Prof. West has it right. Human intelligence requires melding intents. Solving mathematical algorithms requires no creativity or shifting of intentions.
On Tuesday, July 20, 2021, Prof David West <profw...@fastmail.fm> wrote: > Thirty something years ago, Alan Newell walked into his classroom and > announced, "over Christmas break, Herb Simon and I created an artificial > intelligence." He was referring to the program Bacon, which fed with the > same dataset as the human deduced the same set of "laws." It even deduced a > couple of minor ones that Bacon missed (or, at least, did not publish). > > Simon and Newell tried to publish a paper with Bacon as author, but were > rejected. > > AlphaFold (which I think is based on a program Google announced but has > yet to publish in a "proper" journal) is, to me, akin to Bacon, in that it > is not "doing science," but is merely a tool that resolves a very specific > scientific problem and the use of that tool will facilitate humans who > actually do the science. > > I will change my mind when the journals of record publish a paper authored > by AlphaFold (or kin) as author and that paper at least posits a credible > theory or partial theory that transcends "here is the fold of the xyz > sequence to address why that fold is 'necessary' or 'useful'. > > davew > > > On Tue, Jul 20, 2021, at 1:12 PM, Pieter Steenekamp wrote: > > A year or so ago, Deepmind's AlphGo defeated the then world Go-champion > Lee Sedol at a time when leading Ai researchers predicted it will be at > least 10 years before AI can reach that level. But the valid question then > was - why so excited? It's just a game. There is an interesting documentary > on youtube about this at https://www.youtube.com/watch?v=WXuK6gekU1Y > > What's happening now is that AI makes scientific discoveries beyond human > ability. > > Is anybody worried where it will end? > > I quote from https://www.nature.com/articles/s41586-021-03819-2 > Highly accurate protein structure prediction with AlphaFold > Proteins are essential to life, and understanding their structure can > facilitate a mechanistic understanding of their function. Through an > enormous experimental effort1–4, the structures of around 100,000 unique > proteins have been determined5, but this represents a small fraction of the > billions of known protein sequences6,7. Structural coverage is bottlenecked > by the months to years of painstaking effort required to determine a single > protein structure. Accurate computational approaches are needed to address > this gap and to enable large-scale structural bioinformatics. Predicting > the 3-D structure that a protein will adopt based solely on its amino acid > sequence, the structure prediction component of the ‘protein folding > problem’8, has been an important open research problem for more than 50 > years9. Despite recent progress10–14, existing methods fall far short of > atomic accuracy, especially when no homologous structure is available. Here > we provide the first computational method that can regularly predict > protein structures with atomic accuracy even where no similar structure is > known. We validated an entirely redesigned version of our neural > network-based model, AlphaFold, in the challenging 14th Critical Assessment > of protein Structure Prediction (CASP14)15, demonstrating accuracy > competitive with experiment in a majority of cases and greatly > outperforming other methods. Underpinning the latest version of AlphaFold > is a novel machine learning approach that incorporates physical and > biological knowledge about protein structure, leveraging multi-sequence > alignments, into the design of the deep learning algorithm. > > > > > - .... . -..-. . -. -.. -..-. .. ... -..-. .... . .-. . > FRIAM Applied Complexity Group listserv > Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam > un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com > FRIAM-COMIC http://friam-comic.blogspot.com/ > archives: http://friam.471366.n2.nabble.com/ > > > -- Sent from Gmail Mobile
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