Communication - Event

A new research chair bringing together economics and artificial intelligence

Date:
Changed on 07/10/2024
The Inria Foundation is launching “Markets and Machine Learning”, a new five-year chair headed by the renowned American researcher Michael I. Jordan, emeritus professor of Computer Science at the University of California, Berkeley. The chair will have five major backers from the business world (Air Liquide, BNP Paribas Asset Management Europe, EDF, Orange and the SNCF), all of whom operate in environments with significant variability and high levels of uncertainty. The chair is geared towards developing a fundamental understanding of algorithms for learning and decision-making, motivated by problems in industry and science, with an eye towards eventual technology transfer. We caught up with Michael I. Jordan to find out more.
Portrait de Michael I. Jordan

Why has this chair been created at a time when artificial intelligence is already fast establishing itself within businesses?

The AI tools that are currently the focus of much attention make predictions based on learning from massive collections of data are impressive and increasingly useful, but they are also very limited.In particular, they are poor at making indicating uncertainty in their predictions, and updating that uncertainty based on prevailing conditions.

Humans do better at indicating their uncertainty, and at sharing both their knowledge and their uncertainty.These are the ingredients that allow humans to be good not merely at individual reasoning, but at building collectives that can solve problems beyond the reach of single individuals, and at achieving outcomes that are desirable for all participants. In short, current machine learning is good at prediction, but not very good at decision-making under uncertainty or at forming collectives.

These are key issues for companies, given that they often operate in highly variable environments, and given that they generally need to partner with other entities to reduce uncertainty and create sustainable markets. Uncertainty and the need for mutual reduction of uncertainty is everywhere, from the choices made by individual microeconomic agents in local contexts to the dynamics of global markets. Predictive machine learning needs to be placed in an economic and collective context.

How did you come to occupy this chair and what will the organisational structure be?

The person who initially came up with the idea was Francis Bach, head of the joint project team Sierra (ENS-PSL/CNRS/Inria) in Paris, that will be hosting the chair, who also has excellent contacts in the industrial sphere. He reached out to me as we go back a long way (see inset “This chair is a boost for everyone within Sierra”) and I have been responsible for overseeing similar projects in the past in the USA that bridge academic research and companies such as Amazon, Google, Microsoft, etc. I determined our scientific objectives and these were enough to get five leading French companies on board.

The chair will operate in the context of a team of PhD students and postdoctoral researchers based in Paris and led by me, in collaboration with Francis and his team. We held a first meeting with our backers in July and we will now be running separate projects with each of them, convening twice a year. This will enable us to devise bespoke solutions based on real-life cases.

 

Photo of Francis Bach and Michael I. Jordan. © Inria / R. Gorce

In your experience, what types of uncertainty are companies faced with?


These are many and varied. An energy provider, for example, will be looking at any given time to produce the cheapest possible electricity as ecologically as possible. There will also need to be enough of it for millions of households, from whom demand will fluctuate. An ecommerce platform will need to interact effectively with buyers and with backend supply chains, with billions of entities on both sides of the market and with conditions that are continually in flux. 

Uncertainty also arises because of asymmetries of information where people are unwilling to provide private information and put themselves at a competitive disadvantage. It also arises because data are biased, due to the fact that agents have their own agendas and act strategically. To manage these kinds of uncertainty, it's necessary to bring together the theory of incentives from economics with the theory of inference from statistics.  It's also important to build mechanisms that are calibrated and trustworthy.

Is it possible to create artificial intelligence tools capable of getting rid of such uncertainties?

Not to get rid of them, no. But it is possible to minimize them and quantify them. This is primarily done using statistical data and machine learning algorithms, which use past events in order to predict or simulate the future. When a network is saturated that creates problems, but it also generates data that can eventually be used to solve the problem. 

The challenge lies in collecting this data from users - every household in France contributes to the national demand for electricity - in a distributed way using algorithms that take into account biases and incentives. This scientific objective is as ambitious as it is new: worldwide, there are a hundred times fewer academic researchers working on this subject as there are working on topics such as large language models.But it's not unfamiliar to researchers in industry.

Not only will our research enable our backers to improve the running of existing systems and services, but it will also help them to create new ones to be launched in the future. Depending on the case, our research will be capable of delivering answers within a few months, or a few years.

There is also a science outreach and knowledge dissemination component to the chair. How do you see this at a practical level?

We have only just got started and the first event, which is still in the planning phase, isn’t scheduled until the spring of 2025. But we will probably take inspiration from what I experienced and observed at Berkeley: events aimed at decision makers, computer science students and anyone else with an interest in science, comprising lectures, poster sessions, workshops, etc. 

Michael I. Jordan, “the world’s most influential computer scientist”

The American Michael I. Jordan is an internationally renowned researcher in artificial intelligence, machine learning and statistics who was dubbed “the world’s most influential computer scientist” by the prestigious journal Science in 2016. Jordan has a close relationship with Europe - he speaks French and Italian - and spent a sabbatical year at Inria in 2013. “That all played a part in my decision to take up this chair”, he explains. “What’s more, I knew that in France I would be able to find top class economists and computer science researchers, in addition to major companies that would be interested in our project.” . »

 

 

 

The career of Michael I. Jordan's career in a few key dates

  • 1980: Master’s in Mathematics, Arizona State University.
  • 1985: PhD in Cognitive Science, UC San Diego.
  • 1988 - 1998: professor at MIT (Massachusetts).
  • Since 1998: professor at the University of California, Berkeley.
  • Since 1991: more than 20 Best Paper Awards won at international conferences.
  • 2003: his best known article (more than 54,000 citations to date) is published in the Journal of Machine Learning Research.

“This chair is a boost for everyone within Sierra”

The chair in “Markets and Machine Learning” is to be hosted by SIERRA, a joint project team involving Inria, the CNRS and the ENS-PSL. Head of Sierra Francis Bach has made no secret of his excitement about this out-of-the-ordinary collaboration. “Michael's reputation made it a lot easier to seek out backers. We are also tackling problems that our scientific community has never dealt with before. We must adapt our algorithms and create new tools in order to factor in multiple economic agents, each with their own specific logic. This chair is a boost for everyone within the team.” It is also a personal adventure for the two individuals involved. Francis Bach completed his PhD in Machine Learning at Berkeley in 2005. His supervisor? Michael I. Jordan. For the past 20 years the two researchers have remained on good terms, running into each other at conferences and organising exchanges of young researchers. “It is generally not recommended to return to work in your PhD supervisor’s team”, laughs Francis Bach. “And yet here he is joining us! But I’m thrilled about this new collaboration. It’s something I’m really proud of.”

Find out more about the chair “Markets and Machine Learning”

Find out more about the research carried out by Michael I. Jordan and Francis Bach

About the Inria Foundation

An offshoot of Inria, France’s national institute for research in digital science and technology, what sets the Inria Foundation apart is its people-centric vision of “giving meaning to digital”. As our world becomes digitalised and our societies are faced with unprecedented chaos and crisis, the Inria Foundation, working alongside the wider ecosystem, is committed to bringing through and developing projects in the general interest that will have a positive impact for humanity, society and the planet, with digital at their core. Four key sectors in which digital plays a vital role have been identified: health, the environment, education and rebuilding trust. Very much a ‘think and do tank’, the Inria Foundation develops and deploys programmes which encompass the major challenges facing society, the biggest concerns for companies and the solutions which digital science and technology can deliver to everyone, while maintaining fertile dialogue with other disciplines and steering clear of techno-solutionism.

The Inria Foundation website: www.fondation-inria.fr