On Wednesday 6^th November, the European Leadership Network (ELN) and Salamanca Group, co-hosted the latest in their Strategic Insight Breakfast series on “Europe’s stand in the AI Race.”

The European Leadership Network’s Strategic Insight Breakfasts are aimed at our individual, corporate and diplomatic members and partners and bring together London’s leading diplomats, politicians, businesspeople and policy-makers to share their insights on the most pressing strategic concerns facing Europe. In partnership with Salamanca Group, these breakfast seminars are one of London’s premier event series on international affairs.

Panellists

• Lord Des Browne, Chair of the ELN and former Defence Secretary
• Dr. Ulrike Franke, Policy Fellow at the European Council on Foreign Relations
• Ollie Whitehouse, CTO of NCC Group research and science advisor to UK Government on Cybersecurity

Companies in attendance

Adarga, Idemia, Improbable, Lockheed Martin, Innovate UK, RBS, White & Case, Royal Aeronautical Society

Synopsis

With a global emphasis on AI development, it is crucial to question Europe’s position in this so-called race as China and the US seem to be quickly advancing in this field.

Although it was correctly pointed out that there is still a long way to go before AI will be commercially viable, this breakfast discussed its concerns with Europe’s capacity to become an AI leader by analysing its stance in comparison to the US and China through the following key components: talent, data collection, and compute (or the hardware). Through this dialogue, possible solutions were noted on how Europe can accelerate in this ‘race.’

Key takeaways

• The US and China are betting on machine learning developments, and in particular on deep learning, attracting talent and funding as strategies to further develop AI, thus placing them in a position to ‘win’ that so-called race.
• By assuming that the ‘AI race’ can be won, it implies that there possibly will be a moment in which we can stop developing technology and advancing humankind.
• Collaboration between government, business, and academia will be crucial to realise new opportunities and mitigate risks, particularly as AI becomes ever more relied upon.

The US

• The US leads on attracting talent by having great universities, funding tech programmes, and having direct access to students.
• Despite the fact that almost half of the global AI workforce is in the U.S., they are still struggling to meet demand.
• The US through the Defense Advanced Research Projects Agency (DARPA) has access to greater and more diverse data.
• On the hardware front, the US also dominates, as it leads the global chip market, with China working on reducing the gap.

China

• Invests comparatively more into R&D employees than the EU or US but loses researchers and developers to the US.
• Although not as diverse as the US’ data, they are still able to extract big data due to its population size.

Europe

• Lags due its fragmented market with stricter regulations protecting the rights of the individual.
• Despite having great universities, it struggles to maintain its talent, especially those transitioning from academia to the ‘founders world.’
• Europe is heavily dependent on the US for hardware.
• More focus should be given to Europe on its role within AI, although it tends to concentrate on the challenges instead of the opportunities around it.
• It was noted that The European Regulatory landscape can be an obstacle for European AI companies, particularly regarding Direct Foreign Investment, which can restrict companies from hiring talent and acquiring data from overseas. This can yield issues around the sovereignty of data.
• The European model is more conservative than the US approach and more concerned with the rights of the individual than China.
• Europe’s role in the development of AI can be to use its regulatory power to convince the other players to commit to an ethical framework from which AI can be developed.

Risk

• With our growing reliance on AI, the European regulatory landscape could negatively impact innovation and leave Europe behind in the AI race.
• There is a strong concern centred on ‘adversarial machine learning’ mechanisms, especially when there is a growing dependence on it for defence. For instance, if a heavily relied upon dataset has been manipulated without our knowledge, how can the AI unlearn this and return to a good state?

Conclusion

Europe is home to strong, world-leading, fundamental research in AI and is known for a robust ethical background and respect for human rights. If these are put at the core of their advances, it will lead to great breakthroughs that can truly bring AI forward in ways that are both financially profitable and, crucially, promote human and environmental well-being.