The convergence of AI and Blockchain: what’s the deal? - Part III

Ai and blockchain


By Francesco Corea, Ph.D.  |  May 24, 2018
Francesco Corea is a Complexity Scientist, Tech Investor, Data Strategist and AI Advisor


III. How Blockchain can change AI

In the previous section, we quickly touched upon the effects that AI might eventually have on the blockchain. Now instead, we will make the opposite exercise understanding what impact can the blockchain have on the development of machine learning systems. More in details, blockchain could:

  • Help AI explaining itself (and making us believe it): the AI black-box suffers from an explainability problem. Having a clear audit trail can not only improve the trustworthiness of the data as well as of the models but also provide a clear route to trace back the machine decision process;
     
  • Increase AI effectiveness: a secure data sharing means more data (and more training data), and then better models, better actions, better results…and better new data. Network effect is all that matter at the end of the day;
     
  • Lower the market barriers to entry: let’s go step by step. Blockchain technologies can secure your data. So why won’t you store all your data privately and maybe sell it? Well, you probably will. So first of all, blockchain will foster the creation of cleaner and more organized personal data. Second, it will allow the emergence of new marketplaces: a data marketplace (low-hanging fruit); a models marketplace (much more interesting); and finally even an AI marketplace (see what Ben Goertzel is trying to do with SingularityNET). Hence, easy data-sharing and new marketplaces, jointly with blockchain data verification, will provide a more fluid integration that lowers the barrier to entry for smaller players and shrinks the competitive advantage of tech giants. In the effort of lowering the barriers to entry, we are then actually solving two problems, i.e., providing a wider data access and a more efficient data monetization mechanism;
     
  • Increase artificial trust: as soon as part of our tasks will be managed by autonomous virtual agents, having a clear audit trail will help bots to trust each other (and us to trust them). It will also eventually increase every machine-to-machine interaction (Outlier Ventures, 2017) and transaction providing a secure way to share data and coordinate decisions, as well as a robust mechanism to reach a quorum (extremely relevant for swarm robotics and multiple agents scenarios). Rob May expressed a similar concept in one of his last newsletters (that I highly recommend — you should definitely subscribe);
     
  • Reduce catastrophic risks scenario: an AI coded in a DAO with specific smart contracts will be able to only perform those actions, and nothing more (it will have a limited action space then).

In spite of all the benefits that AI will receive from an interaction with blockchain technologies, I do have one big question with no answer whatsoever.

AI was born in an open-source environment where data was the real moat. With this data democratization (and open-source software) how can we be sure that AI will prosper and will keep being developed? What would be the new moat? My only guess at the moment? Talent…


Series: The convergence of AI and Blockchain: what’s the deal?

Part I: http://cognitiveworld.com/article/convergence-of-ai-and-blockchain-whats-the-deal-part-i

Part 2: http://cognitiveworld.com/article/convergence-of-ai-and-blockchain-whats-the-deal-part-ii

Part 3: http://cognitiveworld.com/article/convergence-of-ai-and-blockchain-whats-the-deal-part-iii

Part 4: http://cognitiveworld.com/article/convergence-of-ai-and-blockchain-whats-the-deal-part-iv

 

This series was originally published in December 2017 on Medium and will appear in a forthcoming book edited by Spriger (2019).