A primer on Artificial Intelligence (AI) and Machine Learning (ML)
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Transcript of A primer on Artificial Intelligence (AI) and Machine Learning (ML)
A Primer on Artificial Intelligence (AI) and Machine Learning (ML)
Yacine Ghalim
February 2017
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Everyone is talking about it...
Data: 12k.co
12K Index – Number of Mentions of ”Artificial Intelligence” in English Speaking Tech Media
3
…in very contrasting and sensationalist ways…
What are AI and ML?
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AI is a 61-year old branch of Computer Science that uses algorithms and techniques to mimic human intelligence
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The end goal of AI was (and still is) to build an Artificial Generalized Intelligence holistically mimicking human intelligence.
Logical Reasoning
Perceiving the world
Navigating and moving in the worldMoral Reasoning
Emotional Intelligence
Understanding Human Language
Goal
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Machine Learning is one of several techniques to get computers to perform sophisticated cognitive tasks. It focuses on giving computers the ability to perform those tasks without being explicitly programmed.
Symbolic AI (e.g. Expert Systems)
Probabilistic AI (e.g. Search & Optimization)
Machine learning
Mathematical foundationsAlgorithms and data structuresArtificial intelligenceCommunication and securityComputer architectureComputer graphicsDatabases …
Computer Science
Decision Trees Bayesian inference Deep learningReinforcement learningSupport vector machinesRandom forest…
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The history of AI is a history of successive hype cycles about the prospects of different techniques
Expert Systems
1980’s
Deep Learning
?Markov ModelsConnectionism
2012
AI Hype Cycles and AI Winters
1960’s
…
1970’s
Source: Wikipedia ; Analysis: Sunstone
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Machine Learning is a particularly interesting technique because it represents a paradigm shift within AI
Traditional AI techniques
Machine Learning
Data
Logic
Output
Ø Static – hard-coded set of steps and scenarios
Ø Rule Based – expert knowledgeØ No generalization – handling
special cases difficult
Ø Dynamic – evolves with data, finds new patterns
Ø Data driven– discovers knowledge
Ø Generalization – adapts to new situations and special cases
Data
Output
Logic
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Example: excelling at playing the game of Go
Symbolic AI Mathematical/Statistical AI Machine Learning approach
“Let’s sit down with the world’s best Go player, Lee Sedol, and put his
knowledge into a computer program”
“Let’s simulate all the different possible
moves and the associated outcomes at each single step and go with the most likely to
win”
“Let’s show millions of examples of real life
and simulated games (won and lost) to the
program, and let it learn from experience”
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Machine Learning is particularly good at solving 2 types of problemswhere other AI techniques fail
? ?
? ?
?
?
Tasks programmers can’t describeComplex multidimensional problems that
can’t be solved by numerical reasoning
Why the new hype cycle?
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In the past 5 years, we’ve seen unprecedented progress in solving tough problems that defied our best efforts for 50+ years.
Unprecedented Progress AI is Leaving the Lab and Being Deployed in the Wild
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The confluence of 4 key factors is behind this new AI Renaissance
More Data60 years of Research / Mature Algorithms
More Computing Power Open Source Frameworks/Libraries
DSSTNE
PaddlePaddle
Where are we now?
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We are seeing AI systems reaching equal to above human performance at narrow tasks
Computer Performance
Human Performance
Time
Perf
orm
ance
we are here
Performance at Given Narrow Task Over Time
Source: Sunstone
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Google researchers built a ML model as good at diagnosing diabetic retinopathy as human doctors (Dec 2016) – soon in production!
Source: http://jamanetwork.com/journals/jama/article-abstract/2588763
…and what we cannot do (yet?)…
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Deep Learning models still need a lot of training data to reach state-of-the-art performance (for now)
Significant risk of overfittingState of the art performanceIncreased chance of good generalization
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Deep Learning models are excellent at mimicking training data, but we’re still far away from building systems that “learn to learn” (for now)
Supervised Learning Unsupervised Learning
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Deep Learning models are excellent at performing narrow tasks but we are still very very far away from generalized human-like intelligence
Déjà vu…
Investing in AI
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AI/ML are the next major horizontal enabling technologies, just like cloud, mobile or social. They will transform every industry and make every product better
Infrastructure
Agriculture Education Healthcare Finance
Transportation
Legal
Industry
HR
Real Estate
Travel
Retail Advertising
SpaceGovernment
Energy
Solve complex multidimensional problems by looking for answers in the data
(large productivity gains, close to zero marginal cost)
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…which is why a lot of money poured into companies focusing on AI
Data: Pitchbook ; Analysis: Sunstone
$194$412 $507 $633
$1,982
$2,508
$3,247
$4,288
-
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
2010 2011 2012 2013 2014 2015 2016 2017* (ann.)
$ M
Funding into VC backed AI companies ($M)
17x
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But investing in AI focused companies also has challenges –Timing: it is increasingly difficult to filter signal from noise
Machine Learning / Deep LearningBlockchain tech
VR
Brain/Computer Interfaces
Conversational UIs
Autonomous vehicles
Quantum Computing
AR4DPrinting
3D printing
2-5 years
5-10 years
10+ years
Time to Plateau
Data: Gartner 2016 Hype Cycle
Gartner Hype Cycle 2016 (selected technologies)
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An anecdote: #RocketAI - how to create a completely fake AI company “worth” $M in a few hours
Source: https://medium.com/the-mission/rocket-ai-2016s-most-notorious-ai-launch-and-the-problem-with-ai-hype-d7908013f8c9#.44sbmx7xf
RocketAI Launch Party Metrics at NIPS 2016
27
Startups are competing against very aggressive incumbents that have more $, data, and talent than startups can dream of
Geoffrey Hinton ; Fei-Fei Li ; Demis Hassabis1.2BN MAUs
$19BN net income
Yann LeCun ; Joaquin Candela2BN MAUs
$10BN net income
Andrew Ng600M MAUs
$5BN net income
Hassan Sawaf350M Active customer accounts
$2.2BN net income
Eric Horvitz ; Harry Shum1.2BN office users, 500M LinkedIn profiles
$15BN net income
Ruslan Salakhutdinov500M Apple users$40BN net income
Sunstone’s thesis
29
As always: problems come first ; beware of solutions looking for a problem..
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Large incumbents are much better positioned to build broad horizontal AI products and infrastructure. But startups can thrive in vertical niches.
Solving broad AI problems: horizontal image/video/voice recognition, NLP, translation, AGI...
Solv
ing
a pa
rtic
ular
indu
stry
pr
oble
m
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Data is a major source of defensibility. Access to a proprietary dataset is a key component to build differentiated products.
More Unique
Data
More Accurate
Algorithm
Better Product
Larger Customer
Base
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… and getting paid to collect a proprietary dataset is even better!
Get in touch !Yacine Ghalim
[email protected]@yacineghalim