Academia to industry looking back on a decade of ml

ACADEMIA TO
INDUSTRY:
LOOKING BACK
ON A DECADE OF
ML
idalab seminar #14
DR. MIKIO BRAUN
@mikiobraun
AI ARCHITECT
ZALANDO SE
02-NOV-2018

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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

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MACHINE LEARNING: RESEARCH & INDUSTRY

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/

SEARCH PLATFORM

WAS I RIGHT?

WAS IT WHAT I EXPECTED?
🤔

• 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

ORGANIZATION OF WORK

HOW?

• 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

MACHINE LEARNING: LEARN FROM EXAMPLES

EXAMPLE: RECOMMENDATIONS

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

Typical algorithms:
• Collaborative
filtering,
• Content based
recommendation,
• Predicting customer‘s
next action.
EXAMPLE: RECOMMENDATIONS

Data Scientists and Developers

THE SCIENTIFIC METHOD

Very different approaches to
coding…
← developers
data scientists →
DS&D: Coding

• What is the most
productive way?
• Ideally, interface on
code, not just
documentation
• Production logs often
become data analysis
input!
DS&D: Collaboration

ADDING ML TO THE MIX

ORGANIZING DATA SCIENCE

27
SO… ACADEMIA VS.
INDUSTRY

RESEARCH IS ABOUT EXPLORATION

PUTTING IT INTO PRODUCTION

RESEARCH EXPLORES, INDUSTRY BUILDS

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NOW WHAT ABOUT
ARTIFICIAL
INTELLIGENCE?

MARVIN MINSKY: RECOGNIZING SIMPLE PICTURES
http://web.media.mit.edu/~minsky/papers/PR1971.html

AI TIMELINE PAST 20 YEARS (that’s 1998 till 2018)

• 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

• 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

• 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

• 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

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

• 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

• 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
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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

• 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

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

• 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

ALPHA GO: CONVOLUTIONAL NEURAL NETWORKS FOR POLICY AND VALUE PREDICTION

• 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

• 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

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