used_to_race
Senior Member
- Messages
- 193
- Location
- Southern California
I wonder if she would have better, faster, or more insightful results of she uses A.I. (artificial intelligence)/Deep Learning and AI-chips (or GPUs) instead of super-computing. It's the latest stuff coming out of Silicon Valley and is supposedly more efficient in pattern recognition and optimal solution seeking. I'm far from an expert, but perhaps someone on here knows more about that field and could provide some insight.
I work in Machine Learning/AI and have a couple recent publications, but am far from an expert. The issue is that you need a lot of data. For medical stuff the data requirements actually seem to be a bit less than what I'm used to because our bodies aren't changing at high frequency I guess. You really don't need "supercomputers" to do the kind of stuff they mention, and I have no idea why Klimas puts a picture of some folks standing in a server room in her presentation for "0.3TB of data". I'm not even sure what she means when she says the 7000 CPUs "generate" all this data. It's kind of laughable to be honest. A modern laptop with a GPU and lots of RAM will do okay with some deep learning tasks. It's nice to have maybe a single server with some GPUs but this would be far from a supercomputer. The machine I use at work has 8 high end GPUs, about 700GB of RAM, and (I think) 56 logical CPU cores, and 7 or 8 engineers and data scientists are able to use it for simultaneous tasks, but we just bought a second.
Ultimately it goes back to having a lot of data and being confident that it's labeled correctly. The software makes everything else pretty easy. I see how Stanford is generating large datasets, but nobody is looking at using ML yet as far as I know.
It's funny they talk about biomarkers in these complex diseases, but you could train a classifier to identify other autoimmune diseases like Crohn's, UC, RA, and others maybe 90-95% of the time without a powerful machine or even a deep learning approach. Here's a paper where they did that.