The Fellows and Mentors of ICA4 embarked on their last scientific trip to The École Normale Supérieure of Paris. Two back to back scientific sessions were held in the morning, both of which described the current issues related to Artificial Intelligence and offers future perspectives. The first lecture was given by the Director of the École Normale Supérieure (ENS), Marc Mézard, who stressed that we need a better understanding of AI. This was followed by a talk from the famous economist, Philippe Aghion. He discussed the impact of automation on the labour force, and how AI can be used to boost innovation and growth through facilitating the process of creative destruction. Both mentors emphasised a need for a global set of ethical rules and regulations. The day continued with a presentation of ENS and concluded with a discussion session among the Fellows, as they began preparing their presentations for the plenary seminar on the Final Day of ICA4.

Recent Progress and Future Challenges in AI

Presented by Marc Mézard

Mézard is a theoretical physicist with a personal interest in the development of a theoretical framework to explain how AI works, and more specifically how Deep Neural Networks operate. Huge innovations have been made in the predictive power of neural networks. Still, many of the conceptual foundations have been around since the 1980s. By describing the lineage of the technology, Mézard was able to convincingly argue the lack of a theoretical grounding for these networks. How do they work? "We know everything about these networks, but we understand nothing", the speaker provocatively posed.

Notwithstanding the impressive technological innovations in Deep Neural Networks, Mézard raises three main issues:

• The training of the networks still requires vast amounts of data, which is unpractical and a sign that the networks do not mimic the human brain. Humans can already learn and generalize after being exposed to a small set of training material.
• There is still a clear lack of understanding of what is going on in neural networks. The learning mechanism in networks is poorly understood. In other words, there is no way to explain how the machine makes decisions.
• Neural networks are not able to generate representations of the world. Neural networks are extremely adept at making predictions, but they are not able to generate representations of the world. All in, we are still very far from reaching General AI.

Mézard stressed that we need a better understanding of architecture, algorithms, and data structure, which can improve the explainability of AI. In addition, there is a need for a global set of ethical rules and regulations. We need control mechanisms and a global vision of the possible impacts of AI on our societies.

The Impact of AI on the Economy

Presented by Philippe Aghion

The speaker expanded on ways to stimulate research into AI and the development of AI in industry, while also emphasising the need for regulation. Aghion drew from his expertise on economic growth and drew parallels to the role of AI in society and global economies.

AI has already had a considerable impact on society, for example, through the impact of automation on the labour force. While AI has been instrumental in increasing productivity, economic growth has declined since the mid-2000s. Aghion's major concern is focused on the formation of large companies which boosted growth but also inhibited innovation. Much of the innovation related to AI is currently concentrated in such companies. To give AI more potential, we need to rethink ways to stimulate innovation while also making it sustainable. One crucial step is to re-calibrate the relationship between companies, institutions, and civic society. Rethinking funding strategies while also thinking of governmental regulatory measures and increasing the civic engagement with these companies is vital to achieving sustainable growth for companies developed AI.

Both talks showed the complicated interplay between research objectives related to AI and the societal and economic embedding of the technology. Current research is devoting more attention to the challenges that Mézard raised, for example, work on Explainable AI. However, public awareness of the limitations of neural networks and the research challenges is still lagging. Increasing this awareness might help to balance polarized views on AI that often oscillate between dystopian and utopian perspectives. An interdisciplinary project, offered through the 4th Intercontinental Academia, is of utmost importance in shaping how scholars should communicate the current state of AI and its challenges to a broader audience.

Scribe: Melvin Wevers

Chair: Jakub Growiec