AlphaFold: Using AI for Scientific Discovery
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AlphaFold: Using AI for Scientific Discovery
1–10 of 23 posts
Re: AlphaFold: Using AI for Scientific Discovery
#2A colleague of mine in chemistry gave his thoughts on AI for protein folding recently: “things keep getting better, but they’re nowhere close to being good”.
I think part of the issue at play here is the cost of confirming success, not simply the cost of generating a plausible solution. In most domains of AI that have shown success, the cost of confirmation is trivial (look at the image and check the label) whereas the cost of generating a plausible solution was high.
Like many AI fields, I believe that the real breakthrough will not be a direct approach, but an approach the solves the most pressing barrier to using AI in the first place.
Re: AlphaFold: Using AI for Scientific Discovery
#3A bit off topic, but what the hell does the word "Alpha" mean in their marketing? Are they just going to call every AI they create from now on "Alpha_"
Re: AlphaFold: Using AI for Scientific Discovery
#4A colleague of mine in chemistry gave his thoughts on AI for protein folding recently: “things keep getting better, but they’re nowhere close to being good”. I think part of the issue at play here is the cost of confirming success, not simply the cost of generating a plausible solution. In most domains of AI that have shown success, the cost of confirmation is trivial (look at the image and check the label) whereas t…
> but an approach the solves the most pressing barrier to using AI in the first place.
what do you think is the "most pressing barrier to using AI"?
Re: AlphaFold: Using AI for Scientific Discovery
#5Eagerly waiting for CASP14 in 2020. CASP is a biennial competition. AlphaFold participated in CASP13 in 2018.
Re: AlphaFold: Using AI for Scientific Discovery
#6A bit off topic, but what the hell does the word "Alpha" mean in their marketing? Are they just going to call every AI they create from now on "Alpha_"
It probably has something to do with the parent company being named Alpha_Bet.
Re: AlphaFold: Using AI for Scientific Discovery
#7A bit off topic, but what the hell does the word "Alpha" mean in their marketing? Are they just going to call every AI they create from now on "Alpha_"
AlphaStar didn't really use any techniques from AlphaGo either, so the answer is 'yes'. Anything sufficiently whizzy will get the 'Alpha' prefix for branding.
Re: AlphaFold: Using AI for Scientific Discovery
#8For those interested, it appears as though David Baker (who dedicated his life to protein folding) has also turned to deep learning. His lab recently published https://www.biorxiv.org/content/10.1101/846279v1, which seems to outperform Alphafold with a very concise architecture. Code and model is at https://github.com/gjoni/trRosetta
Re: AlphaFold: Using AI for Scientific Discovery
#9For those interested, it appears as though David Baker (who dedicated his life to protein folding) has also turned to deep learning. His lab recently published https://www.biorxiv.org/content/10.1101/846279v1 , which seems to outperform Alphafold with a very concise architecture. Code and model is at https://github.com/gjoni/trRosetta
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Re: AlphaFold: Using AI for Scientific Discovery
#10A colleague of mine in chemistry gave his thoughts on AI for protein folding recently: “things keep getting better, but they’re nowhere close to being good”. I think part of the issue at play here is the cost of confirming success, not simply the cost of generating a plausible solution. In most domains of AI that have shown success, the cost of confirmation is trivial (look at the image and check the label) whereas t…
> but an approach the solves the most pressing barrier to using AI in the first place. what do you think is the "most pressing barrier to using AI"?
From my perspective, in the life-sciences space, it’s certainly the lack of high quality data. To be more specific, there’s tons of data, but it’s sloppy, lossy, and non standardized.
Many of those discussing the promise of near future AI are either academics or new to the field. The real data landscape is so much worse than they would imagine it is.