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Artificial Intelligence: The Next 5(0) Years

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Talk on explainable and responsible AI, delivered at the sTARTUp talks meetup, Tartu, Estonia, 13 December 2018 - https://startuptalks.ee/

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Artificial Intelligence: The Next 5(0) Years

  1. 1. Marlon Dumas University of Tartu Institute of Computer Science AI: The Next 5(0) Years sTARTUp AI, Tartu, 13 Dec 2018
  2. 2. AI: The Last 50 Years Small Data Big Data Untraceable Decision Traceable Decision Rule-Based System Machine Learning Model 2
  3. 3. AI in 2018: Highly Optimized Black-Box Parrots Robotic Systems • Domestic robots / smart homes • Surveillance systems • Autonomous vehicles Natural Language Processing • Machine translation • Question-answering systems • Content robots (Virtual) Assistants • Recommender systems • Chatbots • RPA bots Automated Decision Systems • Predictive & prescriptive monitoring • Dynamic pricing • Credit scoring / underwriting • Medical diagnosis / precision medicine Machine Learning / Deep Learning 3
  4. 4. AI that cannot understand us… https://www.technologyreview.com/s/602973/ai-machine-attempts-to-understand-comic-books-and-fails/
  5. 5. AI we cannot understand… 5 https://towardsdatascience.com/human-interpretable-machine-learning-part-1-the-need-and-importance-of-model-interpretation-2ed758f5f476
  6. 6. Levels of AI 6 • A – Brute force, use heaps of data, optimize to death trying “all” moves (with “smart pruning” of course) • incl. combinatorial optimization, machine learning (hyperopt), deep learning • B - Selective optimization based on trial-and-error and/or human input. • Incl. Reinforcement learning, active learning • C – Augmented Intelligence • Humans & machines complementing each other, learning from each other https://www.theregister.co.uk/2018/05/10/heres_what_garry_kas parov_an_old_world_chess_champion_thinks_of_ai/
  7. 7. Where are we? 7 Last 5(0) years • Accuracy • Automation • Scalability Next 5(0) years • Reliability • Explainability • Trustworthiness
  8. 8. Challenges 8 • Plainly and model-agnostically – to reach all stakeholders • Actionably – Explaining why? What can be done about it? Explaining outputs • Safety & graceful degradation • Privacy • Fairness • Tamper-proofness Providing guarantees • Combining domain knowledge & machine learning • Learning from user feedback • Context-awareness • Goal-orientation Enabling symbiosis
  9. 9. Scalability • Large amounts of (fine-grained) data • High demands for performance Skills Shortage • AI technology will continue evolving fast • New applications will keep emerging Resistance • AI systems disrupt existing processes and practices • Fear of impact on job market What will not change by 2025? 9
  10. 10. Affordable AI • Vendors lowering the adoption barrier via pre-packaged solutions  clearer ROI • But lack of (quality) data will remain a bottleneck, especially in SMEs Actionable AI • From predicting to assisting, guiding, prescribing • But interpretability by end users will remain a bottleneck Repeatable AI • Engineering methods for reliably building, operating, and maintaining AI systems • Monitoring and graceful degradation and escalation will remain a challenge Responsible AI • Traceable decisions, explainable in simple terms What should we expect to see more in 2025? 10
  11. 11. • Since 25 May 2018, GDPR establishes a right for all individuals to obtain “meaningful explanations of the logic involved” when “automated (algorithmic) individual decision-making”, including profiling, takes place. 11 Right of explanation
  12. 12. Extracting explanations from black-boxes • LIME, Anchor, LORE Hybrid Human-AI systems • Active learning • Combining rules (domain knowledge) and black-box systems QA & monitoring of AI systems • From technical level (accuracy, reliability, sensitivity) to user level (acceptance, performance enhancement) • Monitoring, graceful degradation, escalation, contingency management What should we be learning more about? 12

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