Isn't AI going to be dominated by the big companies like Google and Amazon and Microsoft and Baidu? What can startups do to thrive in this ecosystem? What are investors looking for when they meet AI-powered startups? Should startups with AI inside think about their go-to-market process any differently from other startups? Frank Chen from Andreessen Horowitz will tackle these and other AI startup questions in this session.

1. FRANK CHEN
Artificial Intelligence
@withfries2
frank@a16z.com
Surveying The Startup Landscape

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Databases Got
Inside All Software

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So Will AI

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20162009
Mobile First
2007
Cloud Native AI Inside

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20031995
Internet Architecture
1993
Client-Server Virtual Appliance

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What Can Startups Do?

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See in the Real World

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Seeing Things in
the World
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUES:
Image recognition,
image segmentation, and
visual similarity search
What’s similar to this
thing in the picture?

11. ©2017 Andreessen Horowitz. Proprietary and confidential.
Automated Analysis
of Drone Imagery
Image segmentation, flight
planning, grade analysis
Safety compliance, maximize
fuel efficiency, calculate
stockpile volumes, recognize
storm damage
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUES:

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Understand Language

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Help Lawyers
Win Trials
Logistic Regressions, SVM,
Long Short-Term Memory
Convolutional Neural Nets
Train new prediction models,
identify documents relevant for a
trial by topic, translate
documents into other languages,
audio transcription
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUES:

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Hear the Voice of
the Customer
Topic modeling with BigARTM,
NLP, opinion mining and sentiment
analysis, deep learning (LSTM,
Unsupervised Sentiment Neuron)
with Keras and Tensorflow
Automatically detect themes and
sentiment in customer reviews
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUE:

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Make Predictions

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Help Airbnb Hosters
Price Their Pads
https://www.blog.google/products/gmail/save-time-with-smart-reply-in-gmail/
Image analysis, Aerosolve
open source project
Predict probability of getting a
booking based on price, date,
photos, location, unique listing
features
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUES:

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Surface The Best
Tips And Photos
https://www.blog.google/products/gmail/save-time-with-smart-reply-in-gmail/
Support vector machines,
sentiment analysis, spam
detection, k-nearest neighbor
Rank tips, group photos,
figuring out whether
you’re near a venue
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUES:

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Figure Out Best
Distribution Outlet
for a Story
Logistic Regressions, Word
Embeddings, Recurrent Neural
Networks on Elastic Map
Reduce, RedShift, BigQuery
Figure out initial offsite distribution
channel for item, and how to
optimally route it across channels.
Content adaptation and translation.
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUES:

19. ©2017 Andreessen Horowitz. Proprietary and confidential.
Collect Groceries as
Fast as Possible
Gradient boosted decision
trees, deep learning with
TensorFlow and Keras
8% improvement to pick
up times
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUE:

20. ©2017 Andreessen Horowitz. Proprietary and confidential.
Search For Anti-
Aging Drugs
http://blogtacular.com/wp-content/uploads/2016/03/Pinterest-Visual-Search-Zoom-4.png
Used machine learning techniques
to filter through 14k metabolites and
found a set of 171 metabolites that
are predictive of mortality
This subset of metabolites can be
used to develop anti-aging drugs
and screen for efficacy
COMPANY | ORGANIZATION:
DESCRIPTION:

21. ©2017 Andreessen Horowitz. Proprietary and confidential.
Predict Health
Problems
Detect atrial fibrillation using data
from your smartwatch
COMPANY | ORGANIZATION:
DESCRIPTION:
Deep neural networks,
reinforcement learning
TECHNIQUES:

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Self-Drive, Self-Fly, Self-Map…

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TECHNIQUES:
Map Inside of a
Building
https://www.cnet.com/news/youtube-auto-caption-1-billion-videos-subtitles-hearing-impaired/
Vision/face recognition,
Simultaneous Localization
and Mapping (SLAM), path
planning
COMPANY | ORGANIZATION:
DESCRIPTION:
Flies inside buildings,
constructs 3D maps, identifies
people

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Drive Taxis
https://www.blog.google/products/gmail/save-time-with-smart-reply-in-gmail/
Vision recognition,
SLAM, motion planning,
vehicle control
Self driving taxi service
live in San Jose Villages
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUES:

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Self-Fly And
Drop Off Blood
https://www.blog.google/products/gmail/save-time-with-smart-reply-in-gmail/
Motion planning, navigation
110 kph
75 km delivery radius
30 minute fulfillment time
500 deliveries/day
1.5 kg payload
COMPANY | ORGANIZATION:
DESCRIPTION:
TECHNIQUES:

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Provide Infrastructure
(Picks & Shovels)

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Provide Data
Analytics Platform
COMPANY | ORGANIZATION:
TECHNIQUE:
Apache Spark MLlib
(classifications, data prep,
regressions, random forest,
decision trees, XGBoost…),
Scit-kit Learn, Visuatlizations
DESCRIPTION TEXT
Built on Spark, Databricks enables
ML teams to run experiments at
scale

28. ©2017 Andreessen Horowitz. Proprietary and confidential.
Auto-Tune Your
Existing Models
COMPANY | ORGANIZATION:
TECHNIQUE:
“AI for AI” using ensemble of
Bayesian and global
optimization strategies to
perform black-box optimization
(just looks at configs and
outputs, not data or model)
DESCRIPTION TEXT
Optimization as a Service that bolts
on top of an existing ML or AI
pipeline to automatically and
optimally tune it.

29. ©2017 Andreessen Horowitz. Proprietary and confidential.
HD Mapping for
Safe Autonomy
COMPANY | ORGANIZATION:
TECHNIQUE:
Sensing via sensor fusion,
mapping & localization, ML to
add semantics and understand
traffic patterns, scale out
serving infrastructure
DESCRIPTION TEXT
HD Mapping as Service that creates
and locates vehicles on a 3D map
with centimeter accuracy at 80 mph
Roundabout
Complicated
Intersection

30. What Do We Look For?
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More specifically:
What do we look for in an AI startup
that’s different from the other
startups we invest in?

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Answer:
Nothing

33. Big Market
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Technical Founders With the
Right Kind of Ambition

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Product-Market Fit
PRODUCT
MARKET
FIT

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Founder-Product Fit

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Durable Defensive Moat

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Attractive Unit Economics
(or Other Operating Metrics)
LTV > CAC = 😀
CAC > LTV = 😩

39. Fast Growth

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If You Are an AI Startup
Please do not let me stump
you on the basics
What machine learning
techniques are you using?
Why those techniques?
How well do they work?
What business benefits can you
deliver with those techniques?

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