Startupfest 2016: JANA EGGERS (Nara Logics) - Future of
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What I think about when I think about the future of AI @jeggers
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Transcript of Startupfest 2016: JANA EGGERS (Nara Logics) - Future of
What Ithink about when I think about the future of AI
@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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