machine intelligence landscape 2016

MACHINE INTELLIGENCELANDSCAPE2016@SHIVON @JAMES CHAMNOVEMBER 8, 2016 // SAN FRANCISCO

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WHAT’S CHANGED

20145 Groups35 Categories233 Companies

Focused on academics and big tech companies


Focused on startups and new autonomous systems


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

machine intelligence landscape 2016

Software