 in terms of solving structures of cryo EM data, and I also utilize that to to to map out the mechanisms of the proteins.”
05 / evaluation
“I mean, the 1st I will say is that the hardest part of drug development is that we do not know how biology works very well, right?”
06 / belief
“I think AlphaFold 3 diffusion is similar, and it's especially similar because, in fact, in images, okay, you start generating an image and you see especially these early trained diffusion models generate kind of colored blobs, and they start to decide what those colored blobs mean.”
07 / prediction
“We predict nature level science with the press of a button in a very narrow category of nature level science of the structure of a specific protein.”
08 / commitment
“We are a predictor of this experiment that you did all the time and took you a year. And so in a certain sense, I think and so we have validity in that I can characterize very well how well we're we we will reproduce that experiment.”
09 / evaluation
“Even that, I think you can argue, maybe not entirely the story, but it's definitely not the story for proteins because that's the hardest problem is the large scale structure.”
10 / prediction
“There are now structural biologists around the world that can innovate and build new products and save lives potentially because they have access to this protein database.”