Status und Ausblick - Wie wird sich KI technisch weiterentwickeln? Münchner Kreis 2019

1. Status und Ausblick Wie wird sich KI technisch weiterentwickeln? Willi Schroll , strategiclabs Berlin Europa Ins:tut Universität Zürich – 21. digital lecture – 27. August 2018 1 Willi Schroll, strategiclabs Berlin Fachkonferenz Münchner Kreis: Künstliche Intelligenz und die Automa:on des Entscheidens 9. Oktober 2019 – München

2. Foresight – Strategische Zukun3sforschung Convergence of technologies Digital Culture Learning from nature Ubiquitous intelligence New patterns of consumption Globalisation 2.0 Knowledge- based economy Business ecosystems Radical changes to energy and resources Climate change & environmental pollution Urbanisation A new political world order Global risk society New patterns of mobility Transformation of healthcare systems Changing gender roles New level of individualisation Social and cultural disparities Demographic change Endangered biodiversity & ecosystems Cultural convergence & divergence Internet of Everything Nanotech & Biotech Changes to work & management STEEP Dimensionen Monitoring ▻ Daten ▻ Trendanalysen ▻ Szenarien ▻ strategische Implika:onen SOCIETY TECHNOLOGY ECOLOGY ECONOMY POLITICS Bildrechte/Quelle: mapegy.com, Boston Dynamics2

3. KI-Systema:k: Techniken, Funk:onen, Anwendung, Treiber 3 Bio-inspired approaches Classiﬁca:on and regression trees Deep learning Expert system Fuzzy logic Instance-based learning Latent representa:on Logic programming Machine learning Mul:-task learning Neural network Ontology engineering Probabilis:c graphical models Probabilis:c reasoning Reinforcement learning Rule learning Supervised learning Support vector machine Unsupervised learning AItechniques • • • • • • • • • • Quellen: hdps://www.wipo.int/export/sites/www/tech_trends/en/ar:ﬁcial_intelligence/docs/techtrends_ai_glossary.pdf

4. KI-Systema:k: Techniken, Funk:onen, Anwendung, Treiber 4 Bio-inspired approaches Classiﬁca:on and regression trees Deep learning Expert system Fuzzy logic Instance-based learning Latent representa:on Logic programming Machine learning Mul:-task learning Neural network Ontology engineering Probabilis:c graphical models Probabilis:c reasoning Reinforcement learning Rule learning Supervised learning Support vector machine Unsupervised learning Augmented reality Biometrics Character recogni:on Computer vision Distributed AI Image and video segmenta:on Informa:on extrac:on Knowledge representa:on & reasoning Natural language processing Object tracking Planning/scheduling Predic:ve analy:cs Robo:cs Scene understanding Seman:cs Sen:ment analysis Speech processing Speech recogni:on Speaker recogni:on Speech-to-speech applica:on AItechniques AIfunc:onalapplica:ons enabling enabling • • • • • • • • • • Quellen: hdps://www.wipo.int/export/sites/www/tech_trends/en/ar:ﬁcial_intelligence/docs/techtrends_ai_glossary.pdf

5. KI-Systema:k: Techniken, Funk:onen, Anwendung, Treiber 5 Banking and finance Business Document management & publishing Educa:on Entertainment/Gaming Healthcare Home/Service Robots Industry and manufacturing Life and medical sciences Public Safety Security/Cybersecurity Telecommunica:ons Transporta:on/Logis:cs Bio-inspired approaches Classifica:on and regression trees Deep learning Expert system Fuzzy logic Instance-based learning Latent representa:on Logic programming Machine learning Mul:-task learning Neural network Ontology engineering Probabilis:c graphical models Probabilis:c reasoning Reinforcement learning Rule learning Supervised learning Support vector machine Unsupervised learning Augmented reality Biometrics Character recogni:on Computer vision Distributed AI Image and video segmenta:on Informa:on extrac:on Knowledge representa:on & reasoning Natural language processing Object tracking Planning/scheduling Predic:ve analy:cs Robo:cs Scene understanding Seman:cs Sen:ment analysis Speech processing Speech recogni:on Speaker recogni:on Speech-to-speech applica:on AItechniques AIfunc:onalapplica:ons AIapplica:onfields enabling enabling enabling • • • • • • • • • • Quellen: hdps://www.wipo.int/export/sites/www/tech_trends/en/ar:ficial_intelligence/docs/techtrends_ai_glossary.pdf - ergänzend für Spalte 3: hdps://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai100report10032016fnl_singles.pdf (2016)

6. KI-Systema:k: Techniken, Funk:onen, Anwendung, Treiber Quellen: hdps://www.wipo.int/export/sites/www/tech_trends/en/ar:ficial_intelligence/docs/techtrends_ai_glossary.pdf - ergänzend für Spalte 3: hdps://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai100report10032016fnl_singles.pdf 6 Banking and finance Business Document management & publishing Educa:on Entertainment/Gaming Healthcare Home/Service Robots Industry and manufacturing Life and medical sciences Public Safety Security/Cybersecurity Telecommunica:ons Transporta:on/Logis:cs Smart City Smart Gov/Admin Smart Health Smart Home Smart Learning & Educa:on Smart Mobility/MaaS Smart Produc:on/Industrie 4.0 Smart Work Smart X … … Expected Values Context adap:on Personalsa:on Predic:on Process Op:miza:on Targe:ng … Bio-inspired approaches Classifica:on and regression trees Deep learning Expert system Fuzzy logic Instance-based learning Latent representa:on Logic programming Machine learning Mul:-task learning Neural network Ontology engineering Probabilis:c graphical models Probabilis:c reasoning Reinforcement learning Rule learning Supervised learning Support vector machine Unsupervised learning Augmented reality Biometrics Character recogni:on Computer vision Distributed AI Image and video segmenta:on Informa:on extrac:on Knowledge representa:on & reasoning Natural language processing Object tracking Planning/scheduling Predic:ve analy:cs Robo:cs Scene understanding Seman:cs Sen:ment analysis Speech processing Speech recogni:on Speaker recogni:on Speech-to-speech applica:on AItechniques AIfunc:onalapplica:ons AIapplica:onfields AIintegra:on/guidingvisions enabling enabling enabling • • • • • • • • • •

7. 5-Stufen-Modell der Automa:on des Entscheidens Assistenz, Delega=on, Autonomisierung (Bitkom) 1. Assis:ertes Entscheiden 2. Teilweises Entscheiden 3. Geprüoes Entscheiden 4. Delegiertes Entscheiden 5. Autonomes Entscheiden 7 AI-Matrix nach PWC und Autonomiegrade (Grafik modifiziert) Bildrechte/Quelle: hdps://www.pwc.com/gx/en/issues/analy:cs/assets/pwc-ai-analysis- sizing-the-prize-report.pd (2017) Quelle: hdps://www.bitkom.org/sites/default/files/file/import/Bitkom- Leipaden-KI-verstehen-als-Automa:on-des-Entscheidens-2-Mai-2017.pdf (2017)

8. Phasenmodell der KI Übergreifende Trends • Relevanz von Trainingsdaten • Umgebungsdimensionen (mehr Sensoren, Datendurchsatz) • Mensch-Maschinen-Interak:on • Spezialisierung, enge Nischen • Autonomisierung Bildrechte/Quelle: hdps://www.bitkom.org/sites/default/ﬁles/ﬁle/import/171012-KI-Gipfelpapier-online.pdf (2017), Phasen nach Wahlster (2016) 8 Prozessuale Grundstruktur

9. KI im Kontext der Innova:onsfelder der digitalen Transforma:on Bildrechte: Willi Schroll 2018 9 KI im betrieblichen Einsatz. Bildrechte/Quelle: hdp://cdn.aiindex.org/2018/AI%20Index%202018%20Annua l%20Report.pdf McKinsey „Capabili:es embedded in at least one company func:on (2018)“

10. Research Trends & Challenges Sustaining AI Research Trends Large-scale machine learning Deep learning Reinforcement learning Robo:cs Computer vision Natural Language Processing Collabora:ve systems Crowdsourcing and human computa:on Algorithmic game theory and computa:onal social choice Internet of Things (IoT) Neuromorphic Compu:ng Research Trends: Quelle hdps://ai100.stanford.edu/sites/g/ﬁles/sbiybj9861/f/ai100report10032016fnl_singles.pdf 10 AI Challenges Data quality, bias in data Design complexity Ethical by design vs pluralism of society Ethical implica:ons Fragmenta:on & specialisa:on General theory of cogni:ve process missing Integra:on of techniques … Poli=cs & Society Challenges AI-enabled deep fakes (truth crisis) AI impact on job market AI impact on educa:on (purpose crisis) „AI Race“, geopoli:cs of AI; e.g. China AI in targe:ng + persuasion (asymmetry of power) AI solu:ons for global ecological crisis? … …

11. Watchlist PAI: hyper-personalized AI Vsd. Ansätze sind kombinierbar: personalisierter digitaler Assistent, Digital Twin der Person, Avatar mit Funk:on der Stellvertretung, Verhandlungsmandat, Analyse der Verhaltensmuster, instant Coaching, Verhaltenstherapie, Security/Cybersecurity/Health XAI: explainable AI, transparency Wenn AI-Mechanismen nicht nachvollziehbar sind, leidet die Vertrauenswürdigkeit. Auch die Gesetzgeber stellen neue Anforderungen. XAI soll die Transparenz herstellen. QAI: quantum compu=ng based AI Bes:mmte Berechnungsprobleme in der KI könnten mit Quanten Compu:ng gelöst werden. Google-Teams forschen z. B. an Quantum Neural Networks. Bildrechte/Quelle: hdps://projectpai.com/ - hdps://www.accenture.com/_acnmedia/pdf-85/accenture-understanding-machines-explainable-ai.pdf - hdps://phys.org/news/2018-02-quantum-algorithm-ai-faster.html - hdps://ai.google/research/teams/applied-science/quantum/ 11

12. Vielen Dank für Ihre Aufmerksamkeit! Willi Schroll , strategiclabs Berlin Europa Ins:tut Universität Zürich – 21. digital lecture – 27. August 2018 12 Willi Schroll, strategiclabs Berlin schroll@strategiclabs.de

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