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
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
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