Predictions and Hard Problems With AI
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Transcript of Predictions and Hard Problems With AI
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Rakuten Technology Conference, Tokyo, October 28, 2017
Laurent Ach
Manager of Rakuten Institute of Technology Paris
CTO PriceMinister - Rakuten
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• GIZMODO, article by George Dvorsky, Oct. 18, 2017, http://bit.ly/2gtk1S2
• VentureBeat, article by John Brandon, Oct. 2, 2017, http://bit.ly/2xLJoFl
• The Sun, article by James Beal and Andy Jehring, Aug. 1, 2017, http://bit.ly/2w0fVUq
• Mirror, article by Louise Sassoon, Aug. 1, 2017, http://bit.ly/2whUI7G
• TECH TIMES, article by Aaron Mamiit, Jul. 30, 2017, http://bit.ly/2wdoH0A
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Ray Kurzweil
Nick Bostrom
Intelligence explosion (I.J. Good, 1965)
Consulted by S. Kubrick for 2001: A Space Odyssey (1968)
Science fiction movies become reality… at least in predictions!
2006: The Singularity Is Near (Ray Kurzweil)
2014: SuperIntelligence (Nick Bostrom)
Stanley Kubrick
• Ray Kurzweil picture by Ed Schipul [CC BY-SA 2.0], via Wikimedia Commons
• Nick Bostrom picture by Future of Humanity Institute [CC BY-SA 4.0], via Wikimedia Commons
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Inte
llig
en
ce
(w
ait, w
ha
t?)
Time
Human Intelligence
Artificial Intelligence
Artificial General Intelligence
Artificial Super Intelligence
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Brain as Hardware? Mind as Software?
• Digicomp picture by Pterre [CC BY-SA 3.0 or GFDL], via Wikimedia Commons
• Punched card picture by Mutatis mutandis [GFDL, CC-BY-SA-3.0 or CC BY 2.5], via Wikimedia Commons
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Super Intelligent machines in conflict with Humans
or
Stupid machines, with too much decision power
“[…] we fight to make the machine slightly more intelligent, but they are still so stupid.
[…] The thing I’m more worried about, in a foreseeable future, is not computers taking
over the world. I’m more worried about misuse of AI”
Yoshua Bengio, in MIT Technology Review, January 29, 2016
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Features
Label
(what it is, semantics)
TrainingSupervised
learning
Unsupervised
learning
Features
Training
Clustering
(need a human to
add semantics)
apple /
pear /
banana
attributes (size, color, weight, …)
picture (raw pixels)
text description
Objects
Objects
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Using deep learning, computing semantic distances
Similar meanings
Transformation into vectors,
using a very big neural network
Pictures
Pictures
PicturesPicture
or Text
“low” dimension vectormillion dimensions vector
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1 + 1 = 2
Bits00000010
Computer Memory
(hardware)
Data2
This is “two”,
(useful to count things!)
This does not mean anything
for a computer
Human interpretation
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1. Deep Learning and Reinforcement Learning need to train on millions of examples
(see AlphaGoZero)
2. Computers don't know how the world works, have no “common sense”
3. No generalization capability: AI today is only narrow intelligence
4. Without human interpretation, there is no intelligence in Artificial Intelligence
“The definition of today’s AI is a machine that can make a perfect chess move while the room
is on fire.” - A sentence from the ’70s quoted by Fei-Fei Li.
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John Searle: “A program merely manipulates symbols, whereas a brain attaches
meaning to them” (1990)
David Chalmer, distinguishes
the easy problem and the hard
problem of consciousness (1994)
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Reductive Materialism
(mind explained by brain events)
Eliminativism
(consciousness does not exist)
Panpsychism
(consciousness is everywhere)
Integrated Information Theory
(everything is information)
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Dualism
(mind + body)
centuries of fight
against dualism
subjective experience
remains a mystery for
objective sciences
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