At the Rise of AI Summit 2020, Hans Uszkoreit, scientific director at DFKI in Berlin and founder and chief scientist of an AI company in Beijing, compares the AI strengths of the United States, China and Europe after nearly a year back in Europe following three years in China. Building on Kai-Fu Lee's comparison of the two AI giants, he argues Europe is not leading in AI for internet services, consumer electronics or finance, but holds strong chances in manufacturing, Industry 4.0 and enterprise AI, and in the next wave of AI that combines machine learning with explicit knowledge graphs. He describes the 'intelligent enterprise' as combining data, knowledge and business processes, while cautioning that AI cannot automate the most dynamic, high-level decisions. He argues Europe's persistent weakness is the last mile from basic research to scaled commercialization, and that German 'Angst' around data, illustrated by the country's Corona contact-tracing app, is holding AI adoption back. He proposes secured, trust-governed centralized data storage instead of current data protection norms, and closes discussing whether AI is dangerous and how enterprises can innovate despite AI's bias toward broad, data-rich topics.
Key takeaways
- Uszkoreit argues Europe is not leading in AI for internet, consumer electronics or finance, but has strongholds in manufacturing and enterprise AI.
- He frames the intelligent enterprise as combining data, knowledge and business processes, an idea he traces to 1992.
- He argues AI cannot automate high-level enterprise decisions because markets and regulations change too fast and unpredictably.
- He says Europe's persistent weakness is the last mile from basic research to scaled commercialization, citing GPS, Siri and Google Translate.
- He attributes Germany's slow AI and data adoption to a widespread fear he calls German Angst, illustrated by the Corona app's low data-sharing rate.
Detailed notes
Where Europe stands between the two AI giants
Uszkoreit revisits Kai-Fu Lee's comparison of US and Chinese AI strength, which last year left Europe as a marginal 'safe' player. He offers a more fine-grained view: Europe is not leading and likely will not lead in AI for internet services, consumer electronics or finance, and automotive and public transport remain open questions, but manufacturing, Industry 4.0 and increasingly enterprise AI are strongholds where Europe has real chances.
Enterprise AI and the intelligent enterprise
He frames digital transformation as two waves: first digitizing data processing and resource planning, then adding intelligence through Industry 4.0, digital twins, robotics and AI applications, toward a vision of the 'intelligent enterprise' that he traces to 1992. He describes this as a complex organism of people, technology, knowledge and intelligent processes, resting on three ingredients: available and interoperable data, managed and increasingly machine-usable knowledge, and business processes, both operational and support, that must be adapted or redesigned and modeled digitally. He stresses limits: AI can automate narrow tasks and even some end-to-end processes, but not higher-level decision making, because markets, regulations and external conditions change too fast and combinatorially for any system to predict tomorrow's decisions from past data. He lists main enterprise AI applications: interpreting unstructured data, demand forecasting, anomaly and event detection, business process modeling, and human-machine interaction. He argues the central challenge for the next AI wave is combining the implicit knowledge learned by deep learning models with explicit knowledge held in resources like the Google and Baidu knowledge graphs and Wikidata, and describes his own company's approach of web crawling with partner Ubermetrics, extracting roughly 80 types of events into knowledge graphs used for supplier, competitor, portfolio and compliance monitoring.
Europe's basic research strength and its last-mile weakness
Uszkoreit says European basic research has long led in AI and is well positioned for hybrid AI and human-centered computing, but Europe repeatedly fails at the 'last mile' between applied research and scaled commercialization, including venture-capital risk-taking. He cites DARPA-linked technologies such as the internet, GPS, Siri and Google Translate as often rooted in European research but ultimately developed and commercialized in the United States.
German Angst, data and the Corona app
He argues Germany's deeper problem is a pervasive fear of digital data and algorithms, contrasting it with his experience in China, and illustrates it with examples like overflowing paper mailboxes, cash-only restaurants and public offices without email or IT infrastructure. He criticizes Germany's Corona contact-tracing app as an example of good intentions producing a nearly useless tool: about 25% penetration, with just over 10% of infected users sharing positive results, far below the roughly 60% penetration Oxford researchers estimated was needed for real impact, which he attributes to political decisions rather than the technology. His proposed fix is a highly secured, centralized data store, which he suggests could be called GAIA-X, governed independently like a supreme court or central bank, with strict legislation and harsh penalties for abuse, shifting focus from restricting data collection to protecting people from misuse.
Is AI dangerous, and Q&A highlights
Weighing a German researcher's warning that AI is more dangerous than the atomic bomb against Andrew Ng's view that fearing evil AI is like fearing Mars overpopulation, Uszkoreit sides with caution grounded in current limits: today's AI lacks self-consciousness and free will, so it cannot be evil, but critical deployments still warrant care and continued work on transparent, accountable AI. In the closing Q&A, he agrees that an intelligent enterprise increasingly acts as its own decision-making subject, since more than 90% of decisions are already collective rather than made by a single CEO, and, asked whether AI is inherently a conservative consultant favoring broad, data-rich topics, he says the fix is infusing systems with explicit, condensed knowledge alongside the implicit knowledge learned from few examples.
Quotes
Data are not harmful for health or environment, and data are good, nothing bad about them.
Hans Uszkoreit
We should be very cautious and we should continue to work on transparent, reliable and accountable AI.
Hans Uszkoreit
Questions answered in this talk
Where does Europe stand in the global AI race between the US and China?
According to Hans Uszkoreit at Rise of AI Summit 2020, Europe is not leading in AI for internet services, consumer electronics or finance, but has strong chances in manufacturing, Industry 4.0 and enterprise AI, and in combining machine learning with explicit knowledge for the next wave of AI.
Why does Uszkoreit think Germany's Corona contact-tracing app achieved little?
According to Hans Uszkoreit at Rise of AI Summit 2020, the app reached only about 25% penetration and just over 10% of infected users shared their positive test results, far below the roughly 60% penetration Oxford researchers estimated was needed to be effective, which he blames on political decisions rather than the technology itself.
What is Europe's biggest weakness in turning AI research into successful companies?
According to Hans Uszkoreit at Rise of AI Summit 2020, Europe is strong in basic research but weak at the last mile between research and scaled commercialization, so technologies with European roots, such as GPS, Siri and Google Translate, were ultimately developed and commercialized in the United States.