startupfest 2016: jana eggers (nara logics) - future of

What Ithink about when I think about the future of AI

@jeggers

CC: Stephen Bowler

@jeggers

CC: Geoff Stearns

CC: NASA

CC: anatakti

CC: Geoff Stearns

Artificial light

@jeggers

CC: James Watkins

DID NOT REPLACE THE SUN

@jeggers

My concern:We are the drunkunder the streetlight

@jeggers

CC: Terry Freedman

Reason 1. Data and compute power

@jeggers

CC: /r/AntiAir1018

Reason 2. State of the art

Eric Schmidt:Today’s AI only thrives in narrow, repetitive tasks where it is trained on many examples.

@jeggers

We

need

defi

nitio

ns,

stan

dard

s & b

ench

mar

ks

@jeggers

http://www.appcessories.co.uk/artificial-intelligence/

We

need

defi

nitio

ns,

stan

dard

s & b

ench

mar

ks

http://www.appcessories.co.uk/artificial-intelligence/

@jeggers

Our focus

BOOKS:

Bill O’Reilly book+ a corkscrew?

PRODUCTS:

Computer Mouse and Necklace?

MOVIES:

Outbreak + Mrs. Doubtfire?

PRODUCTS:

Baseball bat and balaclava?

#PersonalizationFail

Synaptic Intelligence in Action: Credit card ecosystem

RECOMMENDED ACTION

NotifyREASONS

• Location• Prior merchant match• Prior purchase match• Vacation purchase match• Exclusive partner

98%

Results delivered

VSAdvanced Segmentation

Increased purchases and revenues for ecommerce versus multiple advanced segmentation algorithms

VSCollaborative Filtering

Achieved greater than 37% in benchmarks against top collaborative filtering algorithms

VSCurated Recommendations

Participants preferred personalized recommendations vs expertly curated recs

40%

>37%

95%

EcommerceOmni-channel personalized product and offer recommendations

IT and Call CentersPrioritization and case assignment; JIT information retrieval

Financial ServicesMatching demand with portfolio holdings; ecosystem development

Supply ChainIntelligent pre-detection for disruption events

Risk and FraudPattern recognition and event flagging

EngagementPersonalization recommendations for restaurants, hotels, movies and apps

HealthcareMatching patients with doctors, and doctors with experts

Enterprise recommendation problems we’ve solved

Government IntelligenceEstablish connections between entities for predicting events

What else do I fear?

Lack of DIVERSITY

Artificial Intelligence’s White Guy ProblemBy KATE CRAWFORD JUNE 25, 2016

“But this hand wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home, and in our legal and judicial systems. Sexism, racism, and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many ‘intelligent’ systems that shape how we are categorized and advertised to.”

Artificial Intelligence Has a ‘Sea of Dudes’ ProblemBy JACK CLARK JUNE 23, 2016

“To learn to identify flowers, you need to feed a computer tens of thousands of photos of flowers so that when it sees a photograph of a daffodil in poor light, it can draw on its experience and work out what it's seeing.

If these data sets aren't sufficiently broad, then companies can create AIs with biases. ”

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