The AI Revolution will not be Monopoilized

Who's going to "win at AI"? There are now several large companies eager to claim that title. Others say that China will take over, leaving Europe and the US far behind. But short of true Artificial General Intelligence, there's no reason to believe that machine learning or data science will have a single winner. Instead, AI will follow the same trajectory as other technologies for building software: lots of developers, a rich ecosystem, many failed projects and a few shining success stories.

Transcript

1. The AI Revolution
   will not be Monopolized
   Ines Montani
   Explosion AI
   

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2. Open-source library for
   industrial-strength Natural
   Language Processing in Python
   

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3. Open-source library for
   industrial-strength Natural
   Language Processing in Python
   Company and digital
   studio, bootstrapped
   with consulting
   

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4. Open-source library for
   industrial-strength Natural
   Language Processing in Python
   Company and digital
   studio, bootstrapped
   with consulting
   First commercial product:
   radically efficient data collection
   and annotation tool, powered
   by active learning
   

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5. Open-source library for
   industrial-strength Natural
   Language Processing in Python
   Company and digital
   studio, bootstrapped
   with consulting
   First commercial product:
   radically efficient data collection
   and annotation tool, powered
   by active learning
   You are here!
   

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6. Open-source library for
   industrial-strength Natural
   Language Processing in Python
   Company and digital
   studio, bootstrapped
   with consulting
   First commercial product:
   radically efficient data collection
   and annotation tool, powered
   by active learning
   Extension platform with a SaaS
   layer to help users scale up
   annotation projects
   SCALE
   You are here!
   

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7. Open-source library for
   industrial-strength Natural
   Language Processing in Python
   Company and digital
   studio, bootstrapped
   with consulting
   First commercial product:
   radically efficient data collection
   and annotation tool, powered
   by active learning
   Coming soon: pre-trained,
   customisable models for a variety
   of languages and domains
   You are here!
   Extension platform with a SaaS
   layer to help users scale up
   annotation projects
   SCALE
   

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22. The concept of an “AI race”
    is hopelessly confused.
    

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23. Implications of an “AI race”
    races are competitive
    races have winners and losers
    races have a start and an end
    

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24. What do we mean by AI?
    

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25. What do we mean by AI?
    Specific consumer products?
    

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26. What do we mean by AI?
    Specific consumer products?
    “Artificial General Intelligence”?
    

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27. What do we mean by AI?
    Specific consumer products?
    “Artificial General Intelligence”?
    A technocratic dictatorship?
    

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28. What do we mean by AI?
    Specific consumer products?
    “Artificial General Intelligence”?
    A technocratic dictatorship?
    A robot army?
    

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29. What do we mean by AI?
    Specific consumer products?
    “Artificial General Intelligence”?
    A technocratic dictatorship?
    A robot army?
    Machine Learning research?
    

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30. Monopolizing a new 

    product category?
    

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31. Monopolizing a new 

    product category?
    Lots of new products will use machine learning
    Many will be monopolized. By who? Fair question!
    If different companies make all these products,
    who “won” at AI?
    

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32. Being the first to develop
    “Artificial General Intelligence”?
    

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33. Being the first to develop
    “Artificial General Intelligence”?
    Very hard to extrapolate from here to proper AGI
    If someone develops AGI, will it even matter who?
    

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34. Being the first to develop
    “Artificial General Intelligence”?
    Whatever you believe about AGI...
    “AGI is science fiction”

    Okay, so there’s nothing to win
    “AGI is an existential threat”

    Okay, so nobody will win
    “AGI will solve all our problems”

    Okay, so everybody wins?
    

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35. Using new technology

    to oppress people?
    

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36. Using new technology

    to oppress people?
    Oppression is a risk we should talk about in AI
    But hardly a race we want to win!
    

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37. Winning a literal arms race?
    

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38. Winning a literal arms race?
    Government research will follow, not lead
    If everyone publishes openly, nobody will pull far ahead
    

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39. Publishing the most

    machine learning research?
    

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40. Publishing the most

    machine learning research?
    Open research is collaborative, not competitive
    If research is published, everyone wins
    

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41. Why don’t companies
    like Google keep their 

    research secret?
    

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42. Reasons companies publish
    Attract talent. You can’t get the best
    researchers if you don’t let them publish
    Inevitability. Trying to lock down the secrets
    wouldn’t work anyway
    Leverage. A higher “AI waterline” is good for
    their business
    

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43. # Company / Institution Total Papers %
    1 Google 60 8.8
    2 Carnegie Mellon University 48 7.1
    3 Mass. Institute of Technology 43 6.3
    4 Microsoft 40 5.9
    5 Stanford University 39 5.7
    6 University of CA, Berkeley 35 5.2
    7 Deepmind 31 4.6
    8 University of Oxford 22 3.2
    9 University of Illinois 20 2.9
    10 Georgia Institute of Technology 18 2.7
    Source: NIPS Accepted Papers Stats by Robbie Allen (Medium)
    Nobody “dominates” 

    machine learning research
    

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44. Original source: gpo.gov
    NOV
    20
    

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45. But what about the data?
    Reusing data is like reusing code. If it doesn’t
    do what you want, it’s not very useful.
    Personal data matters when it’s about you
    personally.
    General knowledge is easy to acquire. It
    doesn’t need unique proprietary datasets.
    

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46. Data alone won’t grant 

    anyone a monopoly
    Data has diminishing returns
    Making your dataset 10× bigger doesn’t 

    make it 10× better
    It’s just not that expensive! What datasets cost
    more than a manufacturing plant?
    

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47. We won’t all be buying
    “AI” from the AI Store.
    

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48. 

    Companies are in-housing
    Machine learning is software development, 

    it needs to evolve with the project
    Nobody has a monopoly on AI expertise, 

    people are learning quickly
    Owning and controlling the data is crucial for 

    many applications
    

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49. Vendors can provide

    products, not magic
    The challenge is taking what’s theoretically
    possible and applying it to a problem
    What matters is making the right decisions for
    the larger application
    

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50. 

    Buyers aren’t old and stupid
    Tech illiterate management stereotype is outdated
    Most companies let developers choose their tools
    Developers strongly prefer open technologies:
    more flexible, better career growth
    

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51. Rich open-source ecosystem
    Library GitHub URL GitHub Stars
    TensorFlow tensorflow/tensorflow 115k
    scikit-learn scikit-learn/scikit-learn 32k
    PyTorch pytorch/pytorch 21k
    XGBoost dmlc/xgboost 14k
    spaCy explosion/spacy 11k
    Gensim RaRe-Technologies/gensim 8k
    NLTK nltk/nltk 7k
    NLTK
    Source: GitHub (November 2018)
    

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52. So, who’s going to
    win at AI then?
    

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53. ✊ The AI revolution will not 

    be monopolized
    There’s no single “AI race” – lots of people are
    building lots of things
    There’s no magic solution waiting to be
    discovered
    Nothing about machine learning suggests a
    monopoly or a winner-takes-all market
    

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54. Thanks!
    Explosion AI

    explosion.ai
    Follow us on Twitter

    @_inesmontani

    @explosion_ai
    

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