This is video of my talk at the University of Chicago Conference on Genetics and Behavior back in April. Slides -- they are not very readable in the video. Here's another link to the talk on the Chicago page.
About Me
- Steve Hsu
- Senior Vice-President for Research and Innovation, Professor of Theoretical Physics, Michigan State University
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9 comments:
I really liked how you stressed that linear approximations work just fine. I also liked that interjection you had during the Q&As following one of the two presentations by Durlauf. You reminded him for all the talk about complexity, non-linear effects, etc. animal & plant breeders do just fine with linear models. I know you have to be polite but your remark just took the sails out of that long-winded talk by Durlauf...
Find the slides "above". Where is that?
I too liked the absence of "fugeddaboutit". That is exactly the right way to argue. Most people (hell, bright academics even) are not going to bother to read enough about an abstruse subject like molecular biology to know that transgenerational epigenetic inheritance (TEI) is almost certainly not an issue. He should have just said that TEI would look like lamarckianism on the surface and no-one was ever able to find any evidence of such phenomena, so TEI is very unlikely to exist.
(I am referring to GCs presentation btw: https://www.youtube.com/watch?v=w3310KWlDXg&feature=youtube_gdata)
Audiences will believe in the person that seems the most sensible, and if you just flat out deny that something is possible, you will seem close-minded even if you are right.
Nice presentation. But the market is profoundly dumb, as are human tastes. Will ticklish professorial humour replace real humor? That's a concern of mine.
Animal and plant breeders can get their crops really big, or their cows to make a lot of milk. I'm not sure how this proves we can do the same thing to increase intelligence 10-25 SDs.
Also, even if we could flip a certain set of SNPs and get a reliable set of increases, that's not an actual understanding of the "architecture of intelligence." That's the equivalent of finding that your monitor shines brighter when you hit it from the side at a certain angle. You don't actually know what makes the monitor shine, or how it really works..
Human intelligence is basically our ability to understand our environment. I expect that we'll be trying to understand the brain and human intelligence for decades to come. But maybe we can do some crazy stuff with the very partial understanding we develop on the way.
Verifying approximate linearity is by itself a big step in understanding the genetic architecture. However, I would be shocked if it weren't the case, based on the arguments I gave in the talk. If you listen carefully, someone from the SSGAC comments during my talk that they already have some empirical evidence for linearity; estimates of non-additive and additive variance can also be made from sibling/adoption studies.
Understanding actual molecular mechanisms for how the brain works will, as you note, be much harder and take much longer.
But plant and animal breeders also do not understand all the molecular mechanisms related to the phenotypes they select for. It does not prevent progress.
It is really frustrating to try to imagine what is on the screen. You could publish the slides on this blog as simple JPG images and the readership could line them up at the appropriate point in your talk. Or you could get some volunteer to create a new composite that integrates the talk and the slides.
There's a link to my slides on the post above. Just open the link in another window and you can toggle back and forth to the video.
I like the notion of "linear combinations of nonlinear gadgets".
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