能够辅助科研的ai有哪些

summarise key points from lengthy documents.In the last four years,LLMs have beendeveloped beyond expectations and they are becoming applicable to an increasingly wide rangeoftasks.28We expand on the development of LLM and other foundation models in section 3.3.3below.Box 1.1:Examples of AI opportunitiesAI helps piece together the first complete image of a black holeAI can enable scientific discovery.A computer vision model was used to piecetogether the first ever image of a black hole 55 million light years away,combiningimages from eight telescopes around theworld.29AI solves decades old protein-folding puzzleAn AI company based in the UK trained neural networks to predict the structuresof proteins,solving a problem that had long stumped scientists.The predictionsare advancing the field of structural biology:scientists have already used them toprevent antibioticresistance,30advance diseaseresearch,31and accelerate thefight against plastic

[title]沃尔夫勒姆：人工智能能解决科学问题吗？[heading2]科学作为叙事A computer can readily check that this is correct,in that each step follows from what comes before.But what we have here is a very “non-human thing”—about which there’s no realistic “human narrative”.So what would it take to make such a narrative?Essentially we’d need “waypoints” that are somehow familiar—perhaps famous theorems that we readily recognize.Of course there may be no such things.Because what we may have is a proof that goes through “ uncharted metamathematical territory ”.So—AI assisted or not—human mathematics as it exists today may just not have the raw material to let us create a human-level narrative.计算机可以很容易地检查这是否正确，因为每一步都遵循之前的步骤。但我们这里所拥有的是一个非常“非人类的东西”——对此没有现实的“人类叙述”。那么，要怎样才能完成这样的叙述呢？本质上，我们需要某种熟悉的“路径点”——也许是我们很容易认识的著名定理。当然也可能没有这样的事情。因为我们可能拥有的是一个穿越“未知的元数学领域”的证明。因此，无论人工智能是否辅助，当今的人类数学可能只是没有原材料来让我们创造人类水平的叙述。In practice,when there’s a fairly “short metamathematical distance” between steps in a proof,it’s realistic to think that a human-level explanation can be given.And what’s needed is very much like what Wolfram|Alpha does when it produces step-by-step explanations of its answers.Can AI help?Potentially,using methods like our second approach to AI-assisted multicomputation above.在实践中，当证明中的步骤之间存在相当“短的元数学距离”时，认为可以给出人类水平的解释是现实的。所需要的与Wolfram|Alpha所做的非常相似，它会对其答案进行逐步解释。人工智能能帮忙吗？可能会使用类似于我们上面的第二种人工智能辅助多重计算方法的方法。

[title]沃尔夫勒姆：人工智能能解决科学问题吗？[heading2]寻找有趣的事情How far can such an approach get?The existing academic literature is certainly full of holes.Phenomenon A was investigated in system X,and B in Y,but not vice versa,etc.And we can expect that AIs—and LLMs in particular—can be useful in identifying these holes,and in effect “planning” what science is(by this criterion)interesting to do.And beyond this,we can expect that things like LLMs will be helpful in mapping out “usual and customary” paths by which the science should be done.(“When you’re analyzing data like this,one typically quotes such-and-such a metric”; “when you’re doing an experiment like this,you typically prepare a sample like this”; etc.)When it comes to actually “doing the science”,though,our actual computational language tools—together with things like computationally controlled experimental equipment —will presumably be what’s usually more central.这种方法能走多远？现有的学术文献无疑是漏洞百出的。现象A在系统X中进行了研究，现象B在系统Y中进行了研究，但反之则不然，等等。我们可以预期AI，特别是LLMs可以用于识别这些漏洞，并且实际上“规划“科学（按照这个标准）有趣的事情”。除此之外，我们可以预期像LLMs这样的东西将有助于规划科学研究的“通常和习惯”路径。（“当你分析这样的数据时，通常会引用这样那样的指标”；“当你进行这样的实验时，通常会准备这样的样本”；等等）然而，实际上“做科学”，我们实际的计算语言工具——连同计算控制的实验设备之类的东西——可能通常是更核心的。