1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Matt Hartman
Partner, betaworks
AWS Startup Day
Machine Learning & Your Startup

2. @MattHartman

3. twitter tumblr GroupMe
kickstarter venmo OMGPop
2008-2013: How do we communicate in new interfaces?
Social, Mobile

4. Gimlet
(audio)
2014+: how do we communicate in new interfaces?
Voice, Messaging, AR/VR, Communities
Anchor
(audio)
Giphy
(messaging)
Dots
(native media)
Product Hunt
(community)
RecRoom
(VR)

5. Where does machine learning play a part?
Visual/AR/VR Conversational/Messaging Verbal Computing/Audio
Computer Vision NLP & Speech Generation

6. Agenda
• How VCs think about machine learning
• How to use machine learning in your startup
• The future: Synthetic Media

7. How VCs think about machine learning
(or at least this VC)

8. Example: Deep Neural Networks
- Discovering as a new Computer Science algorithm: 2004
- Solidified as a technology available to others via OpenSource: ~2010
- Built as a SaaS API: 2013 to present

9. Data + Untrained Algorithms = Trained Algorithm
Proprietary
or not?
Open Source? Who owns? How much
better does it get as amt of
data improves?
value

10. How to use Machine Learning in your startup

11. Three Types of Machine Learning
SaaS APIs
• Clarify
• Google Speech API
• Algorithmia
Technologies
• Deep Learning
• Stochastic ML
Computer Science
Algorithms
• Hidden Markov Models
(e.g., Trumpbot)
• Conditional Random
Fields

12. What is your moat:
Brand / Sales
- ML not core, use whatever
is best
- If a competitor got your
trained algorithm, would still
be ok
Vertical / Data
- Trained algorithms are
valuable
- More data = better results
- Not a near-term asymptote

13. Example: Anchor
- Brand: Consumers go directly to the app
- Network: user generated content which
originates on Anchor
- ML is a feature: NLP makes video better for
sharing, helps grow the platform
- Verdict: don’t need custom trained ML, can
use Google Speech API

14. Example: Giphy
- Brand: Consumers go directly to the site
- BD: API relationships with all major
messaging platforms
- ML is a feature: for tagging makes it better,
but a competitor wouldn’t win by copying the
ML tagging
- Verdict: will need to custom train ML, which
will make the product better, but doesn’t need
to be core compitency

15. - Technology: Injects new content
seamlessly into pre-recorded
video content
- Customers: relationships with
brands & video players
- ML: Computer vision to determine
angle, blur, foreground objects
- Verdict: Proprietary is critical.
PhD and Comp Sci Masters team
solving a hard technical problem
with applications of customized
technologies (i.e., trained
algorithms)
Example: URU

16. Food for thought: Synthetic Media

17. Lyrebird Synthesized Voice

18. Washington University’s synthesized video

19. Thank you
@matthartman

20. @matthartman
hrt.mn

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