How Human Connection Drives the Scientific Process

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Heidelberg Laureate Forum
Panelists from the "The Scientific Vocation Revisited" session: Jeffrey A. Dean, Harry Collins, Dafna Shahaf, and Volker Stollorz
Panelists from the “The Scientific Vocation Revisited” session: Jeffrey A. Dean, Harry Collins, Dafna Shahaf, and Volker Stollorz

A somewhat surprising theme emerged during the “Scientific Vocation Revisited – Can Future Discoveries be Made by Artificial Intelligence?” session at the 8th Heidelberg Laureate Forum (HLF). The session featured panelists Jeffrey A. Dean (ACM Prize in Computing, Google Research), Harry Collins (Cardiff University), and Dafna Shahaf (The Hebrew University of Jerusalem); while they did discuss the potential impact of AI systems on the process of scientific discovery, they also kept reiterating the importance of human collaboration to making scientific advancements; particularly collaborations that occur face-to-face.

To open the session, moderator Volker Stollorz (Science Media Center Germany) asked Jeffrey Dean why private industry, such as Google and OpenAI, has been able to make such substantial breakthroughs in AI and machine learning. Stollorz cited DeepMind’s Alphafolds, which can predict the shape a protein will fold into based on its amino acid sequence, as one such example. Dean replied that many of these discoveries happen in private companies because they are capable of bringing together many people with diverse expertise, which is necessary to solve these kinds of complex problems.  

Following Dean, Harry Collins, a sociologist of science, discussed the time he spent embedded in a team of scientists working on detecting gravitational waves. Collins argued that this group succeeded in detecting gravitational waves after years of research because of the trust and relationships developed during small group meetings. He said that to emulate this kind of science would require communicative computer systems that are able to recognize trust and moral values. Collins hopes to convince the scientific community not to abandon face to face conferences – in a 2020 article from Physics World, Collins and co-authors Bill Barnes and Riccardo Sapienza write: “The aspects that make science special are all developed through face-to-face socialization and it doesn’t seem to happen naturally over the Internet.”

Next, Dafna Shahaf discussed the role analogy plays in innovation; for example, the NASA radiator that was inspired by origami or the Odon childbirth device, which was created by an Argentinian car mechanic, who came up with the idea after watching a YouTube video on how to remove a loose cork from a bottle. These examples highlight the importance of sharing ideas between people to drive innovation, and many times these ideas are encountered by pure chance. Shahaf argued that we can accelerate innovation by teaching AI how to discover solutions by using analogy to find patterns, and she has done research in this space.  

During the Q&A session, the panelists were asked how they would better align the publication and conference incentive system to better enable scientific advancement if they had magical powers. Shahaf said that, unfortunately, the conference system is broken, especially in very popular areas like machine learning, which gets thousands of submissions for major conferences. In her dream world, Shahaf said she would limit every researcher to a certain amount of submission “tokens” for their lifetime, so that you could only submit what you really thought was your best work. An interesting, though probably unworkable idea. Shahaf’s more serious suggestion was for researchers to submit to something like arXiv, after which conferences would compete to give papers their stamp of approval.

Dean said he agrees that we need to encourage more long-term research and that conferences are currently set up to reward incremental improvements in what we know how to do right now, rather than research that does something totally new. He argued we need to enable more innovative research, and that fewer, but longer-term papers are more useful than incremental innovations. Dean cited the ACM HOTNETS workshop as an example of a computing-centered workshop that focuses on blue-sky ideas. Shahaf agreed with Dean and cited the Innovative Ideas in Data Science (IID) workshop, which she co-organized, as another example. 

While there were more technical discussions of emerging techniques that could allow AI to help make scientific discoveries, ironically, the major theme of the session seemed to really be the importance of human collaboration to the scientific process. You can watch the full video of this session on the HLF Youtube channel.

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Khari Douglas is the Senior Program Associate for Engagement for the Computing Community Consortium (CCC), a standing committee within the Computing Research Association (CRA). In this role, Khari interacts with members of the computing research community and policy makers to organize visioning workshops and coordinate outreach activities. He is also the host and producer of the Catalyzing Computing podcast.

3 comments

  1. In reality, AI-Generated Inventions are, for the most part, AI-assisted inventions that use AI as a kind of search engine that searches the solution space. One example of this are today’s theorem provers. They are able to find evidence for small proof steps using known proof methods and a list of already proven lemmas.

    AI currently lacks curiosity, motivation and even orientation. But the computing power behind AI never tires and doesn’t skip seemingly boring branches of the search space.

    But isn’t it true that even invention and innovation are 90 percent sweat and only 10 percent ingenuity?

    One problem with current research is that really new approaches are rare. Most papers improve and optimize existing solutions. They are published because of these improvements. On the other hand, there are ideas from researchers that can be very valuable and inspiring, but which are not published because they do not immediately lead to the state of the art or even better. This is where the value of communication and collaboration comes in. The exchange of ideas can lead to new ideas, to new approaches that are a merging of several inputs.

  2. The sentence “…private companies because they are capable of bringing together many people with diverse expertise, which is necessary to solve these kinds of complex problems.” applies not only to AI and science, but in general to any company that wants to produce and sell more than just yesterday’s products. In my former job we had a “problem solver group”, different people from different areas of the company, different ages, different education, different skills, different hierarchy levels, but we had one thing in common: We could look at a problem quite openly “sine ira et studio” from all possible sides and look for solutions for it, quasi 7 people and then 8 different proposals. Most of the time we discussed which proposal was the most practicable (time, money, success, operational procedure) and then tried to implement it. At the next meeting the result was discussed, if the problem was not ( well ) solved with it, we discussed again and then mostly tried the former second best suggestion.
    Then the company was sold, the new owner was a large corporation, its structures were “implemented”, the bureaucracy, the formalism and the “number crunchers” took over – and that was it with the group. Such “nonsense” no longer suited the company.

  3. Karl Maier,
    Kleine Firmen werden groß , weil sie nach der besten Lösung suchen.
    Große Firmen haben das nicht mehr notwendig, die suchen nach der preisgünstigsten Lösung.
    Dafür gibt es viele Beispiele. Wenn US_Konzerne deutsche Firmen aufkaufen, dann nur wegen des Know How oder um unliebsame Konkurrenz zu beseitigen.

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