Public evidence record
Published podcast speaker
Claim ledger
18 transcript-backed records
01 / evaluation
“We did a simple calculation—which amazingly gives the correct result. This polygenic predictor that we built (which isn't perfect yet but will improve as we gather more data) is used in selecting embryos today.”
02 / prediction
“Pretty confident in 10 years or so, with the CRISPR gene editing technologies will be ready for massively multiplexed edits. We'll start navigating in this high-dimensional space as much as we like.”
03 / commitment
“I mean, when someone confronts me with that, I will acknowledge it as a legitimate concern.”
04 / commitment
“From the venture guys' perspective, I will just say this: one out of 10 babies in Denmark is born this way.”
05 / uncertainty
“What happens is that if you train a predictor in this ancestry group and go to a more distant ancestry group, there's a fall-off in the prediction quality. Again, this is a frontier question, so we don't know the answer for sure.”
06 / evaluation
“In other words, if you live close to a McDonald's and you're able to afford all the food you want, then the height phenotype becomes super heritable because the environmental variation doesn't matter very much.”
07 / commitment
“I may not see all of it, but I'll see the technology perfected; I won't necessarily see this impact on society.”
08 / belief
“The funny thing is the guy you're talking about (guy who solved the physics problem in his mind), whose name I believe is Asana, is a Sri Lankan guy.”
09 / recommendation
“You shouldn’t internalize whatever happens if you get rejected, because that hurts.”
10 / prediction
“I don't know if you’ve ever done deep learning or high-dimensional optimization, but in that high-dimensional space, you're often moving on a slightly-tilted surface.”
11 / prediction
“” The most direct application of this is in the following setting: Every year around the world, millions of families go through IVF—typically because they're having some fertility issues, and also mainly because the mother is in her 30s or maybe 40s.”
12 / prediction
“Let me point out that most of the diseases we care about now—not the rare ones, but the common ones—manifest when you're 50-60 years old.”
13 / commitment
“The very first paper we wrote on this was to prove that using accurate genomic data and these very abstract theorems in combination could predict how much data you need to “solve” individual human traits.”
14 / preference
“Don't you want to know how the world actually is if we have an inequality problem because some people don't do well in school, don't you want to give those families these resources so they can fix it for the next generation?”
15 / evaluation
“Nobody will be able to get in there very easily when they come later because we're developing trust and an extensive track record with clinics worldwide—and we're well-known. So could 23andme or some company with a huge amount of data—if they were to get better AI/ML people working on this—blow us away a little bit and build better predictors because they have much more data than we do?”
16 / preference
“I'm not even sure he would disagree with the quantitative things I just mentioned to you. But, I just want to say some positive things about Gwern because I like to read his stuff.”
17 / prediction
“I think we’re going to evolve to a point where we are going to be out of the wet part of this business and only in the cloud and bit part of this business.”
18 / prediction
“Once we have my prediction from this earlier math that I was talking about, when you get to a border a million, it could be 1 million or 2 million well-phenotyped people and genomes, we would be able to build a pretty decent IQ predictor that might have a standard error of maybe 10 points or something.”