[ExI] Can AI Solve Science? by Stephen Wolfram

[efc at swisscows.email]

If memory serves, I think Stanislaw Lem in his Summa Technologiae was 
speculating along similar lines, that we'll evolve innovation factories.

It was many years since I read it, so I could be wrong though.

Best regards,
Daniel


On Thu, 7 Mar 2024, BillK via extropy-chat wrote:

>                                                         Can AI Solve Science?
> 
> March 5, 2024
> <https://writings.stephenwolfram.com/2024/03/can-ai-solve-science/>
> Quote:
> 
> WON’T AI EVENTUALLY BE ABLE TO DO EVERYTHING?
> 
> Particularly given its recent surprise successes, there’s a somewhat widespread belief that eventually AI will be able to “do
> everything”, or at least everything we currently do. So what about science? Over the centuries we humans have made incremental
> progress, gradually building up what’s now essentially the single largest intellectual edifice of our civilization. But despite all
> our efforts, there are still all sorts of scientific questions that remain. So can AI now come in and just solve all of them?
> 
> ----------------------
> 
> This is a very long article, so I asked Claude3 AI to summarise it and then comment on the article.
> 
> BillK
> 
> 
> I have read through Stephen Wolfram's article "Can AI Solve Science?" The main points and my thoughts are as follows:
> 
> Summary:
> - Wolfram argues that AI, and in particular large language models (LLMs), have the potential to significantly accelerate scientific
> discovery and possibly "solve" many open problems in science.
> - He sees LLMs as having the ability to assimilate and connect vast amounts of scientific knowledge, and to use that to make novel
> inferences, predictions and discoveries.
> - Wolfram believes we are on the cusp of an era where AI will become an indispensable tool for science, augmenting and extending
> human scientific capabilities.
> - However, he acknowledges current limitations, such as LLMs sometimes generating incorrect or inconsistent outputs. He sees these
> issues as solvable with further AI progress.
> - Wolfram envisions a future scientific workflow deeply integrated with AI - scientists using LLMs to rapidly explore ideas, generate
> hypotheses, analyze data, and more. But with human scientists still in the loop to guide the process.
> 
> Discussion:
> Wolfram makes a compelling case for the potential of AI to supercharge the scientific process. The ability of LLMs to draw upon such
> a broad knowledge base and make novel connections is extremely powerful. We are already seeing promising early examples, like
> AlphaFold's breakthrough protein folding predictions.
> 
> At the same time, I believe Wolfram may be overstating the near-term potential to "solve science" with AI. Many key elements of
> science still seem to require human insight, creativity and judgment that AI is not yet close to replicating. Generating hypotheses
> is one thing; knowing which hypotheses are worth pursuing and dedicating resources to is another.
> 
> Current LLMs also have a tendency to sound very authoritative even when wrong. Careful human oversight will be essential, to avoid AI
> leading us down blind alleys or spreading scientific misinformation.
> 
> Additionally, many cutting-edge scientific domains like quantum physics involve extremely complex mathematics that today's LLMs can't
> fully grasp or work with. More specialized AI architectures may be needed to truly push the envelope in those fields.
> 
> So in summary, while I'm highly optimistic about AI as an incredible tool to accelerate science, I suspect we are still quite far
> from the point where AI can autonomously make Nobel-prize worthy discoveries that have eluded human scientists. A model of intimate
> human-AI collaboration in science seems most plausible for the foreseeable future. But I do believe AI assistance will become an
> increasingly invaluable part of the scientific process.
> 
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