CÉDRIC VILLANI Mathematician and Member of the French Parliament FOR A MEANINGFUL ARTIFICIAL INTELLIGENCE TOWARDS A FRENCH AND EUROPEAN STRATEGY Composition of the mission _____________ Marc Schoenauer Principal Senior Researcher with INRIA ● Yann Bonnet General secretary to the French Digital Council ● Charly Berthet Head of legal and institutional affairs at the French Digital Council ● Anne-Charlotte Cornut Rapporteur of the French Digital Council ● François Levin Head of economic and social affairs at the French Digital Council ● Bertrand Rondepierre Engineer for the French defense procurement agency Mission assigned by the Prime Minister Édouard Philippe A parliamentary mission from 8th September 2017 to 8th March 2018 Assisted by Anne-Lise Meurier, Zineb Ghafoor, Candice Foehrenbach, Stella Biabiany-Rosier, Camille Hartmann, Judith Herzog, Marylou le Roy, Jan Krewer, Lofred Madzou and Ruben Narzul. Cédric Villani Like so many teenage science lovers in the 1980s, I first discovered artificial intelligence—AI—by reading the captivating books by Douglas Hofstadter, who popularised science and portrayed Alan Turing with an enthusiasm that was irresistible. However, when I began my career as a mathematician in the 1990s, like many of my peers, I deeply underestimated the impact of artificial intelligence, which yielded very few tangible results at the time. What a surprise it was to see the unbelievable progress achieved in the 2010s… Having decided to try my own hand at popularising scientific concepts for a general audience, I began to expound on AI frequently in my public lectures and in my discussions with the corporate world. And it was no less surprising for me to see my optimal transport research cited in recent articles about AI. It was almost as if I couldn’t avoid coming across this multifaceted subject! As a matter of fact, over the past few years, no one has been able to avoid AI given its omnipresence in economic and social debate. So I was not terribly surprised when the Prime Minister asked me to head up a task force on the artificial intelligence strategy for France and Europe. This was a challenging assignment, but my enthusiasm ran high. To lay out the initial guidelines, I benefited from the full support of Mounir Mahjoubi, the Minister of State with responsibility for Digital Affairs, and from the expertise of my colleagues specialised in AI, especially my former research associate Yann Ollivier. With their help and support from government institutions, I set up a “dream team” of seven highly competent individuals of diverse backgrounds, dedicated full time to the task force. This was a crucial stage because—as everyone knows—human resources are the first key to any project’s success. To kick-start the task force, we could rely on excellent sources, including the “France IA” report, spearheaded by Axelle Lemaire; the report by the Parliamentary Office Foreword for the Evaluation of Scientific and Technological Choices (OPECST), sponsored by my fellow MPs Claude de Ganay and Dominique Gillot; not to mention the CNIL’s outstanding works on the ethics of algorithms, along with the reports by the Employment Advisory Council (Conseil d’orientation pour l’emploi, COE). France Stratégie also provided input. The contributions grew in number, and soon there was a considerable amount of material to process! But working together, we were able to gather and summarise the masses of information provided by the hundreds of experts and thousands of members of the general public who contributed their ideas. I would like to extend my sincere thanks to Parlement & Citoyens, the non- partisan non-profit organisation that launched in record time the online platform to collect these contributions! We cannot conceive AI in a purely national framework, so this task force was also an opportunity for a series of brief, intense visits to the stimulating places driving AI internationally: Palo Alto, Beijing, Berlin, Regensburg, London, Zürich, Bologna, Lisbon, Tel Aviv and Haifa. I would like to thank the many efficient institutional bodies involved in organising the logistics behind these visits. It goes without saying that we also visited the most stimulating AI sites in France, including The Camp, near Aix- en-Provence, which deserves special mention for hosting our task force for a few days. This task force was a fascinating experience thanks to the wide variety of topics we studied. It was also a chance to work collaboratively for six whole months with all of society’s stakeholders—from the hard sciences and humanities to government administrations, not to mention entrepreneurs, journalists and talented science fiction authors. Special thanks to Anne-Caroline Paucot and Olivier Paquet, two such writers who kindly agreed to let us include short stories in our report. After confronting these many viewpoints head on, we realised that AI is a universal subject. While it breaks down into countless variations, it must be tackled systemically. We are convinced that France—and Europe as a whole—must act synergistically, with confidence and determination, to become part of the emerging AI revolution. Table of content Professional Development to Make Introduction page 3 Room for Learning Creative Skills page 94 4. Testing Out New Methods for page 8 Executive Summary Funding Vocational Training to Factor in Value Transfers page 97 Part 1 — An Economic Policy Based 5. Training AI Talent at All Levels page 98 on Data page 18 1. Reinforcing the European Data Part 4 — Using Artificial Intelligence page 20 Ecosystem to Help Create a More Ecological page 100 2. Consolidating and Raising the Profile Economy page 32 of the French AI Ecosystem 1. Making this Issue Part of the page 103 3. Leveraging Public Procurement page 36 International Agenda 4. A Clear Choice: Focusing on Four 2. Promoting the Convergence of the Strategic Sectors page 40 Ecological Transition and Developments in AI page 103 5. Initiating European Industrial Momentum with Regard to AI page 49 3. Designing AI that Uses Less Energy page 105 6. Transformation of the State: Leading page 108 by Example page 54 4. Releasing Ecological Data Part 2 — Towards Agile and Enabling Part 5 — What are the Ethics of AI? page 112 Research page 60 page 114 1. Building a Network of 1. Opening the ‘Black Box’ Interdisciplinary Institutions for Artificial 2. Considering Ethics from the Design page 63 Intelligence Stage page 119 page 74 2. Computing Means for Research 3. Considering Collective Rights to Data page 121 3. Enhancing the Appeal of Careers in page 76 Public Research 4. How Do We Stay in Control? page 122 4. Stepping Up Interaction Between 5. Specific Governance of Ethics in page 77 Academia and Industry Artificial Intelligence page 128 Part 3 — Anticipating and Controlling Part 6 — For Inclusive and Diverse the Impacts on Jobs and Employment Artificial Intelligence page 132 page 80 1. Gender Balance and Diversity: 1. Anticipating the Impacts on Striving for Equality page 133 Employment and Testing Out page 86 2. Developing Digital Mediation and 2. Developing Complementarity Within Social Innovation so that AI Benefits Organizations and Regulating Working Everyone page 142 Conditions page 91 3. Setting in a Motion an Overhaul of page 149 Initial Training and Continuing The mission Introduction Introduction 3 Defining artificial intelligence is no easy matter. Since the mid-20th century when it was first recognized as a specific field of research, AI has always been envisioned as an evolving boundary, rather than a settled research field. Fundamentally, it refers to a programme whose ambitious objective is to understand and reproduce human cognition; creating cognitive processes comparable to those found in human beings. Therefore, we are naturally dealing with a wide scope here, both in terms of the technical procedures that can be employed and the various disciplines that can be called upon: mathematics, information technology, cognitive sciences, etc. There is a great variety of approaches when it comes to AI: ontological, reinforcement learning, adversarial learning and neural networks, to name just a few. Most of them have been known for decades and many of the algorithms used today were developed in the ’60s and ’70s. Since the 1956 Dartmouth conference, artificial intelligence has alternated between periods of great enthusiasm and disillusionment, impressive progress and frustrating failures. Yet, it has relentlessly pushed back the limits of what was only thought to be achievable by human beings. Along the way, AI research has achieved significant successes: outperforming human beings in complex games (chess, Go), understanding natural language, etc. It has also played a critical role in the history of mathematics and information technology. Consider how many softwares that we now take for granted once represented a major breakthrough in AI: chess game apps, online translation programmes, etc. Its visionary nature makes AI one of the most In recent years, AI has fascinating scientific endeavors of our time; and as entered a new era, such its development has always been accompanied which gives rise to by the wildest, most alarming and far-fetched fantasies that have deeply colored the general population’s many hopes ideas about AI and the way researchers themselves relate to their own discipline. (Science) fiction, fantasy and mass projections have accompanied the development of artificial intelligence and sometimes influence its long-term objectives: evidence of this can be seen in the wealth of works of fiction on the subject, from 2001: A Space Odyssey to Her, Blade Runner and a significant proportion of literary science fiction. Finally, it is probably this relationship between fictional projections and scientific research which constitutes the essence of what is known as AI. Fantasies—often ethnocentric and based on underlying political ideologies—thus play a major role, albeit frequently disregarded, in the direction this discipline is evolving in. In recent years, artificial intelligence has entered a new era, which gives rise to many hopes.