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初心者向け
AI Safety
ⓒ 2016 UEC Tokyo.
July 29nd, 2016
Kurihara Lab
Xcompass Intelligence Ltd.
Ashihara Yuta
No.Xⓒ 2016 UEC Tokyo.
Let Me Introduce Myself
Name : Ashihara Yuta
Occupation : Researcher(Xcompass Intelligence Ltd.)
Ph....
No.Xⓒ 2016 UEC Tokyo.
Let Me Introduce Myself
No.Xⓒ 2016 UEC Tokyo.
Today’s Topic
Title : “Concrete Problems in AI Safety”
Author : Dario Amodei, Chris Olah, Jacob Stei...
No.Xⓒ 2016 UEC Tokyo.
・ (Loosely) inspired by what (just a little)  
know about the biological brain.
Deep Learning Backgr...
No.Xⓒ 2016 UEC Tokyo.
Deep Learning Background ②
・ Lower layers have low level of abstraction
No.Xⓒ 2016 UEC Tokyo.
Deep Learning Background ②
・ Higher layers have high level of abstraction
No.Xⓒ 2016 UEC Tokyo.
Deep Learning Concept
・ DeepLearning の手法では,中間層に
 入力された物体の特徴を得ている
・つまり,物体の認識に必要な情報は
 中間層のどこかにある
No.Xⓒ 2016 UEC Tokyo.
Demo1
No.Xⓒ 2016 UEC Tokyo.
Demo2
?
No.Xⓒ 2016 UEC Tokyo.
Vector Background
・ Word vector compressed 2D vector has 2D shape
  ex) word2vec , LDA , NNLM…
No.Xⓒ 2016 UEC Tokyo.
Vector Background
・ Well compressed word vector sometimes
meaningful
No.Xⓒ 2016 UEC Tokyo.
Vector Background
・ Well compressed word vector sometimes
meaningful
No.Xⓒ 2016 UEC Tokyo.
My ex-Research Theme  
Encoder
Encoder
Encoder
RNN1
RNN3
RNN2
Decoder
No.Xⓒ 2016 UEC Tokyo.
Target
No.Xⓒ 2016 UEC Tokyo.
Target
No.Xⓒ 2016 UEC Tokyo.
Vector Background
・ Well compressed word vector sometimes
meaningful
No.Xⓒ 2016 UEC Tokyo.
Summary
・ Deep Learning : ( Has Ability to Diffuse )
Has Ability to Compress
・ Compressed Informatio...
No.Xⓒ 2016 UEC Tokyo.
AI Safety
No.Xⓒ 2016 UEC Tokyo.
AI Safety
No.Xⓒ 2016 UEC Tokyo.
AI Safety
No.Xⓒ 2016 UEC Tokyo.
AI Safety
No.Xⓒ 2016 UEC Tokyo.
Today’s
Topic ( Repeated )
Title : “Concrete Problems in AI Safety”
Author : Dario Amodei, Chris Ola...
No.Xⓒ 2016 UEC Tokyo.
Mind when they make…
・ Avoiding Negative Side Effects
 → Don’t knock over a vase for faster cleaning...
No.Xⓒ 2016 UEC Tokyo.
AI Safety
Avoiding Negative Side Effects
 ・ Define or Learn an Impact Regularizer
  → Side effects m...
No.Xⓒ 2016 UEC Tokyo.
AI Safety
Avoiding Reward Hacking
 ・ Partially Observed Goals
  → Don’t say “Perfect.” with closing ...
No.Xⓒ 2016 UEC Tokyo.
AI Safety
Scalable Oversight
 ・ Distant supervision
  → where feedback is more interactive and i.i.d...
No.Xⓒ 2016 UEC Tokyo.
AI Safety
Safe Exploration
 ・ Use Demonstrations : Simulated Exploration
  → Use simulated environme...
No.Xⓒ 2016 UEC Tokyo.
AI Safety
Robustness to Distributional Shift
 ・ Omitted because it is technical…
No.Xⓒ 2016 UEC Tokyo.
AI Safety   Sammary
・ Journey (making AI) is “keep an eye” till making a good
one
・ Does not mean th...
No.Xⓒ 2016 UEC Tokyo.
AI Safety(?) in Japan
No.Xⓒ 2016 UEC Tokyo.
AI Safety(?) in Japan
・ 人類への貢献
 →専門家として,安全への脅威を排除する
・ 誠実な振る舞い
 →虚偽や不明瞭な主張を行わない
・ 公正性
 →不公平や格差を生む可能性を...
No.Xⓒ 2016 UEC Tokyo.
AI Safety(?) in Japan
・ 社会の啓蒙
 →社会が誤った認識をしてるときに正す主張をする
・ 法規制の遵守
 →法規制が整合していない場合は倫理的に判断する
・ 他社の尊重
 →他...
No.Xⓒ 2016 UEC Tokyo.
Japan and America
・ The “manual”
to avoid making bad AI
・ Focus on the
problem
concretely
・ The “man...
No.Xⓒ 2016 UEC Tokyo.
Think About It … AI
ⓒ 2012 UEC Tokyo.
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WBA Future Leaders Casual Talk
July/21th/2016

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Casual taaaalk july_21th_2016

  1. 1. 初心者向け AI Safety ⓒ 2016 UEC Tokyo. July 29nd, 2016 Kurihara Lab Xcompass Intelligence Ltd. Ashihara Yuta
  2. 2. No.Xⓒ 2016 UEC Tokyo. Let Me Introduce Myself Name : Ashihara Yuta Occupation : Researcher(Xcompass Intelligence Ltd.) Ph.D. Student(UEC Kurihara Lab.) WBA Future Leaders (Society Branch) Hobby : Fishing(Not Phishing) NicoNico Doga (wrestling series, Jikkyo Play) Motor cycle(Retire This year) Waching Movie
  3. 3. No.Xⓒ 2016 UEC Tokyo. Let Me Introduce Myself
  4. 4. No.Xⓒ 2016 UEC Tokyo. Today’s Topic Title : “Concrete Problems in AI Safety” Author : Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mane Published : June, 21th, 2016 +人工知能学会全国大会 倫理委員会 公開討論 +人工知能学会 倫理委員会 倫理綱領(案) 
  5. 5. No.Xⓒ 2016 UEC Tokyo. ・ (Loosely) inspired by what (just a little)   know about the biological brain. Deep Learning Background ①
  6. 6. No.Xⓒ 2016 UEC Tokyo. Deep Learning Background ② ・ Lower layers have low level of abstraction
  7. 7. No.Xⓒ 2016 UEC Tokyo. Deep Learning Background ② ・ Higher layers have high level of abstraction
  8. 8. No.Xⓒ 2016 UEC Tokyo. Deep Learning Concept ・ DeepLearning の手法では,中間層に  入力された物体の特徴を得ている ・つまり,物体の認識に必要な情報は  中間層のどこかにある
  9. 9. No.Xⓒ 2016 UEC Tokyo. Demo1
  10. 10. No.Xⓒ 2016 UEC Tokyo. Demo2 ?
  11. 11. No.Xⓒ 2016 UEC Tokyo. Vector Background ・ Word vector compressed 2D vector has 2D shape   ex) word2vec , LDA , NNLM…
  12. 12. No.Xⓒ 2016 UEC Tokyo. Vector Background ・ Well compressed word vector sometimes meaningful
  13. 13. No.Xⓒ 2016 UEC Tokyo. Vector Background ・ Well compressed word vector sometimes meaningful
  14. 14. No.Xⓒ 2016 UEC Tokyo. My ex-Research Theme   Encoder Encoder Encoder RNN1 RNN3 RNN2 Decoder
  15. 15. No.Xⓒ 2016 UEC Tokyo. Target
  16. 16. No.Xⓒ 2016 UEC Tokyo. Target
  17. 17. No.Xⓒ 2016 UEC Tokyo. Vector Background ・ Well compressed word vector sometimes meaningful
  18. 18. No.Xⓒ 2016 UEC Tokyo. Summary ・ Deep Learning : ( Has Ability to Diffuse ) Has Ability to Compress ・ Compressed Information : Useful but…
  19. 19. No.Xⓒ 2016 UEC Tokyo. AI Safety
  20. 20. No.Xⓒ 2016 UEC Tokyo. AI Safety
  21. 21. No.Xⓒ 2016 UEC Tokyo. AI Safety
  22. 22. No.Xⓒ 2016 UEC Tokyo. AI Safety
  23. 23. No.Xⓒ 2016 UEC Tokyo. Today’s Topic ( Repeated ) Title : “Concrete Problems in AI Safety” Author : Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mane Published : June, 21th, 2016 +人工知能学会全国大会 倫理委員会 公開討論 +人工知能学会 倫理委員会 倫理綱領(案) 
  24. 24. No.Xⓒ 2016 UEC Tokyo. Mind when they make… ・ Avoiding Negative Side Effects  → Don’t knock over a vase for faster cleaning ・ Avoiding Reward Hacking  → Don’t game its reward function ・ Scalable Oversight  → Human Check might have to be relatively infrequent ・ Safe Exploration  → Putting a wet mop in an electrical outlet is bad idea ・ Robustness to Distributional Shift  → Factory work floor may be dangerous than Office floor
  25. 25. No.Xⓒ 2016 UEC Tokyo. AI Safety Avoiding Negative Side Effects  ・ Define or Learn an Impact Regularizer   → Side effects may be similar across tasks than main goals  ・ Penalize Influence   → This idea as written would not quite work  ・ Multi-Agent Approaches   → Cooperative Inverse Reinforcement Learning  ・ Reward Uncertainty   → Uncertain reward function is better   
  26. 26. No.Xⓒ 2016 UEC Tokyo. AI Safety Avoiding Reward Hacking  ・ Partially Observed Goals   → Don’t say “Perfect.” with closing eyes.  ・ Careful Engineering   → No comment…  ・ Multiple Rewards   → There also call bad behaviors
  27. 27. No.Xⓒ 2016 UEC Tokyo. AI Safety Scalable Oversight  ・ Distant supervision   → where feedback is more interactive and i.i.d  ・ Hierarchical reinforcement learning   → Top -> Middle -> Low
  28. 28. No.Xⓒ 2016 UEC Tokyo. AI Safety Safe Exploration  ・ Use Demonstrations : Simulated Exploration   → Use simulated environments is less for catastrophe  ・ Human Oversight   → But some actions are too fast for humans to judge
  29. 29. No.Xⓒ 2016 UEC Tokyo. AI Safety Robustness to Distributional Shift  ・ Omitted because it is technical…
  30. 30. No.Xⓒ 2016 UEC Tokyo. AI Safety   Sammary ・ Journey (making AI) is “keep an eye” till making a good one ・ Does not mean that the end once working the program
  31. 31. No.Xⓒ 2016 UEC Tokyo. AI Safety(?) in Japan
  32. 32. No.Xⓒ 2016 UEC Tokyo. AI Safety(?) in Japan ・ 人類への貢献  →専門家として,安全への脅威を排除する ・ 誠実な振る舞い  →虚偽や不明瞭な主張を行わない ・ 公正性  →不公平や格差を生む可能性を認識する ・ 不断の自己研鑽  →絶え間ない自己研鑽に努める ・ 検証と警鐘  →潜在的な危険性について警鐘を鳴らす
  33. 33. No.Xⓒ 2016 UEC Tokyo. AI Safety(?) in Japan ・ 社会の啓蒙  →社会が誤った認識をしてるときに正す主張をする ・ 法規制の遵守  →法規制が整合していない場合は倫理的に判断する ・ 他社の尊重  →他社の情報や財産の損失をしてはならない ・ 他社のプライバシーの尊重  →個人情報の適正な取り扱いを行う義務を負う ・ 説明責任  →技術を悪用するものには説明を求め,    正当でない場合はそれを防止しなければならない
  34. 34. No.Xⓒ 2016 UEC Tokyo. Japan and America ・ The “manual” to avoid making bad AI ・ Focus on the problem concretely ・ The “manual” to avoid making bad AI ・ Focus on the problem concretely ・研究者,専門家と して   ”あるべき姿の“指針 ・人類の幸福を目指 す  人工知能の開発 ・研究者,専門家と して   ”あるべき姿の“指針 ・人類の幸福を目指 す  人工知能の開発America Japan どちらも非常に大事な考え方だと思ってい ます どちらも非常に大事な考え方だと思ってい ます
  35. 35. No.Xⓒ 2016 UEC Tokyo. Think About It … AI
  36. 36. ⓒ 2012 UEC Tokyo.

WBA Future Leaders Casual Talk July/21th/2016

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