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A Primer on Artificial Intelligence (AI)
and Machine Learning (ML)
Yacine Ghalim
February 2017
2
Everyone is talking about it...
Data: 12k.co
12K Index – Number of Mentions of ”Artificial
Intelligence” in English Spea...
3
…in very contrasting and sensationalist ways…
What are AI and ML?
5
AI is a 61-year old branch of Computer Science that uses algorithms
and techniques to mimic human intelligence
6
The end goal of AI was (and still is) to build an Artificial Generalized
Intelligence holistically mimicking human intel...
7
Machine Learning is one of several techniques to get computers to
perform sophisticated cognitive tasks. It focuses on g...
8
The history of AI is a history of successive hype cycles about the
prospects of different techniques
Expert Systems
1980...
9
Machine Learning is a particularly interesting technique because it
represents a paradigm shift within AI
Traditional AI...
10
Example: excelling at playing the game of Go
Symbolic AI Mathematical/Statistical AI Machine Learning approach
“Let’s s...
11
Machine Learning is particularly good at solving 2 types of problems
where other AI techniques fail
? ?
? ?
?
?
Tasks p...
Why the new hype cycle?
13
In the past 5 years, we’ve seen unprecedented progress in solving
tough problems that defied our best efforts for 50+ y...
14
The confluence of 4 key factors is behind this new AI Renaissance
More Data
60 years of Research / Mature
Algorithms
Mo...
Where are we now?
16
We are seeing AI systems reaching equal to above human
performance at narrow tasks
Computer Performance
Human Performan...
17
Google researchers built a ML model as good at diagnosing diabetic
retinopathy as human doctors (Dec 2016) – soon in pr...
…and what we cannot do
(yet?)…
19
Deep Learning models still need a lot of training data to reach
state-of-the-art performance (for now)
Significant risk...
20
Deep Learning models are excellent at mimicking training data, but
we’re still far away from building systems that “lea...
21
Deep Learning models are excellent at performing narrow tasks but
we are still very very far away from generalized huma...
Investing in AI
23
AI/ML are the next major horizontal enabling technologies, just
like cloud, mobile or social. They will transform every...
24
…which is why a lot of money poured into companies focusing on AI
Data: Pitchbook ; Analysis: Sunstone
$194
$412 $507 $...
25
But investing in AI focused companies also has challenges –
Timing: it is increasingly difficult to filter signal from ...
26
An anecdote: #RocketAI - how to create a completely fake AI company
“worth” $M in a few hours
Source: https://medium.co...
27
Startups are competing against very aggressive incumbents that
have more $, data, and talent than startups can dream of...
Sunstone’s thesis
29
As always: problems come first ; beware of solutions looking for a
problem..
30
Large incumbents are much better positioned to build broad
horizontal AI products and infrastructure. But startups can ...
31
Data is a major source of defensibility. Access to a proprietary
dataset is a key component to build differentiated pro...
32
… and getting paid to collect a proprietary dataset is even better!
Get in touch !
Yacine Ghalim
yacine@sunstone.eu
@yacineghalim
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A primer on Artificial Intelligence (AI) and Machine Learning (ML)

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Over the past couple of years, we found ourselves investing in 7 AI and ML enabled companies, in areas as diverse as marketing, credit scoring, recruitment, fertility tracking and so on. It appears that we’ve been among the most active European investors in what most people today still view as a “theme”. Most importantly, more and more of our other portfolio companies are starting to adopt these technologies in order to make their products better.

What follows is a presentation that we gave to our LPs at our most recent investor day in February. We tried to give them a primer on these technologies: what they are ; why we are all talking about them now ; and how we, at Sunstone, are thinking about investing in those companies.

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A primer on Artificial Intelligence (AI) and Machine Learning (ML)

  1. 1. A Primer on Artificial Intelligence (AI) and Machine Learning (ML) Yacine Ghalim February 2017
  2. 2. 2 Everyone is talking about it... Data: 12k.co 12K Index – Number of Mentions of ”Artificial Intelligence” in English Speaking Tech Media
  3. 3. 3 …in very contrasting and sensationalist ways…
  4. 4. What are AI and ML?
  5. 5. 5 AI is a 61-year old branch of Computer Science that uses algorithms and techniques to mimic human intelligence
  6. 6. 6 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 world Moral Reasoning Emotional Intelligence Understanding Human Language Goal
  7. 7. 7 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 foundations Algorithms and data structures Artificial intelligence Communication and security Computer architecture Computer graphics Databases … Computer Science Decision Trees Bayesian inference Deep learning Reinforcement learning Support vector machines Random forest …
  8. 8. 8 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
  9. 9. 9 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 knowledge Ø 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
  10. 10. 10 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”
  11. 11. 11 Machine Learning is particularly good at solving 2 types of problems where other AI techniques fail ? ? ? ? ? ? Tasks programmers can’t describe Complex multidimensional problems that can’t be solved by numerical reasoning
  12. 12. Why the new hype cycle?
  13. 13. 13 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
  14. 14. 14 The confluence of 4 key factors is behind this new AI Renaissance More Data 60 years of Research / Mature Algorithms More Computing Power Open Source Frameworks/Libraries DSSTNE PaddlePaddle
  15. 15. Where are we now?
  16. 16. 16 We are seeing AI systems reaching equal to above human performance at narrow tasks Computer Performance Human Performance Time Performance we are here Performance at Given Narrow Task Over Time Source: Sunstone
  17. 17. 17 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
  18. 18. …and what we cannot do (yet?)…
  19. 19. 19 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 performance Increased chance of good generalization
  20. 20. 20 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
  21. 21. 21 Deep Learning models are excellent at performing narrow tasks but we are still very very far away from generalized human-like intelligence Déjà vu…
  22. 22. Investing in AI
  23. 23. 23 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)
  24. 24. 24 …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
  25. 25. 25 But investing in AI focused companies also has challenges – Timing: it is increasingly difficult to filter signal from noise Machine Learning / Deep Learning Blockchain tech VR Brain/Computer Interfaces Conversational UIs Autonomous vehicles Quantum Computing AR4D Printing 3D printing 2-5 years 5-10 years 10+ years Time to Plateau Data: Gartner 2016 Hype Cycle Gartner Hype Cycle 2016 (selected technologies)
  26. 26. 26 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. 27. 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 Hassabis 1.2BN MAUs $19BN net income Yann LeCun ; Joaquin Candela 2BN MAUs $10BN net income Andrew Ng 600M MAUs $5BN net income Hassan Sawaf 350M Active customer accounts $2.2BN net income Eric Horvitz ; Harry Shum 1.2BN office users, 500M LinkedIn profiles $15BN net income Ruslan Salakhutdinov 500M Apple users $40BN net income
  28. 28. Sunstone’s thesis
  29. 29. 29 As always: problems come first ; beware of solutions looking for a problem..
  30. 30. 30 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... Solvingaparticularindustry problem
  31. 31. 31 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
  32. 32. 32 … and getting paid to collect a proprietary dataset is even better!
  33. 33. Get in touch ! Yacine Ghalim yacine@sunstone.eu @yacineghalim

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