Folding Forum

I saw one of the papers posted on the news section of the site, discussing the potential for deep learning in drug discovery: http://folding.stanford.edu/2017/07/10/ ... or-pharma/

I was a little surprised by this because I thought that F@H basically did statistical modeling, not machine learning or deep learning. So now I'm wondering, do any of the cores do ML work? Might they in the future?

Right now I would assume no available core is doing ML; they are “just” tools to compute the proteins and ML might happen before and after a WU to steer the initial setup of projects and to analyze the results.
But who knows what is cooked behind the curtain.

In some other forum there was recently some soliciting for a BOINC/FAH style of new AI community : but seems not yet took off. Funny; they asked in multiple distributed computing forums for unused cycles ; made me smile. What is an unused cycle ? Don’t have any.

Haha that is funny.

https://arstechnica.com/civis/viewtopic ... &t=1400871
You will notice he has no goal other than building the largest. He is not planning on solving any of mankind's ills, he just wants to stroke his ego.

I was not kind to to him.

A traditional computer can do some things very quickly and efficiently and is very slow and inefficient at other things. One important difference is whether the goals/questions are highly focus or highly unfocused or somewhere in between. The brain tends to prefer the opposite.

(Read about the Google Cat project. Use 16,000 computers for a month with the goal of "Find the cat in pictures")

If the goal is to train a self-driving car to avoid killing pedestrians, we could turn a lot of them loose on the road and whenever a pedestrian was killed, we's (symbolically) say "Bad Dog" but there are better ways, even though that same technique (maybe using "Good Dog") can teach a puppy to fetch a ball.

FAH doesn't use pure guided/focused searches. It uses a semi-guided technique. The typical project starts with a random sampling of a protein's motions. Then using a Bayesian process, interesting areas for study are separated from uninteresting areas. This makes the focused (sequential) processing much, much more effective. This refinement process may be repeated on a given protein.

FAH researchers have made many, many improvements in the 15+ years that FAH has been going. FAH's management are good about reading research papers or attending conferences about anything that might improve FAH's productivity, whether or not the latest buzz-words are including in the associated title.