Updated: Apr 18, 2018
Emmanuel Macron, the French President, recently held a conference to talk about the country's strategy to develop AI in France and Europe. Here is an overview.
Defining artificial intelligence (AI) is not easy. The field is so vast that it cannot be restricted to a specific area of research: it is more like a multidisciplinary program. Originally, it sought to imitate the cognitive processes of human beings. Its current objectives are to develop automatons that solve some problems better than humans, by all means available.
AI is at the crossroads of several disciplines: computer science, mathematics (logic, optimization, analysis, probabilities, linear algebra), and cognitive science, not to mention the specialized knowledge of the fields to which we want to apply it. The algorithms that underpin it are based on equally varied approaches: semantic analysis, symbolic representation, statistical and exploratory learning, neural networks, and so on. The recent boom in AI is due to significant advances in machine learning. Learning techniques are revolutionary compared to AI's historical approaches: instead of the machine being programmed with the rules that govern a task (often much more complex than one might think), it now discovers them itself.
AI is also developing quickly due to the international “dataization” of all sectors (i.e. big data) and the exponential increase in computing power and data storage capacities. Applications are multiplying and directly affecting our daily lives: image recognition, self-driving cars, disease detection, and content recommendation are some of the many possibilities being explored. The universal nature of AI and its many variations herald a new revolution, with its share of pitfalls and opportunities.
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Developing an aggressive data policy
Many artificial intelligence (AI) strategies start with the collection of large bodies of data.
Data is a key competitive advantage in the global AI race. Digital giants in China, Russia and the United States, which have built up their positions by focusing on data collection and use, have a considerable head start. This asymmetry is clearly visible: for instance, in France, large American platforms capture approximately 80% of visits to the 25 most popular sites every month.
A data policy taking into account AI requirements is therefore essential if France and the European Union wish to attain the goals of sovereignty and strategic autonomy. Although these goals are ambitious, they are necessary steps in the creation of a French and European AI industry.
Data is raw material of AI, and essential for the development of new practices and applications.
01: Encourage companies to pool and share their data The government must encourage the creation of data commons and support an alternative data production and governance model based on reciprocity, cooperation and sharing. The goal is to boost data sharing between actors in the same sector. The government must also encourage data sharing between private actors, and assist businesses in this respect. It must organize for certain data held by private entities to be released on a case-by-case basis, and support data and text mining practices without delay.
02: Create data that is in the public interest Most of the actors heard by the mission were in favour of progressively opening up access to some data sets on a case-by-case and sector-specific basis for public interest reasons. This could be in one of two ways: by making the data accessible only to the government, or by making the data more widely available, for example to other economic actors.
03: Support the right to data portability The right to data portability is one of the most important innovations in recent French and European texts. It will give any individual the ability to migrate from one service ecosystem to another without losing their data history. This right could be extended to all citizen-centred artificial intelligence applications. In this case, it would involve making personal data available to government authorities or researchers. This would be beneficial for three reasons:
It would encourage the creation of new databases for use by public services;
It would give new meaning to the right to portability by supporting improved data circulation under the exclusive control of citizens;
It could be implemented immediately after the European data protection regulation enters into force, without the need for new constraints being introduced for private actors.
Targeting four strategic sectors
Dominant players and emerging countries in the AI field have adopted radically different development models. France and Europe will not be able to claim a place on the global stage if they simply attempt to create a “European Google”.
France must instead draw on its economy’s comparative advantages and areas of excellence, focusing on priority sectors where our industries can play key roles at the global level.
The sectors with sufficient maturity to launch major transformation operations are health, transport, the environment and defence and security.
Why these four sectors?
01They are areas in which France and Europe excel.
02They represent important challenges in terms of the public interest.
03They attract the interest and involvement of public and private actors.
04They require strong public leadership to trigger the transformations.
Efforts must focus on achieving these three goals:
01Implement sector-specific policy focusing on major issues Industrial policy must focus on the main issues and challenges facing our era, including the early detection of pathologies, P4 medicine, medical deserts and zero-emission urban mobility. These issues could be identified by sector-specific commissions in charge of publicizing and running activities for their ecosystems.
02Test sector-specific platforms To support innovation, sector-specific platforms must be created to compile relevant data and organize its capture and collection; to provide access to large-scale computing infrastructures suitable for AI; to facilitate innovation by creating controlled environments for experiments; and to enable the development, testing and deployment of operational and commercial products.
03Implement innovation sandboxes The AI innovation process must be streamlined by creating testing areas (sandboxes) with three characteristics: