[ExI] Can AI Solve Science? by Stephen Wolfram

William Flynn Wallace foozler83 at gmail.com
Thu Mar 7 12:59:49 UTC 2024


Will we be like sheep when the level of accuracy of the ais reaches 99%?  I
think this would be a bad thing.  bill w

On Thu, Mar 7, 2024 at 5:09 AM BillK via extropy-chat <
extropy-chat at lists.extropy.org> 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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