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

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…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

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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

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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

yacine@sunstone.eu@yacineghalim