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I don't think DM is the only lab doing high-impact AI applications research, but they really seem to punch above their weight in it. Why is that or is it just that they have better technical marketing for their work?
They have been at it for a long time and have a lot of resources courtesy of Google. Asking perplexity it says the alphafold 2 database took "several million GPU hours".
I don't think DM is the only lab doing high-impact AI applications research, but they really seem to punch above their weight in it. Why is that or is it just that they have better technical marketing for their work?
I wish there's some breakthrough in cell simulation that would allow us to create simulations that are similarly useful to molecular dynamics but feasible on modern supercomputers. Not being able to see what's happening inside cells seems like the main blocker to biological research.
Earlier quoted context omitted.
> Azure is #2, behind AWS because Satya's effective and strategic decisions I am going to have to disagree with this. Azure is number 2, because MS is number 1 in business software. Cloud is a very natural expansion for that market. They just had to build something that isn't horrible and the customers would have come crawling to MS.
You could just as easily make the argument that cloud is a very natural expansion for Google given their expertise in datacenters and cloud software infrastructure, but they are still behind. Satya absolutely deserves credit for Microsoft's success here.
Google is not behind capability wise, they are in front of MSFT actually. The customer relationships matter a whole lot more.
When I went to work at Google in 2008 I immediately advocated for spending significant resources on the biological sciences (this was well before DM started working on biology). I reasoned that Google had the data mangling and ML capabilities required to demonstrate world-leading results (and hopefully guide the way so other biologists could reproduce their techniques). We made some progress- we used exacycle to demo…
> Sundar is a really uninspiring leader I understand, but he made google a cash machine. Last quarter BEFORE he was CEO in 2015, google made a quarterly profit of around 3B. Q1 2025 was 35B. a 10x profit growth at this scale well, its unprecedented, the numbers are inspiring themselves, that's his job. He made mistakes sure, but he stuck to google's big gun, ads, and it paid off. The transition to AI started late but…
Google's revenue in 2014 was $75B and in 2024 it was $348B, that's 4.64 times growth in 10 years or 3.1 times if corrected for the inflation.
And during this time, Google failed to launch any significant new revenue source.
I wish there's some breakthrough in cell simulation that would allow us to create simulations that are similarly useful to molecular dynamics but feasible on modern supercomputers. Not being able to see what's happening inside cells seems like the main blocker to biological research.
Naturally, the (AI-generated?) hero image doesn't properly render the major and minor grooves. :-)
So out of my own frustration, I drew this. It's a cross-section of a single base pair, as if you are looking straight down the double helix.
Aka, picture a double-strand of DNA as an earthworm. If one of the earthworms segments is a base-pair, and you cut the earthworm in half, and turn it 90 degrees, and look into the body of the worm, you'd see this cross-sectional perspective.
Apologies for overly detailed explanation; it's for non-bio and non-chem people. :)
https://www.instagram.com/p/CWSH5qslm27/
Anyway, I think the way base pairs bond forces this major and minor grove structure observed in B-DNA.
I don't think DM is the only lab doing high-impact AI applications research, but they really seem to punch above their weight in it. Why is that or is it just that they have better technical marketing for their work?
Well, they are a Google organization. Being backed by a $2T company gives you more benefits than just marketing.
I wish there's some breakthrough in cell simulation that would allow us to create simulations that are similarly useful to molecular dynamics but feasible on modern supercomputers. Not being able to see what's happening inside cells seems like the main blocker to biological research.
The folks at Arc are trying to build this! https://arcinstitute.org/news/virtual-cell-model-state
Personally, I think arc's approach is more likely to produce usable scientific results in a reasonable amount of time. You would have to make a very coarse model of the cell to get any reasonable amount of sampling and you would probably spend huge amounts of time computing things which are not relevant to the properties you care amount. An embedding and graphical model seems well-suited to problems like this, as long as the underlying data is representative and comprehensive.
Naturally, the (AI-generated?) hero image doesn't properly render the major and minor grooves. :-)
When I was restudying biology a few years ago, it was making me a little crazy trying to understand the structural geometry that gives rise to the major and minor grooves of DNA. I looked through several of the standard textbooks and relevant papers. I certainly didn't find any good diagrams or animations. So out of my own frustration, I drew this. It's a cross-section of a single base pair, as if you are looking str…
My graduate thesis was basically simulating RNA and DNA duplexes in boxes of water for long periods of time (if you can call 10 nanoseconds "long") and RNA could get stuck for very long periods of time in the "wrong" (IE, not what we see in reality) conformation, due to phosphate/ 2' sugar hydroxyl interactions.