AI and digitalization

1. AI & Digitaization:
#digichalmers
Aug. 29 2018
Devdatt Dubhashi
LAB
(Machine Learning. Algorithms, Computational Biology)
Computer Science and Engineering
Chalmers

2. 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

3. “Electricity ,
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.”

4. Data is the New Oil!

6. Why Now?
Convergence of Technologies
• Digitalization
• Rapid increase in
computing power
• Novel algorithms
• Software frameworks

7. AI/MACHINE LEARNING

8. • “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)

9. 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

10. AI/Machine Learning
– How can one construct
computer systems that
automatically improve through
experience?
– What are the fundamental
statistical-computational-
information-theoretic laws that
govern all learning systems?
• Fundamental scientific and
engineering questions and
highly practical computer
software inmany applications.

11. Three Paradigms of ML

12. Supervised Learning

15. Unsupervised Learning

16. Unsupervised Learning of Word senses
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'
Learn the different senses
of a word from raw text
without any training data

17. Document summarization
Automatically extract a
Summary of documents
From raw text without
any supervision.

18. Reinforcement Learning

20. Data Science Division (CSE)
Genomics Systems
Biology
Structural
Biology
Algorithms
Big
Data
Health
Informatics
Visual
Analytics
Deep
Learning
Bio
Informatics
https://www.chalmers.se/en/departments/cse/organisation/ds/Pages/default.aspx
Facts:
• 260+ department members
• All areas of computer science
• Extensive experience:
• Algorithms for big data
• Large-scale machine
learning & AI
• (Deep Learning, Bayesian
methods)
• Collaborations:
• Basic biology
• Clinical applications
• Industry
• Funding:
• NIH / NCI, VR, SSF, EU,
Vinnova,Industry
Mobile
Health
Example Projects:
• Compressive genomics:
analyzing massive DNA
sequencing data sets
• Mobile health: Personalized
management of diabetes
• Algorithms for learning from and
integrating networks
Teaching:
• ML/AI classes
• MS Programs in
(Applied) Data Science
at GU and Chalmers
Dept of Computer Science and Engg.
AI and Data Science

25. Mobile Health: Deep
Learning for Diabetes
Management
• Continuous Glucose Monitoring &
Insulin dosage, Carbohydrate intake
(Future: activity, circadian rhythm,
genetic factors,…)
• Deep Learning (RNN) for
personalized precision predictions
and suggestion of interventions
• Contact: Devdatt Dubhashi, Alexander Schliep
Recurrent Neural Network (RNN)
Suggest
Interventio
n
Predict Glucose Level
https://www.chalmers.se/en/departments/cse/organisation/ds/Pages/default.aspx
Data Science Division (CSE)

26. ML for language processing
Methods based on Deep
Learning for information
extraction and
summarization tasks
For instance: extracting
entities in medical text,
automatic summarization
Contact: Richard Johansson, Devdatt
Dubhashi
https://www.chalmers.se/en/departments/cse/organisation/ds/Pages/default.aspx
Data Science Division (CSE)

27. Learning from and
integrating network data
• Improved Algorithms (probabilistic graph
kernel) and optimization techniques for
network data
• Application: Drug targets and interactions
from large-scale functional screen in
humanized yeast (Anders Blomberg (GU) w/
Boone lab)
• Contact: Devdatt Dubhashi, Alexander Schliep
https://www.chalmers.se/en/departments/cse/organisation/ds/Pages/default.aspx
Data Science Division (CSE)

28. AI + DIGITALIZATION: CHALLENGES

29. Horizon (100 years): Superintelligence

30. Horizon (20 years): Automation

31. • “ … we really have to
think through the
economic implications.
Because most people
aren’t spending a lot of
time right now worrying
about singularity—they
are worrying about “Well,
is my job going to be
replaced by a machine?”
WIRED Nov. 2016
D. Dubhashi and S. Lappin (2017)
“AI Dangers: Real and Imagined”
Comm. ACM, Vol. 60 No. 2, Pages
43-45

32. A Spectre is Haunting the World
“Greatest problem of 21st century Economics is what to do with surplus humans.”
Yuval Noah Harari, Homo Deus: History of the Future (2016)

34. Today

35. The Vision…
All pervasive cognitive computing
agents .

36. • AI will contribute as much
as $15.7 trillion to the world
economy by 2030 (PwC)
• $6.6 trillion from increased
productivity as businesses
automate processes and
augment with new AI
technology, and $9.1 trillion
from consumption side-
effects as shoppers snap up
personalized and higher-
quality goods