[FRIAM] Can current AI beat humans at doing science?

Tue Jul 20 19:41:20 EDT 2021

When I was in the Robotics Institute (now department) at CMU, Raj Reddy
used to say that a professor would be easy to replace with an AI program.
He felt that a genuinely hard problem would be to develop an intelligent
bulldozer.  That's why I have suggested to Stephen over the years that he
build a miniature bulldozer that could read a topographic map and create
that landscape on the sand table.

The few people who don't know what I'm talking about should see simtable.com

Frank

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Frank C. Wimberly
140 Calle Ojo Feliz,
Santa Fe, NM 87505

505 670-9918
Santa Fe, NM

On Tue, Jul 20, 2021, 5:14 PM Marcus Daniels <marcus at snoutfarm.com> wrote:

> I don’t have the quote handy but I recall the folks at Allen AI talking
> about their hard problems.
>
> Acing the SAT, easy.   Math is the hardest.
>
>
>
> *From:* Friam <friam-bounces at redfish.com> *On Behalf Of *Patrick Reilly
> *Sent:* Tuesday, July 20, 2021 3:02 PM
> *To:* The Friday Morning Applied Complexity Coffee Group <
> friam at redfish.com>
> *Subject:* Re: [FRIAM] Can current AI beat humans at doing science?
>
>
>
> 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 <profwest at 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.
>
>
>
>
>
>
>
>
>
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