What is AI and how is it the key to harness our Digital Future?

1. AI: The New Electricity to Harness
Our Digital Future
Lindholmen Software Development
Day Oct. 26 2018.
Devdatt Dubhashi
LAB
(Machine Learning. Algorithms, Computational Biology)
Computer Science and Engineering
Chalmers

3. AI: the New Electricity
“AI is the new electricity.
Just as electricity transformed industry
after industry 100 years ago,
I think AI will do the same.”
Andrew Ng, Stanford, Baidu, Coursera

4. AI

5. • “I believe that at the end
of the century the use of
words and general
educated opinion will
have altered so much that
one will be able to speak
of machines thinking
without expecting to be
contradicted.”
― Alan Turing,
Computing Machinery
and Intelligence (1950)

6. Every aspect of learning or any other
feature of intelligence can in principle be so
precisely described that a machine can be
made to simulate it. An attempt will be
made to find how to make machines use
language, form abstractions and concepts,
solve kinds of problems now reserved for
humans, and improve themselves. We
think that a significant advance can be
made in one or more of these problems if a
carefully selected group of scientists work
on it together for a summer.
John McCarthy,
Dartmouth Workshop 1956

7. GOFAI
• Knowledge Representation
• First Order Logic and Theorem
Proving
• at(restaurant,Alice)
• at(restaurant,Bob)
• at(restaurant,Carol)
• works_at(restaurant,Carol)
• has_job(restaurant,waitress,Carol)
• orders(Bob,pizza)
• orders(Alice,sushi)
• forall X,Y,Z. orders(X,Y) and
has_job(restaurant,waitress,Z) ->
serves(Z,X,Y)
• serves(Carol,Bob,pizza)
serves(Carol,Alice,sushi)

8. Statistical Machine Learning
• Probabilistic models
• Statistical learning and
inference
• Data driven (no
hardcoded rules)

9. Deep Learning

10. Why Now?
Convergence of Technologies
• Data sensing,
acquisition revolution
• Rapid increase in
computing power
• Novel algorithms
• Software frameworks

11. Yann LeCun, NIPS 2016

15. Google Translate
reduce translation errors across its Google Translate service by between 55 percent and 85 percent

16. AI Revolution in NLP

17. • EU Parliament documents in multiple languages
• Bibles in multiple languages

18. Word senses and Machine Translation

20. Word sense induction
him political
her government
god influence state
came us act labour given
council about authority
energy unit system
battery x performance
high allows engine equipment
processing systems failure
management provide
Instance cloud for: 'power'
M. Kageback, F. Johansson,
R.Johansson, D. Dubhashi, “Neural
context embeddings for automatic
discovery of word senses”, (NAACL
2015 workshop on Vector Space
Modeling for NLP)
Used an innovative clustering
technique
Exploited word and context
vectors.

21. Document summarization
Word vectors
+ Multiple Kernel
learning
+ Submodular
optimization
M. Kågeback, O. Mogren
et al,
“Extractive Summarization
using Continuous Vector
Space Models”,
Workshop on
(CVSC) EACL 2014
Olof Mogren, et ql, “Extractiv
Summarization by Aggregatin
Multiple similarities”
RANLP 2015

25. Learning to Communicate
• Agents interact sending
messages to solve a
common task.
• Invent a language
grounded in real world
objects
E, Jorge, M. Kageback and E. Gustavsson, “Learning to play Guess Who? And inventing
a Grounded Language as a Consequence”, NIPS Deep RL Workshop (2016)

26. AI: NEW ELECTRICITY

27. Electricity and AI as General Purpose
Technologies• Wide scope for
improvement and
elaboration
• Application across a wide
range of uses
• Potential for use in a wide
variety of products and
processes
• Strong complementarities
with existing or potential
new technologies

28. Easy to Use and Improve

29. “Electricity transformed industries:
agriculture, transportation,
communication, manufacturing. I
think we are now in that phase
where AI technology has advanced
to the point where we see a clear
path for it to transform multiple
industries.”

30. Data is the New Oil!

32. X+AI

34. Complementary Technologies
CRISPR/CAS9 Gene Editing for
Life Sciences
Additive Manufacturing

35. MACHINE, PLATFORM, CROWD

36. Lessons from History
• Electric dynamo 19th
century
• Slow adoption and no
productivity gain until
1920
• Unprofitability of
replacing still serviceable
manufacturing plants
adapted to the old regime
of mechanical power
from steam.

37. Workplace Revolution
• switch from group drive
to unit drive
transmission
• Electric motors for all
machines and tools
• savings in fuel and in
energy efficiency
• single-story, linear
factory layouts
• reconfiguration of
machine placement

38. • Mind Machine
• Product Platform
• Core Crowd

40. Crowds

41. AI for Platforms and Crowds
• Data Driven Decisions
• NLP analytics to
understand
requirements from
demand side
• Semantic search and
match on supply side
• Other modalities:
conversational

42. Competence Intelligence