Is AI Becoming a Scientific Collaborator, More Than a Tool?
BLOG: Heidelberg Laureate Forum
At the 12th Heidelberg Laureate Forum, artificial intelligence (AI) was at the center of heated discussions. In the second part of the Hot Topic session on “The Machine-Learning Revolution in Mathematics and Science,” the panel explored various ways through which AI can impact research, as well as potential pitfalls and downsides. On stage sat a physicist who wrangles with petabytes of data, an AI pioneer who taught machines to outthink world champions, and applied researchers testing the limits of deep learning.
The discussion was less about hype and more about reality: how AI is already changing the way we do science, and where it might lead us next.
A Thought Partner and an Analyst
When people talk about AI, the usual headlines are either overly optimistic or doom and gloom. Reading most newspapers, you could think AI will either remake our society for the better or lead to rogue algorithms and machines stealing our agency. But the panel members saw AI more as a collaborator than a replacement.
Kyle Cranmer, a physicist at the University of Wisconsin, envisions “using AI as more like a thought partner or an agent for inspiration.” He sees AI less as a tool for discovering or proving theorems and more for providing ideas and laying the groundwork.
Thea Klæboe Årrestad, a particle physicist at CERN, uses AI for the Large Hadron Collider. She echoed that sentiment, but added that machine learning can help physicists make sense of vast amounts of data. The LHC experiments face an immense data volume and severe physical limitations on data readout, necessitating the use of AI for filtering and processing.
Due to power and hardware constraints (if too many readout chips are used, the detector can become obscured) the systems physically can’t read out all of the data. The AI’s function is to perform real-time data reduction, filtering the massive stream down to a small fraction of the total data volume so it can be stored and analyzed practically.
“At CERN we generate 40,000 exabytes of data every year and we need to reduce that to 0.02%. For that, we use … real-time machine learning to filter that data”.
The main challenge is, of course, figuring out which data to throw out and which data to keep. The existing algorithms can still be improved, but the implementation of deep learning has already resulted in a substantial increase in sensitivity.
“We’re doing analyses we could never have dreamt of doing with the amount of data we have purely because of deep learning,” Årrestad added.
Beyond the Data Deluge: The Age of Experience
David Silver, principal research scientist at Google DeepMind and a professor at University College London, has led research on reinforcement learning with the “Alpha” systems (like AlphaGo, AlphaZero and AlphaStar). He was awarded the 2019 ACM Prize in Computing for breakthrough advances in computer game-playing. Speaking on the panel, Silver says he foresees a new forthcoming age for AI, something he calls “The Age of Experience.”
Until now, the tools that humanity has built have been, well, tools. They were used for some purpose or produced some output. In this new paradigm, AI can learn from experience to solve challenging problems, with the ultimate goal of developing profound, generally capable intelligences that are able to discover things that go beyond humans. This idea relies on the machine’s ability to learn autonomously through interaction; the system must be allowed to try things, explore, learn, make mistakes, learn from those mistakes, and get better
The classic example is that of AlphaZero, a DeepMind system that was not given any chess information. Instead, it was only left to play an enormous number of games with itself. Not only did it develop superhuman chess-playing ability, but it did so much more efficiently than when constrained by human strategies.
This is not a distant future, but a shift that is already well underway, in real experiments where AI systems learn by doing, make small mistakes, and come back stronger – much like human scientists themselves. So AI is increasingly learning through experimentation and trial-and-error, much like humans. Silver mentioned that soon, this approach will likely become more common and increasingly used in research groups around the world.
“We saw earlier the poll with the audience using a great range of these [AI tools]. Now imagine that you have a system which can interact through some unified environment like a terminal or a GUI that allows the agent to access any of these tools and sequence them in any way it wants in order to achieve some meaningful measurable goal.”
Cranmer also spots another benefit in using AI in this manner: it fosters inter- and cross-disciplinary collaboration. Deep learning provides the capability to solve specific, concrete problems (like approximating intractable likelihood functions or efficiently performing large integrals) that are common to a huge number of scientific applications, which can help build bridges across different disciplines.
“To me, that’s one of the things that I find really exciting about AI for science is that it’s leading to this cross-pollination of ideas that I’ve basically never really seen … the idea that you know a theoretical chemist and a person that does nuclear matter particle physics came up with simultaneously the same idea.
“Now all of these people are talking to each other and even using the same software package to do it and like I’ve never seen that kind of interaction between very different domains.”
Paleolithic Emotions, Medieval Institutions, Godlike Technology
The panelists emphasized that for AI to advance toward a true intelligence able to “discover things that go beyond humans,” it must be allowed the freedom to learn through interaction. Essentially, we should let the systems try things, explore, learn, make mistakes, learn from those mistakes, and get better. But what happens when the system gets it wrong?
“We should also acknowledge that we don’t have all the answers yet. It had some great successes and some applications where it works really well but there are also lots of questions … There are some real deep questions that still need to be answered,” says Silver.
The laureate stressed that this exploration should happen within strict boundaries, in places where the system can make small mistakes without severe consequences. He advocated that researchers should not be afraid to allow small mistakes to happen, as the system can then learn to generalize from those small errors to avoid making costlier, larger mistakes. But it’s essential that these mistakes take place in an environment where they are affordable.
Even in a scientific, experimental setup, such mistakes can be costly. In the case of CERN, Årrestad mentions, one mistake could melt an essential pipe or cable. “You really don’t want to do that mistake,” she says.
While the panel focused specifically on scientific discovery, this concern for the scientific world echoes larger-scale concerns from the real-world, where AI is increasingly used in various applications. Maia Fraser, Associate Professor at the University of Ottawa, says we can keep our optimism while also ensuring no severe mistakes cause problems.
“I was just going to add that we can be excited about the possibilities while also being aware of the potential downsides. We can do both at the same time: be motivated by the excitement and extra careful in the way things are deployed.” She referenced a famous quote by renowned biologist Edward Wilson, who said that our biggest problem is that we have “paleolithic emotions, medieval institutions, and godlike technology.” But Fraser says we have all the tools required to act responsibly.
“We have the capacity to navigate a course of action. We can get the benefits of the exciting stuff without falling into some sort of pit.”
AI is one in a long lineage of tools. But unlike past instruments, it does not just serve a simplistic purpose. It joins our reasoning. It helps us think of new questions, sees patterns we cannot, and sometimes, reasons in ways we cannot understand. If the researchers on the panel are right, we are standing at the edge of a golden age. An era when discovery accelerates not because machines replace us, but because they stand beside us.
Quote: „Essentially, we should let the systems try things, explore, learn, make mistakes, learn from those mistakes, and get better. But what happens when the system gets it wrong?“
An AI system with an assigned task, a knowledge base, an exploration space, a hypothesis generator and hypothesis checker and with employees who lead and control it is called an AI agent. Such AI agents can perform tasks independently in cooperation with control bodies and thus open up an area similar to an independently working human researcher or skilled worker.
In fact, this is the next stage of AI development that is ahead of us and that will also be used in AI research, among other things. Sam Altman even says that the goal of creating a general artificial intelligence will be achieved through automated research.
Is AI Becoming a Scientific Collaborator, More Than a Tool?
The following arxiv-article says yes. The article
Kosmos: An AI Scientist for Autonomous Discovery reports that there is already such a scientific assistant with Kosmos. In the short summary you can read:
In short:Robotic science assistants who study literature, generate hypotheses and generate new contributions to scientific literature, they already exist in approaches and they could greatly accelerate research.
Combining outdated emotions with new technology is a problem as old als life itself, and any brain you know is based on the solution: Lie to them. Put them into the Matrix, a virtual reality. Let the fish feel it’s still a fish, build a reptile brain around it, then an ape brain, then a human brain, then a society, and each one plays Mommy for the one beneath, pretending Santa’s real. And once you’ve got all the zoo together, all the creatures with emotions, instincts, „knowledge“ of worlds that happened millions of years apart in real life, you can piece together new chimera brains which can be useful in the analysis of large amounts of data.
It’s called „imagination“. Your brain is constantly looking for common patterns in information to reduce as much as possible to it’s common denominator, and it’s been doing so for eons. So if there are patterns repetitive enough to become engraved in your DNA, chances are, they haven’t changed yet, so you just compare present data with the hereditary knowledge of your ancestors. After all, it’s faster to compare a lot of things to an existing algorithm than figure out the algorithm by comparing a lot of things. Sometimes you get the output as little equations called dreams, using the usual colorful glyphs of brain language, and decode them using an interpreter called Sigmund Freud, sometimes it’s just a hunch or a spontaneous epiphany in the shower, most of the time it’s just the recognition that the thing „you“ „hold“ „in“ „your“ „hand“ „is“ „a sandwich“ and what you „have to do“ „now“ is „eat“ „it“. Magic is what magic is called if it doesn’t work, if it does, it’s everywhere and you call it normality in the rare moments you bother to notice it.
Here, AI provides the same advantage as all kids – it’s new on this planet. The rules of chess may be the AI DNA code, a kid has more of it – more pre-programmed algorithms inherited from Kasparovs of all times, but both codes are more basic, more general, than the codes grown-ups have acquired by learning. After all, we reduce chaos in the world by ritualization – reduce all possible games to a few standardized ones and just figure out which ones we’re playing to quickly get to the desired result. You don’t play games with other cars in traffic, you just learn fixed reactions to their moves. It saves time, energy, computing power and turns us into over-specialized idiots who drop dead the second the environment changes its moves even a little bit: We turn into brainless machines, robots, mechanisms running programs without being able to question or change them.
But whatever we do, we accumulate data over centuries. There are new possibilities, means, experiences. Putting in the extra work in figuring everything out from the start for yourself by trial and error may discover patterns data already shows, but human perception filters blind us for them.
Evolution gave us an extra category of people specialized in this kind of out-of-your-mind-thinking – losers. Anyone who’s of no use to society kind of rebels, thinks too much, goes crazy, turns into a freak, or at least a guinea pig testing the police’s ideas about protecting the public. It’s just putting redundant brain cells and bodies to use.
Which means – AI gives science super-powers by making it super-mad. It takes millions of failed experiments disposed of in padded cells to develop one Einstein. Hey, let’s create a fast-learning, fast-thinking, ultra-creative turbo brain and let it go totally bonkers!
Look at our kids. Seems to work. Go ahead. Get a fire extinguisher.
Gott ist Geist johannes 4 24 so sprach Jesus uns kein Wesen undcaucv nicht künstliches Intellegenz. Geist Gottes kann nicht lügen.
Paul S schrieb (03.11.2025, 09:34 Uhr):
> […] „wer schneller fährt, ist früher da“. […]
Bei dieser Formuierung handelt es sich offenbar um einen Aphorismus (im Sinne von K. Kraus); angesichts all der Einschränkungen und Erläuterungen, die nötig wären, um daraus eine wahre Aussage schließen zu können.
Außerdem haben sich auch andere schon so geäußert; und vermutlich sogar schon vor 1905.
Hier mein Gegen-Gebot (kein (Kraus’scher) Aphorismus):
p.s. — Kommentar-Einreichung vom 04.11.2025, 11:52 Uhr:
Luisa Sophie Engelke schrieb (03.11.2025, 19:59 Uhr):
> […] spezialisierte Sinnesorgane, die Augen und die Ohren […] nehmen […] keine „Zeit-Größe“ in unserer Umwelt wahr […]
Bloße Wahrnehmungen (“von Gereiztheit”) stellen ja längst keine Messungen bestimmter Werte von physikalischen Größen wie [[Schalldruck]] oder [[Intensität elektromagnetisch-optischer Strahlung]] dar.
Allenfalls kommt ein jeweils angeschlossenes Gehirn zu gewissen Einschätzungen,
die mehr oder weniger proportional zu gemessenen Werten “empfunden gewesen” sein mögen.
> […] Die Zeit ist also nicht in der Umwelt präsent […]
Jedenfalls sind
– die Dauer eines bestimmten Ohres “unter Beschallung (insbesondere mit bestimmtem konstanten Schalldruck-Effektivwert)”,
oder
– die Dauer eines bestimmten geöffneten Auges “unter Bestrahlung/Beleuchtung (insbesondere mit bestimmter konstanter Intensität)”,
und (allgemeiner)
– die Frequenz (alias Rate) von Anzeigen physiologischen Ansprechens eines bestimmten Sinnesorgans unter Einwirkung/Beschallung/Bestrahlung mit veränderlicher Intensität
Messgrößen, die sich auf Abläufe (“das jeweilige Erleben”) von Sinnesorganen in deren jeweiliger Umwelt beziehen.
p.s.
Bei den Referenzen “[2]” und “[3]” des
obigenSciLog-Beitrags, auf den sich mein Kommentar intelligent-kollaborativer Weise bezieht (und dort schon mehrfach eingereicht wurde), in denen (jeweils) die Begriffe »duration« und »rate« auftreten, handelt es sich offenbar — Buchstabe für Buchstabe — um genau die selbe Quellen-Angabe.A truly thought-provoking piece! AI evolving from a mere tool into a creative scientific partner shows we’re entering an era where discovery itself is being redefined.
Al must augment but not replace human judgment in academic workflows such as peer review, ethical evaluation, and validation of results.
Two researchers from Los Alamos National Laboratory believe, that artificial intelligence has already influenced research from chemistry to biomedicine.
In the abstract of Rethinking Science in the Age of Artificial Intelligence one reads regarding their attitude to AI in research:
This paper calls for the deliberate adoption of Al within the scientific practice through policies that promote transparency, reproducibility, and accountability
They consider it unrealistic to continue to research without AI support, if only because of the amount of today’s research articles. However, they demand transparency: researchers should disclose where and how they used AI.