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The Ethics of Artificial Intelligence

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"AI is “our greatest existential threat…”
“I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish.”

“I think there is potentially a dangerous outcome there.” (referring to Google’s Deep Mind which he invested in to keep an eye on things)."
Elon Musk

Published in: Technology
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The Ethics of Artificial Intelligence

  1. 1. The Ethics In Artificial Intelligence Are intelligent machines friend or foe? Nov 14, 2016
  2. 2. TONIGHT’S SPEAKERS Chris Messina Member of the Board of Directors North American Nickel Karl Seiler PIVIT & Big Data Florida Malcolm McRoberts Software Architect NANTHEALTH
  3. 3. Artificial Intelligence When a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving"
  4. 4. AI IS ALREADY EVERYWHERE, EVERYDAY
  5. 5. You live in the age of the data-driven algorithm Decisions that affect your life — are being made by mathematical models.
  6. 6. Why the rush to AI? o Cheaper computing o More data o Better algorithms …its because we can
  7. 7. Why the rush to AI? o Decision automation is now an inevitable economic imperative o Driven by a faster-paced, micro- managed, interconnected, automated, and optimized world o Never-asleep autonomous decision making - it is here now
  8. 8. Why the rush to AI? o Decisions are made in view of assessed positive and negative projected outcomes o Positive and negative are merely derived (learned) weights o Relative to some system of value o Moving toward or away from objectives & problems -+
  9. 9. Why the rush to AI? o Weights are encoded intent o Based on some worldview, zeitgeist, culture, rule of law, economic goal and philosophical perspective o So autonomous systems are encoded with intent
  10. 10. Why the rush to AI? o A linked chain from software to intent o How can we impose systems that bend code creating and learning systems toward positive intent for our friends and potentially negative intent for the evil-doers?
  11. 11. The good
  12. 12. More precision
  13. 13. Better reliability
  14. 14. Increased savings
  15. 15. Better safety
  16. 16. More speed
  17. 17. “We have the opportunity in the decades ahead to make major strides in addressing the grand challenges of humanity. AI will be the pivotal technology in achieving this progress. We have a moral imperative to realize this promise while controlling the peril. It won’t be the first time we’ve succeeded in doing this.” Ray Kurzweil
  18. 18. The bad
  19. 19. “Success in creating AI would be the biggest event in human history,…” “Unfortunately, it might also be the last, unless we learn how to avoid the risks. In the near term, world militaries are considering autonomous-weapon systems that can choose and eliminate targets.” “…humans, limited by slow biological evolution, couldn’t compete and would be superseded by A.I.” Stephen Hawking
  20. 20. “I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.” Bill Gates
  21. 21. AI is “our greatest existential threat…” “I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish.” “I think there is potentially a dangerous outcome there.” (referring to Google’s Deep Mind which he invested in to keep an eye on things) Elon Musk
  22. 22. When really smart people get worried I make it a habit to pay attention!
  23. 23. More than 16,000 researchers and thought leaders have signed an open letter to the United Nations calling for the body to ban the creation of autonomous and semi-autonomous weapons,
  24. 24. “…it’s all changing so fast…”
  25. 25. No one before has seen the change you have seen It is nothing compared to the change that is coming
  26. 26. The ugly The ugly Another fatal Tesla crash reportedly on Autopilot emerges, Model S hits a streetsweeper truck – caught on dashcam
  27. 27. Remember I-ROBOT & Asimov’s 3 Laws  A robot may not injure a human being or, through inaction, allow a human being to come to harm.  A robot must obey orders given it by human beings except where such orders would conflict with the First Law.  A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
  28. 28. The ugly (autonomous cars & the trolley predicament) Ethical questions arise when programming cars to act in situations in which human injury or death is inevitable, especially when there are split- second choices to be made about whom to put at risk.
  29. 29. The ugly (gap-filling non-human care providers) AI-based applications could improve health outcomes and quality of life for millions of people in the coming years— but only if they gain the trust of doctors, nurses, and patients.
  30. 30. The ugly (non-human directed education) Though quality education will always require active engagement by human teachers, AI promises to enhance education at all levels, especially by providing personalization at scale.
  31. 31. The ugly (lights-out economy) The whole idea is to do something no other human—and no other machine—is doing. If we all die, it would keep trading.
  32. 32. The ugly (no work for you – reskill becomes a priority in education) In the first machine age the vast majority of Americans worked in agriculture. Now it's less than two percent. These people didn't simply become unemployed, they reskilled. One of the best ideas that America had was mass primary education. That's one of the reasons it became an economic leader and other countries also adopted this model of mass education, where people paid not only for their own children but other people's children to go to school.
  33. 33. Safe exploration - agents learn about their environment without executing catastrophic actions? Robustness - machine learning systems that are robust to changes in the data distribution, or at least fail gracefully?
  34. 34. Avoiding negative side effects- avoid undesired effects on the environment? Avoiding “reward hacking” - prevent agents from “gaming” their reward functions
  35. 35. Scalable oversight - agents efficiently achieve goals for which feedback is very expensive? For example, can we build an agent that tries to clean a room in the way the user would be happiest with, even feedback from the user is very rare
  36. 36. …and so o AI adoption and sophistication is speeding up o It is an economic imperative outpacing constraints o Decision making is being coded into every system and product o Decision making overlaps ethics and will be autonomous o Forward thinkers are CONCERNED and starting to work this problem Carbon-based work-units unite!
  37. 37. Karl Seiler | President + 1 321 - 7 5 0 - 5165 k ar l@ piviting.c om www.Piviting.c om S M A R T E R C H A N G E

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