Regarding artificial intelligence in Europe, this week, the Expert group on the economic and societal impact of research and innovation (ESIR) published the policy brief A European model for artificial intelligence. The paper comes of great relevance because it addresses the need for a coordinated European response to the challenges of AI. Its main recommendations relate to increased investment, policy alignment, and skills development to use AI effectively for societal good, while also ensuring it aligns with societal transitions and environmental sustainability.
To build a cohesive AI strategy, the strengths and weaknesses of artificial intelligence in Europe were addressed. In today’s blog, we are going to summarize them.
Weaknesses for Artificial Intelligence in Europe development
The paper covered the main areas in where Europe is lagging in the AI global landscape. This failure to lead in AI has significant economic implications and limit Europe’s ability to ensure economic security, reduce dependencies on its main competitors, as well as direct AI development towards broader economic, societal and environmental goals.
Lack of access to data for AI training. The data is not transparent nor public; this hinders Europe’s ability to catch up in AI applications and future developments. US and Chinese tech giants, on the other hand, have amassed vast data sets, propelled their AI research and development, while also raised concerns about the uncontrolled pace of AI advancements.
Fragmentation of the EU system of innovation. The Existing European hubs are not strongly connected to each other. Analysis from the paper showed that the majority of connections remain within the same country. This fragmentation leads to costly inefficiencies, lack of economies of scale and knowledge spillovers, missed opportunities, and duplications of efforts. Only a combination of the comparative advantages of various AI hubs can create a more robust and competitive AI ecosystem. Also, this will ensure that the benefits of AI are distributed more evenly across different regions.
Brain drain. The paper mentioned that almost 14% of the top AI talent that graduated in Europe went to the US for grad school. Also, at the grad school level, the US is the place that gains the most, being a magnet from the world’s talent (China, India and Europe). After grad school, there is another brain drain from Europe to the US, with more grand talent working in the US than returning to Europe.
Skills gaps. There is a growing importance of analytical, social, and domain-specific skills. The digitalization of education, coupled with the application of AI, offers a transformative potential to enhance the learning experience, making education more accessible, personalized, and relevant to the continuously and rapidly evolving job markets. The paper recommends the need to take bold actions to integrate digitalization and AI through innovation methods sometimes in tandem with augmented reality (AR), virtual reality (VR), and gamification.
Lack of funding. There is an extreme insufficiency of funding for bolstering public-private investments in AI and public funding in European R&I. There is need for coherent and coordinated investments, with more focused investments and risk-taking necessary to support the transition, including, for example, local smart specialization strategies. The funding deficit is shown in this fact: there was 7 times more venture-capital funding in the US than the EU.
Strengths for Artificial Intelligence in Europe development
However, the ecosystem of artificial intelligence in Europe has some strenghts where it can start to develop itself.
Europe is a global leader in AI scientific publications. Europe has a significant opportunity to lead in the application of artificial intelligence (AI) in scientific research, particularly in the field of laboratory robotics. It trains a substantial amount of the top AI researchers.
The EU Single Market role. Fragmented AI ecosystems would get together in this market and benefit from a proactive industrial AI policy. State aid (or “European aid”) and mission-based policy could contribute to creating an “Airbus moment”, following the path of the famous EU industrial leader, while considering the constraints of sustainable and socially responsible deployment.
The European Commission leads AI legal framework. Although two critical gaps persist -the focus on narrow AI (e.g., ChatGPT) overlooks future threats from Artificial General Intelligence (AGI), and fragmented national or regional regulations struggle to govern borderless AI systems, as external innovators outside the EU’s jurisdiction continue advancing technologies accessible to Europeans-, the need for a historical commitment to ethical AI, aligns with the “AI Alignment” vision to ensure AI stays aligned with human values and EU objectives.
