Openbar Kontich // Is your AI plotting to kill you? Why AI and Machine Learning are dangerous by Ben Vermeersch (Infofarm)
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Killer Robots. Mass surveillance. Fake videos and news. Racist and biased AI. The news is flooded with bad and dangerous applications of machine learning. In this evening, you will learn from a person who makes a living out of developing AI algorithms why they can be dangerous and should be avoided.
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Openbar Kontich // Is your AI plotting to kill you? Why AI and Machine Learning are dangerous by Ben Vermeersch (Infofarm)
- 1. Is your AI plotting to kill you? The dangers of AI and Machine Learning
- 2. Artificial Intelligence Artificial intelligence is computer software or a computer system that is capable of performing intelligent tasks that are only deemed possible by humans
- 3. 1943 – first description of neural network Pitts - McCulloch 1950– Alan Turing publishes paper about machines that think 1956 – AI research started at Dartmouth College 1973 – Governement funding halted. Start of first AI winter 1980 – AI research gets billions of funding again 1987 – Disillusion by lack of hardware power. Start of second AI winter 1997 – Deep Blue wins chess game against Kasparov 2011– Watson wins Jeopardy 2014 – Facebook announces Deepface facial recognition 2016– AlphaGo wins Go
- 4. Enough with the success stories Let’s get to the juicy stuff…
- 5. AI is threathening our privacy
- 9. Protect yourself! With some help of machine learning
- 10. AI is really good at making fake things
- 14. You can generate voice too!
- 18. The solution to fake AI… is AI Yes, you will be out of a job soon
- 19. People are building bad models You and your AI are really not as smart as you think
- 22. People are awful And your algorithm will be no different
- 24. Current state of AI • Each narrow application needs to be specially trained • Require large amounts of hand-crafted, structured training data • Learning must generally be supervised: Training data must be tagged • Require lengthy offline/ batch training • Poor transfer learning ability, reusability of modules, and integration • Systems are opaque, making them very hard to debug • They encode correlation, not causation or ontological relationships • Only handle very narrow aspects of natural language • Not well suited for high-level, symbolic reasoning or planning Peter Voss - 2016
- 25. What can you do?
- 26. Get informed You’re doing well…
- 27. Let Data Scientists do Data Science And good things take time
- 28. Have a mixed team Both in skills and demographically
- 29. Don’t be evil And keep it that way
- 30. Thanks!