AI Free Panacea?

1. AI
Michał Łopuszyński
Orange R&D Workshop, Warsaw, 2017.05.10
DEEP LEARNING
BIG DATA
Free Panacea
?

2. Agenda or the most well-known facts about AI
AI is omnipotent•
AI is easy to apply•
AI is cheap•

3. Is AI omnipotent?

4. What AI can do?
Many executives ask me what artificial intelligence
can do. They want to know how it will disrupt their
industry and how they can use it to reinvent their
own companies. But lately the media has sometimes
painted an unrealistic picture of the powers of AI.
•
Andrew Ng
Stanford
Google
Baidu
Surprisingly, despite AI’s breadth of impact, the types
of it being deployed are still extremely limited. Almost
all of AI’s recent progress is through one type, in which
some input data (A) is used to quickly generate some
simple response (B).
•
Here’s one rule of thumb (...) If a typical person can do
a mental task with less than one second of thought, we
can probably automate it using AI either now or in the
near future.
••
Source: A. Ng, What AI Can and Can’t Do Right Now, Harvard Business Review, 09.11.2016Source: A. Ng, What AI Can and Can’t Do Right Now, Harvard Business Review, 09.11.2016

5. What AI can do?
Choosing A and B creatively has already
revolutionized many industries.
•
Andrew Ng
Stanford
Google
Baidu
Today’s supervised learning software has an
Achilles’ heel: It requires a huge amount of data.
You need to show the system a lot of examples of
both A and B.
For instance, building a photo tagger requires anywhere
from tens to hundreds of thousands of pictures (A)
as well as labels or tags telling you if there are people
in them (B).
•
Source: A. Ng, What AI Can and Can’t Do Right Now, Harvard Business Review, 09.11.2016

6. Is AI easy to apply?

7. Is AI easy to apply?
Sample headaches for AI models
Need for explanation•
Sooner or later your consumer will ask why your model gives certain
prediction. What will you answer?
•
Overfitting•
Perhaps your model is simply memorizing the (limited) training data?•
Data amount, data quality, data leaks•
Do you have enough labeled data?•
Are your data correct, complete, coherent, deduplicated, independent,
representative, up-to-date, stationary?
•
Are your attributes (A) fully protected from artificial leakage of labels (B)?•

8. Is AI cheap?

9. Is AI cheap? A parable

10. Is AI cheap? A parable
doi:10.1126/science.1248506
Great supplementary material is available for this paper! Check this link.

11. Is AI cheap? The moral
Paper from
NIPS 2015
conference
•
Acceptance
rate ~ 20 %
•
This is a real
problem with
ML/AI
•

12. Summary & conclusions

13. Summary – free panacea?
AI recently achieved a lot of success and transformed many
disciplines
•
However, media hype, advertising campaigns, etc. are light
years ahead from where we stand now
•
AI now not even remotely resembles free panacea (i.e., cheap,
one-stop solution for variety of problems)
•

14. Summary – a historical perspective
1.3 The History Of Artificial Intelligence
1.3.6 AI Becomes Industry (1980-present)
The first successful commercial expert system, R1, began
operation at the Digital Equipment Corporation (McDermott, 1982).
The program helped configure orders for new computer systems;
by 1986, it was saving the company an estimated 40 million
dollars a year. By 1988, DEC’s AI group had 40 expert systems
deployed, with more on the way. DuPont had 100 in use and 500
in development, saving an estimated 10 million dollars a year.
Nearly every major U.S.corporation had its own AI group and
was either using or investigating expert systems.
Overall, the AI industry boomed from a few million dollars in
1980 to billions of dollars in 1988, including hundreds of
companies building expert systems, vision systems, robots, and
software and hardware specialized for these purposes. Soon after
that came a period called the “AI Winter,” in which many
companies fell by the wayside as they failed to deliver on
extravagant promises.
...
...
AI, A modern
approach,
K. Russell,
P. Norvig

15. Summary – parting thoughts
Resist the hype, fight bullshit, stick to facts!•
Build solutions to problems, not AI solutions!•
Remember engineering!•
New AI winter will come, as predicted by the hype cycle, be prepared!•
My subjective prediction on AI now
VISIBILITY
TIME

16. Thank you!
Questions?
@lopusz