The victory of AlphaGo against Lee Sedol in March 2016 is a new milestone in Artificial Intelligence history, next one might be the mass adoption of self-driving cars or the generalized use of chatbots. These successes both raise expectations and increase the fear of an Artificial Intelligence getting out of control, which is now addressed in books and scientific papers. Even though some other human capabilities like natural language are still way beyond machine's reach, the fear of this "control problem" is sometimes intensified by the assumption that human intelligence will be at some point surpassed in all domains by machines even if it is a controverse whether human elementary abilities like subjective experience, consciousness, moral values are even theoretically transferable to computers. https://tech.rakuten.co.jp/

1. Technology Conference 2016
AI Playing Go and Driving
Cars, What’s Next?
Laurent Ach
Manager of
Rakuten Institute of Technology Paris
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2. 1979: Backgammon - Carnegie Mellon University
1997: Chess - IBM
2011: Jeopardy - IBM
2014: Image recognition - Google, Microsoft,
Facebook, CMU
2016: AlphaGo - Google DeepMind
Soon: Self Driving cars - Google, Tesla, Toyota…
Big trend now in media: chatbots!
(Update from Rakuten Technology Conference 2015)
Garry Kasparov picture by S.M.S.I., Inc. - Owen Williams,
The Kasparov Agency via Wikimedia Commons
More and more typical human tasks better done by computers
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3. Move #37
Michael Redmond:
“That’s a very surprising move!”
Chris Garlock:
“I thought it was a mistake”
Google DeepMind Challenge
Match 2, move 37
Lee Sedol vs AlphaGo
AlphaGo
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4. Any specific task?
Computers will outperform the most skilled humans
Next frontier: Natural Language understanding
Next Challenge for AI
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5. “Bots as the new interface” - Satya Nadella, Microsoft
CEO
“Threads are the new apps” - David Marcus,
Facebook, Head of Messaging Products
"A socialbot that can converse coherently for 20
minutes is unprecedented and at least five times more
advanced than state-of-the-art conversational AI”
(about students competition to create an artificial
intelligence) - Rohit Prasad, head scientist at Amazon
Alexa.
Screenshot from Sephora
chatbot in Kik app
Chatbots Are Coming
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6. > Hello, I am Eliza
* Hi Eliza, still around since 1966!
1966
The first chatbot with a few hundreds lines of code
2016
Integration of chatbots in messaging apps
• Google Allo messaging app, Google Home, Google Assistant
• Facebook Messenger Platform (wit.ai)
• Amazon Echo (Alexa), acquisition of Angel.ai
• Microsoft Bot Framework
• Oracle (as part of new cloud platform)
• Bots in WeChat, Viber, Telegram, Kik, Line, Slack…
From Eliza to Facebook
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7. What we have now:
• chatbots with predefined answers (manually engineered)
• automatic translation and mimicking dialogs: Google Translate, Microsoft Tay bot…
Machine: could you please let me know what is the operating system you are using ?
Human: linux
Machine: ok this is the issue with the system performance, nasser, tools.
Human: what is that?
Machine: is it a windows machine?
Machine: we have linux on your machine .
Machine: is it windows xp or windows 7?
Human: i am using linux
Machine: let me take a remote of your machine and check if issue has been pushed
properly.
(Excerpt from Google paper Neural Conversational Model, Jun. 2015)
Dialogs in Natural Language
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8. Picture of Symbolics 3640 lisp machine, by Michael L. Umbricht and
Carl R. Friend (Retro-Computing Society of RI), CC BY-SA 3.0
GOFAI (expert systems)
• was very limited by manual entries
• symbolic information
Data-driven dialogs
• very powerful
• difficult to combine with semantics
What is Missing in Dialogs with Chatbots
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9. Embeddings: converting texts and images into vectors (Word2Vec, Deep Learning…)
• Semantic distances
• Data driven semantics
Semantics from Images and Texts
at RIT
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10. Using Computer Vision to recognize product categories and create an easy selling
process
Now integrated in PriceMinister iOS app
Adapted in RIT Tokyo and integrated in Rakuma
Demo Video: http://bitly.com/RIT_SellIt
Image Analysis using Deep Learning
at RIT (QuickSell)
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11. Setting AI goals in natural language
(but Human language is not a formal system)
Concrete Problems in AI Safety (by Google Brain, and others)
Partnership on AI (Amazon, Google, Facebook, IBM, Microsoft)
Setting Goals for a Strong AI
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12. • brain = hardware
• mind = software
Computational Theory of Mind:
?
Semantics in Computers, Semantics in Brain
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13. The Chinese room argument
Computers and Semantics
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14. Computer Human understanding
Chinese
Picture of Chinese poet Xu Zhimo
Public domain, via Wikimedia Commons
Human not
understanding Chinese
我想坐飞机去北京。
什么时候？
我想坐飞机去北京。
什么时候？
我想坐飞机去北京。
什么时候？
The Chinese room argument
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15. John Searle (see Talk at Google)
- Computer intelligence is observer relative
- Computation is a fact of interpretation
- Computers are purely syntactical
- Simulation is not Replication
See their Exchange in 1999
John Searle picture by FranksValli [CC BY-SA 4.0], via Wikimedia Commons
Ray Kurzweil picture by Ed Schipul [CC BY-SA 2.0], via Wikimedia Commons
Ray Kurzweil
- Brain mechanisms are replicable in computers
- Computers will be conscious just like humans
- One day, upload / download our mind to / from a computer
- Immortality
Opposite Theories on Computer Intelligence
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16. • Probably no limits in AI capabilities to solve problems in
restricted domains
• Strong AI not on the horizon
• Fundamental differences between computers and human brain
What’s Next
Minds are Semantical
Computers are Syntactical
John Searle
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