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

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Zuwachs des BSP weltweit durch Einsatz der KI: 16 Billionen USD (= 14%) bis 2030 (PWC). Prozesse werden optimiert, Ressourcen effizienter eingesetzt, Mobilität neu gedacht, KI wird aus der Cloud gezogen oder ist als AI-on-Chip direkt in smarten Dingen verbaut. V.a. in Kombination mit IoT, AR, Blockchain, Business + Market Data werden völlig neue Geschäftsmodelle denkbar. Wie ist dieses Potenzial zu heben? Wo ist Licht, wo ist Schatten? Wo lauern Illusionen schneller Machbarkeit? Welches sind die low hanging fruits der KI? Kommt die Autonomisierungswelle als Tsunami über Wirtschaft, Arbeit und Gesellschaft?
Aus den Folien:
06 • KI-Systematik: Techniken, Funktionen, Anwendung, Treiber (WIPO)
07 • 5-Stufen-Modell der Automation des Entscheidens (Bitkom)
• Phasenmodell der KI
• KI im Kontext der Innovationsfelder der digitalen Transformation
09 • Kontext der Innovationsfelder
10 • Research Trends & Challenges – inkl.
Large-scale machine learning
Deep learning
Reinforcement learning
Collaborative systems
Crowdsourcing and human computation
Neuromorphic Computing
- AI Challenges
e.g. Ethics by design, Integration of techniques
- Politics & Society Challenges,
e.g. AI-enabled deep fakes (truth crisis), AI impact on job market, AI geopolitics (China)

11 • Watchlist
• PAI: hyper-personalized AI
Vsd. Ansätze sind kombinierbar: personalisierter digitaler Assistent, Digital Twin der Person, Avatar mit Funktion 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 computing based AI
Bestimmte Berechnungsprobleme in der KI könnten mit Quanten Computing gelöst werden. Google-Teams forschen z. B. an Quantum Neural Networks.
...

Published in: Business
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Status und Ausblick - Wie wird sich KI technisch weiterentwickeln? Münchner Kreis 2019

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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