machine intelligence landscape 2016
TRANSCRIPT
MACHINE INTELLIGENCELANDSCAPE2016@SHIVON @JAMES CHAMNOVEMBER 8, 2016 // SAN FRANCISCO
2014
2015
2016
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WHAT’S CHANGED
20145 Groups35 Categories233 Companies
Focused on academics and big tech companies
20158 Groups34 Categories265 Companies
Focused on startups and new autonomous systems
20168 Groups38 Categories318 Companies
Focused on companies trying to understand machine intelligence
READY, PLAYER
ONE
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ATARI BREAKOUT
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GO
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THE MI ENTERPRISE
WHY EVEN BOT-HER?
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HOW THEY FAIL
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AGENTS VS. BOTS
CYBORGS SYNTHESIZERS COORDINATORS
TRANSACTORS COMPANIONS DIAGNOSTICIANS
AGENTS
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THE ENTEPRISE OPPORTUNITY: SUPPORT STAFF FOR EVERYONE> Schedulers: Clara and x.ai> Handlers: Google Now, Siri,
and Cortana> Coordinators: Howdy,
Standup Bot, Tatsu, and Geekbot
> Notetakers: Gridspace Sift, Evernote and Pogo
> Copy Editors: Textio and IBM’s Watson Tone Analyzer.
ON TO 11111000001
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THE NEW STACK
Questions from big companies about Machine Intelligence lead to a new stack—and the real risk of AI
CODE DATA
MI IS DIFFERENT FROM SOFTWARE
CODE DATADATADATALOTS of DATA
THE CURRENT STATE OF IT
CODE DATAMODELS
ADDING MI TO IT
CODE DATAMODELS
Generated by code using dataDifferent from traditional SWMore flexible, tooHarder (impossible?) to grokHarder (impossible?) to verifyWhen to trust?
LEADING TO NEW QUESTIONS
CODE DATAMODELSMODELSMODELSLOTS of MODELS
Iterates faster than codeBuy or buildCan improve quickly or subtlygo very, very wrongWhere should you deploy?
AND NEW ISSUES
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MICRO-ECONOMIC MODELS FOR MACHINE INTELLIGENCE
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WHAT HAPPENS WHEN COST OF PREDICTIONS GO DOWN?
Analysis from Agrawal, Gans, Goldfarb> (bit.ly/economics-of-AI)> Idiot savants—understand the limitations of models> What happened with MC(arithmetic) 0> What happens when MC(prediction) 0> Think: substitutes, complements, new business
models
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BUILDING THE MI STACK
> Not the same as SW dev—add models to your IT inventory
> Instead of systems of record, records of predictions> Requires new answers—when do you trust the
model?> New change management—for organizations and as
models change> Look at examples in other industries
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INDUSTRIES
NEVER NEVER LAND
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PROMINENT ACQUISITIONS
Nervana IntelMagic Pony TwitterTuri AppleMetamind SalesforceOtto UberCruise GMSalesPredict eBayViv Samsung
Strong leadersBroad technology platformsScarce, valuable talentOften $100M+
But also a reminder of difficulty of building a independent startup
MACHINEINSPIRATION
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MACHINE INTELLIGENCE CAN SOLVE NEW PROBLEMS
MACHINE INTELLIGENCELANDSCAPE2016@SHIVON @JAMES CHAMNOVEMBER 8, 2016 // SAN FRANCISCO