Trent McConaghy discusses the relationship between data, AI, and token economics within the Ocean Protocol, emphasizing how the availability of vast amounts of data enhances the accuracy of AI models. The document highlights the importance of creating economic incentives to curate and share relevant data, thereby breaking down existing data silos. Ultimately, McConaghy advocates for democratizing access to AI data and services to unlock new opportunities, including advances in fields like healthcare and autonomous driving.

1. Trent McConaghy
@trentmc0
Data, AI and Tokens:
Ocean Protocol

2. Audio radar

3. 1000x more data
The Unreasonable Effectiveness of Data

4. Deep learning
models with >> capacity
Error 5% .. 0.01%Models with
limited capacity
Error 25% .. 5%
Another 1000x more data
Deep Learning Loves Data

5. The Problem

6. Mo’ data
(and mo’ compute)
Mo’ accuracy
Mo’ $

7. Here’s
your
personal
data

9. Eroding the silos

10. Silo Pool Mo’ Data
(and mo’ compute)
Mo’ accuracy
Mo’ $

11. Have lotsa data
(1000 enterprises)
Have lotsa AI
(1000 AI startups)

12. Have lotsa AI
(1000 AI startups)
Have lotsa data
(1000 enterprises)

13. A new data economy
Have lotsa AI
(1000 AI startups)
Have lotsa data
(1000 enterprises)
DM DM DM DM
DM DM DM DM

14. A new data economy
Have lotsa AI
(1000 AI startups)
Have lotsa data
(1000 enterprises)
DM DM DM DM
DM DM DM DM
Ocean

15. Economic incentives

16. Blockchain Superpower:
Get people to do stuff
By rewarding with tokens

17. Bitcoin goal: maximize security of network
Token rewards if: run compute to secure network

18. Economic Incentive for Bitcoin
Objective: Maximize security of network
• Where “security” = compute power
• Therefore, super expensive to roll back changes to the transaction log
E(Ri) α Hi * T
E() = expected
value
# tokens (BTC)
dispensed each
block
block
rewards
hash power of actor
= contribution to
“security”

19. Ocean goal: maximize supply of relevant data
Token rewards if: supply data, and curate it

20. Economic Incentive for Ocean
Objective: Maximize supply of relevant data
• This means: reward curating data + making it available
• Where “curating” = betting on data. Reward taste-making.
E(Rij) α log10(Sij) * log10(Dj) * T *Ri
Expected
reward for user
i on dataset j
Dj = proofed popularity
= # times made dataset
available
Sij = predicted popularity
= user’s curation market
stake in dataset j
# tokens
during
interval

21. From AI data to AI services
Motivations:
• Privacy, so compute on-premise or decentralized
• Data is heavy, so compute on-premise
• Link in emerging decentralized AI compute
Objective function: Maximize supply of relevant services
=reward curating services + proving that it was delivered
E(Rij) α log10(Sij) * log10(Dj) * T *Ri
proofed popularity
of service
predicted popularity
of service

22. Ocean is a network of curated services. An AI services pipeline.
Availability Consumption Privacy GovernanceProduction
commons
Inter-
Operability
Discovery
*Note: logos shown are examples and do not imply partnerships or integrations

23. Prototypes

25. Sandbox with Singapore Data Authority

26. What unlocking AI data
& services unlocks

27. Self-driving cars: fewer accidents, more mobility

28. >100x more data for health
care research

29. Erode the data silos

30. Erode the data silos

31. Erode the data silos

32. Conclusion

33. AI data is siloed.
AI services are siloed.
Let’s change the rules of the game with incentives.
Let’s democratize access to AI data & services!
Trent McConaghy
@trentmc0