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ACADEMIA TO
INDUSTRY:
LOOKING BACK
ON A DECADE OF
ML
idalab seminar #14
DR. MIKIO BRAUN
@mikiobraun
AI ARCHITECT
ZALANDO SE
02-NOV-2018
2
FROM ACADEMIA TO
INDUSTRY
3
FROM THINKING MACHINES… TO DETECTING CATS AND DOGS
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018
4
MACHINE LEARNING: RESEARCH & INDUSTRY
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20185
ZALANDO
• 17 Countries
• 7 locations in Europe
• 4.5 bn € revenue 2017
• ~15.000 employees
• IPO Oct 2014
https://geschaeftsbericht.zalando.de/2017/geschaeftsbericht/zahlen-daten-und-fakten/
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20186
SEARCH PLATFORM
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20187
WAS I RIGHT?
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20188
WAS IT WHAT I EXPECTED?
🤔
9
“DOING AI”
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201810
• What is out there: AI Hierarchy of needs + methods + tools = AI?
SO HOW DO YOU “DO AI?”
https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201811
ORGANIZATION OF WORK
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201812
HOW?
13
FROM BUSINESS TO
PRODUCT
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201814
• Customer has a Job to be
done.
• Business has to give a
Solution that adds value.
• Solution consists of
Activities, and some of that
activities might be solved
with ML.
• ML: solve a task by learning
from examples.
FROM BUSINESS PROBLEMS TO MACHINE LEARNING PROBLEMS
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201815
MACHINE LEARNING: LEARN FROM EXAMPLES
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201816
EXAMPLE: RECOMMENDATIONS
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201817
Typical problem for machine learning:
• Hard to specify what exactly means “similar.”
• A lot of example data is available.
• Recommendations have to change based on new articles
frequently.
EXAMPLE: RECOMMENDATION
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201818
Typical algorithms:
• Collaborative
filtering,
• Content based
recommendation,
• Predicting customer‘s
next action.
EXAMPLE: RECOMMENDATIONS
19
WORKING IN TEAMS
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201820
Data Scientists and Developers
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201821
THE SCIENTIFIC METHOD
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201822
Very different approaches to
coding…
← developers
data scientists →
DS&D: Coding
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201823
• What is the most
productive way?
• Ideally, interface on
code, not just
documentation
• Production logs often
become data analysis
input!
DS&D: Collaboration
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201824
ADDING ML TO THE MIX
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201825
ADDING ML TO THE MIX
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201826
ORGANIZING DATA SCIENCE
27
SO… ACADEMIA VS.
INDUSTRY
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201828
RESEARCH IS ABOUT EXPLORATION
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201829
PUTTING IT INTO PRODUCTION
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201830
RESEARCH EXPLORES, INDUSTRY BUILDS
31
NOW WHAT ABOUT
ARTIFICIAL
INTELLIGENCE?
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201832
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201833
MARVIN MINSKY: RECOGNIZING SIMPLE PICTURES
http://web.media.mit.edu/~minsky/papers/PR1971.html
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201834
AI TIMELINE PAST 20 YEARS (that’s 1998 till 2018)
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201835
• Initially approached AI like any
other problem computers could
solve.
• Alternatively, using an approach
inspired by human biology.
• Machine Learning added a
statistical approach to the mix.
• Recently, Deep Learning has led to
impressive improvements.
APPROACHES IN ARTIFICIAL INTELLIGENCE
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201836
• Classical approach is to specify
what the input/output relation is,
then devise programs to solve
that.
• Machine Learning replaces that
with examples (+ a cost
function).
• Training then means to infer a
model that generalizes well on
future data.
BIRD’S EYE VIEW OF MACHINE LEARNING
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201837
• Artificial Intelligence is the overarching goal or challenge.
• Machine Learning is one approach that has proven very successful if the problem
itself cannot be specified easily.
ARTIFICIAL INTELLIGENCE VS. MACHINE LEARNING
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201838
• Many reasons why:
convenience, security,
disrupting mobility.
• Current approaches are a mix
of many systems, some of
which make heavy use of
machine learning.
• Deep Learning very successful
for computer vision and image
analysis.
AUTONOMOUS DRIVING
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201839
Autonomous driving is based on a mix of sensors with quite
different capabilities to improve reliability.
• Sonar/Radar: Cheap, low resolution, works well under
extreme weather and in darkness, can estimate velocity.
• Camera: Cheap, very high resolution, similar to what we
humans see.
• Lidar (light detection and ranging): expensive, very
accurate depth maps.
SENSORS IN AUTONOMOUS DRIVING
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201840
• Companies like Waymo do extensive data
collection and simulation to evaluate and tune
the system.
• Not just for training ML methods, also for overall
systems testing.
ML inspired approach to defining the problem, but mix
of ML and explicit solutions.
(Waymo lecture at MIT)
DATA-DRIVEN APPROACH TO AUTONOMOUS DRIVING
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018
• Not that much ML in
there (at least right
now).
• Dialog is done through
Frames that capture a
piece of information
required and an
analysis part that maps
user input to fields.
• ML used for
understanding
speech2text, named
entity recognition,
analysis
41
CHATBOTS
Example 1:
A: “I’d like to book a flight tomorrow”
B: “From where to where do you want to fly?”
A: “From London to Berlin.”
B: “With how many passengers?”
A: “Just me.”
B: “Okay, so I have one passenger for a flight
from London to Berlin tomorrow. Is that
correct?”
A: “Yes.”
B: …
Example 2:
A: “I’d like to book a flight for me
tomorrow from London to Berlin.”
B: …
Booking a flight:
Frame:
- when: Date
- start, end: Location
- how many persons: Number
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201842
• Machine Learning
used especially on
“perception” part.
• Core is rule based
system.
• Potential to improve
those based on
examples, too, same
for text2speech.
CHATBOT SYSTEM OVERVIEW
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201843
Recommendations as an AI problem:
• Understand what the user is
looking for right now. What is his
intent, what is in his mind?
• Technically, predict next action.
• Quite involved, dealing with real-
time data, etc.
RECOMMENDATIONS
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201844
• From computer science’s point
of view, strategy games are
“easy” if you know the value of
each state.
• Cleverly simulating “plausible
actions” leads to speedup
(Monte Carlo tree search)
ALPHA GO AND OTHER STRATEGY GAMES
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201845
ALPHA GO: CONVOLUTIONAL NEURAL NETWORKS FOR POLICY AND VALUE PREDICTION
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201846
• Machine Learning used as part of the system
• Otherwise lots of engineering and “classical approaches”
• Main difference IMHO is “data driven” vs. “spec driven” approach.
SOME MODERN “AI” SYSTEMS
MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201847
• Image recognition almost on human level performance.
• Outperforms humans on strategy games.
• Able to learn features by itself.
• Can act intelligently and autonomously in the real world.
YES!
What is Deep Learning - StrataData New York, 2017 - Mikio Braun48
• From raw data to symbolic representations?
• Capability to self reflect?
• What happens inside the machine? Is it just imitating?
• Learning from single examples.
NO!
MIKIO BRAUN
mikio.braun@zalando.de
02-NOV-2018
DX INFORMATION
AI ARCHITECT
This presentation and its contents are strictly confidential. It may not, in
whole or in part, be reproduced, redistributed, published or passed on to
any other person by the recipient.
The information in this presentation has not been independently verified. No
representation or warranty, express or implied, is made as to the accuracy
or completeness of the presentation and the information contained herein
and no reliance should be placed on such information. No responsibility is
accepted for any liability for any loss howsoever arising, directly or
indirectly, from this presentation or its contents.
DISCLAIMER
50

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Academia to industry looking back on a decade of ml

  • 1. ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF ML idalab seminar #14 DR. MIKIO BRAUN @mikiobraun AI ARCHITECT ZALANDO SE 02-NOV-2018
  • 3. 3 FROM THINKING MACHINES… TO DETECTING CATS AND DOGS MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018
  • 4. 4 MACHINE LEARNING: RESEARCH & INDUSTRY MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018
  • 5. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20185 ZALANDO • 17 Countries • 7 locations in Europe • 4.5 bn € revenue 2017 • ~15.000 employees • IPO Oct 2014 https://geschaeftsbericht.zalando.de/2017/geschaeftsbericht/zahlen-daten-und-fakten/
  • 6. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20186 SEARCH PLATFORM
  • 7. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20187 WAS I RIGHT?
  • 8. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 20188 WAS IT WHAT I EXPECTED? 🤔
  • 10. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201810 • What is out there: AI Hierarchy of needs + methods + tools = AI? SO HOW DO YOU “DO AI?” https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007
  • 11. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201811 ORGANIZATION OF WORK
  • 12. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201812 HOW?
  • 14. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201814 • Customer has a Job to be done. • Business has to give a Solution that adds value. • Solution consists of Activities, and some of that activities might be solved with ML. • ML: solve a task by learning from examples. FROM BUSINESS PROBLEMS TO MACHINE LEARNING PROBLEMS
  • 15. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201815 MACHINE LEARNING: LEARN FROM EXAMPLES
  • 16. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201816 EXAMPLE: RECOMMENDATIONS
  • 17. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201817 Typical problem for machine learning: • Hard to specify what exactly means “similar.” • A lot of example data is available. • Recommendations have to change based on new articles frequently. EXAMPLE: RECOMMENDATION
  • 18. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201818 Typical algorithms: • Collaborative filtering, • Content based recommendation, • Predicting customer‘s next action. EXAMPLE: RECOMMENDATIONS
  • 20. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201820 Data Scientists and Developers
  • 21. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201821 THE SCIENTIFIC METHOD
  • 22. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201822 Very different approaches to coding… ← developers data scientists → DS&D: Coding
  • 23. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201823 • What is the most productive way? • Ideally, interface on code, not just documentation • Production logs often become data analysis input! DS&D: Collaboration
  • 24. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201824 ADDING ML TO THE MIX
  • 25. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201825 ADDING ML TO THE MIX
  • 26. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201826 ORGANIZING DATA SCIENCE
  • 28. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201828 RESEARCH IS ABOUT EXPLORATION
  • 29. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201829 PUTTING IT INTO PRODUCTION
  • 30. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201830 RESEARCH EXPLORES, INDUSTRY BUILDS
  • 32. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201832
  • 33. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201833 MARVIN MINSKY: RECOGNIZING SIMPLE PICTURES http://web.media.mit.edu/~minsky/papers/PR1971.html
  • 34. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201834 AI TIMELINE PAST 20 YEARS (that’s 1998 till 2018)
  • 35. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201835 • Initially approached AI like any other problem computers could solve. • Alternatively, using an approach inspired by human biology. • Machine Learning added a statistical approach to the mix. • Recently, Deep Learning has led to impressive improvements. APPROACHES IN ARTIFICIAL INTELLIGENCE
  • 36. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201836 • Classical approach is to specify what the input/output relation is, then devise programs to solve that. • Machine Learning replaces that with examples (+ a cost function). • Training then means to infer a model that generalizes well on future data. BIRD’S EYE VIEW OF MACHINE LEARNING
  • 37. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201837 • Artificial Intelligence is the overarching goal or challenge. • Machine Learning is one approach that has proven very successful if the problem itself cannot be specified easily. ARTIFICIAL INTELLIGENCE VS. MACHINE LEARNING
  • 38. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201838 • Many reasons why: convenience, security, disrupting mobility. • Current approaches are a mix of many systems, some of which make heavy use of machine learning. • Deep Learning very successful for computer vision and image analysis. AUTONOMOUS DRIVING
  • 39. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201839 Autonomous driving is based on a mix of sensors with quite different capabilities to improve reliability. • Sonar/Radar: Cheap, low resolution, works well under extreme weather and in darkness, can estimate velocity. • Camera: Cheap, very high resolution, similar to what we humans see. • Lidar (light detection and ranging): expensive, very accurate depth maps. SENSORS IN AUTONOMOUS DRIVING
  • 40. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201840 • Companies like Waymo do extensive data collection and simulation to evaluate and tune the system. • Not just for training ML methods, also for overall systems testing. ML inspired approach to defining the problem, but mix of ML and explicit solutions. (Waymo lecture at MIT) DATA-DRIVEN APPROACH TO AUTONOMOUS DRIVING
  • 41. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 2018 • Not that much ML in there (at least right now). • Dialog is done through Frames that capture a piece of information required and an analysis part that maps user input to fields. • ML used for understanding speech2text, named entity recognition, analysis 41 CHATBOTS Example 1: A: “I’d like to book a flight tomorrow” B: “From where to where do you want to fly?” A: “From London to Berlin.” B: “With how many passengers?” A: “Just me.” B: “Okay, so I have one passenger for a flight from London to Berlin tomorrow. Is that correct?” A: “Yes.” B: … Example 2: A: “I’d like to book a flight for me tomorrow from London to Berlin.” B: … Booking a flight: Frame: - when: Date - start, end: Location - how many persons: Number
  • 42. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201842 • Machine Learning used especially on “perception” part. • Core is rule based system. • Potential to improve those based on examples, too, same for text2speech. CHATBOT SYSTEM OVERVIEW
  • 43. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201843 Recommendations as an AI problem: • Understand what the user is looking for right now. What is his intent, what is in his mind? • Technically, predict next action. • Quite involved, dealing with real- time data, etc. RECOMMENDATIONS
  • 44. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201844 • From computer science’s point of view, strategy games are “easy” if you know the value of each state. • Cleverly simulating “plausible actions” leads to speedup (Monte Carlo tree search) ALPHA GO AND OTHER STRATEGY GAMES
  • 45. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201845 ALPHA GO: CONVOLUTIONAL NEURAL NETWORKS FOR POLICY AND VALUE PREDICTION
  • 46. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201846 • Machine Learning used as part of the system • Otherwise lots of engineering and “classical approaches” • Main difference IMHO is “data driven” vs. “spec driven” approach. SOME MODERN “AI” SYSTEMS
  • 47. MIKIO BRAUN: “ACADEMIA TO INDUSTRY: LOOKING BACK ON A DECADE OF AI” IDALAB SEMINARS #14, BERLIN, NOV 2, 201847 • Image recognition almost on human level performance. • Outperforms humans on strategy games. • Able to learn features by itself. • Can act intelligently and autonomously in the real world. YES!
  • 48. What is Deep Learning - StrataData New York, 2017 - Mikio Braun48 • From raw data to symbolic representations? • Capability to self reflect? • What happens inside the machine? Is it just imitating? • Learning from single examples. NO!
  • 50. This presentation and its contents are strictly confidential. It may not, in whole or in part, be reproduced, redistributed, published or passed on to any other person by the recipient. The information in this presentation has not been independently verified. No representation or warranty, express or implied, is made as to the accuracy or completeness of the presentation and the information contained herein and no reliance should be placed on such information. No responsibility is accepted for any liability for any loss howsoever arising, directly or indirectly, from this presentation or its contents. DISCLAIMER 50

Editor's Notes

  1. - Always interested in AI and Model of the Mind. - As soon as I could => ML Just about prediction But that is actually great => what is the core of ML?
  2. - Name - Position - Story of Academia to Industry and what I learned about ML
  3. Fulfillment center == warehouse 135M visits/month =~ 450K visits/day Active Customer: Anyone who has placed an order in the past 12 months ⅓ of 9000 in Berlin
  4. Spend some time
  5. Explore Explore islands, look for loops Global system to facilitate: peer reviewed publications focussed on novelty => something is missing. Startup on the side Pilot projects Two engs, didn’t take off => industry
  6. ML at the core Layer of infrastructure to deliver product Automate what was one off Research still there, at product level => so how do we compare?
  7. research explores / industry builds Simple stuff works best, because research is often exploring Similar to toolsmiths, but overlooked because everything is software => Leaps: Deep Learning
  8. This is eniac. Ever since people built the first computers, the idea was to build a machine that thinks, to understand the mechanics of mental work, but we didn’t really know what this is.
  9. Famously, Marvin Minsky in 1966 posed this as a summer project, and years later it wasn’t solved.