Could I revive within me
Her symphony and song
To such deep delight ’twould win me
That with music loud and long
I would build that pleasure dome in air
That sunny dome, those caves of ice
etc. etc.

> On Jul 21, 2021, at 8:41 AM, Frank Wimberly <[email protected]> wrote:
> 
> 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 
> <https://linkprotect.cudasvc.com/url?a=http%3a%2f%2fsimtable.com&c=E,1,RtDS8sTKXJpbu-QPY46tn5LYUPUIM42ANJRngp6qq1wPJewk7zevGDHXsPahW7S4qNHZ-cC08fmTsqXnDyBSUm_P979JBhy-z9PqjXHjEEl_KR6iR7D03Qw,&typo=1>
> 
> Frank
> 
> ---
> 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 <[email protected] 
> <mailto:[email protected]>> 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 <[email protected] <mailto:[email protected]>> On 
> Behalf Of Patrick Reilly
> Sent: Tuesday, July 20, 2021 3:02 PM
> To: The Friday Morning Applied Complexity Coffee Group <[email protected] 
> <mailto:[email protected]>>
> 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 <[email protected] 
> <mailto:[email protected]>> 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 
> <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 
> <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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