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

Tue Jul 20 19:47:09 EDT 2021

It is intelligent because it knows who ought to get bulldozed?   Yes, it could solve so many problems these days!

From: Friam <friam-bounces at redfish.com> On Behalf Of Frank Wimberly
Sent: Tuesday, July 20, 2021 4:41 PM
To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
Subject: Re: [FRIAM] Can current AI beat humans at doing science?

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<http://simtable.com>

Frank
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Frank C. Wimberly
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505 670-9918
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On Tue, Jul 20, 2021, 5:14 PM Marcus Daniels <marcus at snoutfarm.com<mailto: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<mailto: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<mailto: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<mailto: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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