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The Convergence of AI and Blockchain: What’s the deal?


This article wants to give a flavor of the potentialities realized at the intersection of AI and Blockchain and discuss standard definitions, challenges, and benefits of this alliance, as well as about some interesting player in this space.



III. How Blockchain can change AI

 


https://datafloq.com/read/how-cognitive-computing-can-revolutionize-business/3317

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 as 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…

 

IV. Decentralized Intelligent Companies

 


Image Credit: Wit Olszewski/Shutterstock

There are plenty of landscapes of blockchain and cryptocurrencies startups out there. I am anyway only interested in those companies working at the intersection (or the convergence, as someone calls it) of AI and blockchain, which apparently are not that many. They are mainly concentrated in San Francisco area and London, but there are examples in New York, Australia, China, as well as some European countries.

They are indeed so few of them that is quite hard to classify them into clusters. I usually like to try to understand the underlying patterns and the type of impact/application certain groups of companies are having in the industry, but in this case is extremely difficult given the low number of data points so I will simply categorize them as follows:

  • Decentralized IntelligenceTraneAI (training AI in a decentralized way); Neureal (peer-to-peer AI supercomputing); SingularityNET (AI marketplace); Neuromation (synthetic datasets generation and algorithm training platform); AI Blockchain (multi-application intelligence); BurstIQ(healthcare data marketplace); AtMatrix (decentralized bots); OpenMinedproject (data marketplace to train machine learning locally); Synapse.ai(data and AI marketplace); Dopamine.ai (B2B AI monetization platform);
  • Conversational PlatformGreen Running (home energy virtual assistant); Talla (chatbot); doc.ai (quantified biology and healthcare insights);
  • Prediction PlatformAugur (collective intelligence); Sharpe Capital(crowd-source sentiment predictions);
  • Intellectual PropertyLoci.io (IP discovery and mining);
  • Data provenance: KapeIQ (fraud detection on healthcare entities); Data Quarka (facts checking); Priops (data compliance); Signzy (KYC)
  • TradingEuklid (bitcoin investments); EthVentures (investments on digital tokens). For other (theoretical) applications in finance, see Lipton (2017);
  • InsuranceMutual.life (P2P insurance), Inari (general);
  • MiscellaneousSocial Coin (citizens’ reward systems); HealthyTail (pet analytics); Crowdz (e-commerce); DeepSee (media platform); ChainMind(cybersecurity).

A few general comments:

  • It looks interesting that many AI-blockchain companies have larger advisory board than teams. It might be an early sign that the convergenceis not fully realized yet and there are more things we don’t understand that those ones we know;
  • I am personally very excited to see the development of the first category (decentralized intelligence) but I also see a huge development in conversational and prediction platforms as well as intellectual property. I grouped other examples under “miscellaneous” because I don’t think at this stage they represent a specific category but rather only single attempt to match AI and blockchain;
  • Those companies are incredibly hard to evaluate. The websites are often cryptic enough to not really understand what they do and how (a bit paradoxical if you want to buy the blockchain transparency paradigm) and technology requires a high tech-education to be fully assessed. Cutting through the hype is a demanding task and this makes it very easy to be fooled. But let me give you a concrete example: ever heard of Magos AI? In the effort of researching companies for this post, I found myself reading several articles on this forecasting blockchain AI-driven platform company (WiredPrnewswire, etc.), which just did an ICO for over half a million dollars and that made great promises on its deliverables. The website didn’t work — weird, if you consider that they need to share material/information on the ICOs. But you know, it might happen. I made then an extra effort because I read it on Wired and I was curious to know more about it. I was able to find its co-founders, which I couldn’t find eventually on Linkedin. Weird again. Well, there are people who do not like socials, fair enough, especially if you consider that until three months ago there was no proof of the company existence whatsoever. Let me look into the rest of the team. Nothing even there, and no traceable indications of their previous experiences (except for the CTO master in analytics, that I found no proof of). I tried to then dig into the technology: white papers, blue papers, yellow papers, you name it. I only found reviews of them, no original copies. Final two steps: I don’t consider myself an expert in blockchain at all, but I read, a lot. And I also believe I am fairly knowledgeable when it comes to AI and what is happening in the industry. These guys claimed they created 5 different neural nets that could achieve the same accuracy in complex different domains than Libratus (or DeepStack) reached in Poker, but I never heard of them — very weird. Well, you know what? Maybe I could write them and meet them to understand. Their address points to the AXA office in Zurich. Ah.

After 5 minutes of research, I finally Google the two key words: “Magos scam”. It seems these guys took the money and disappeared. They are surely building the 6 neural net somewhere, so stay tuned.

My point here is that exponential technologies are fantastic and can advance mankind, but as much as the benefits increase also the potential “negative convergence” increases exponentially. Stay alert.

 

V. Conclusion

 


Image Credit: Sasun Bughdaryan/Shutterstock

Blockchain and AI are the two extreme sides of the technology spectrum: one fostering centralized intelligence on close data platforms, the other promoting decentralized applications in an open-data environment. However, if we find an intelligent way to make them working together, the total positive externalities could be amplified in a blink.

There are of course technical and ethical implications arising from the interaction between these two powerful technologies, as for example how do we edit (or even forget) data on a blockchain? Is an editable blockchain the solution? And is not an AI-blockchain pushing us to become data hoarder?

Honestly, I think the only thing we can do is keep experimenting.

References

  • Castro, M., Liskov, B. (2002). “Practical Byzantine Fault Tolerance and Proactive Recovery”. ACM Transactions on Computer Systems, 20(4): 398–461.
  • Catalini, C., Gans, J. S. (2017). “Some Simple Economics of the Blockchain”. MIT Sloan School Working Paper: 5191–16.
  • Deloitte (2016). “Blockchain Enigma. Paradox. Opportunity”. White Paper.
  • Iansiti, M., Lakhani, K. R. (2017). “The Truth About Blockchain”. Harvard Business Review, January–February 2017: 118–127.
  • Lipton, A. (2017). “Blockchains and Distributed Ledgers in Retrospective and Perspective”. arXiv:1703.01505.
  • Mazieres, D. (2016). “The stellar consensus protocol: A federated model for internet-level consensus”. White Paper.
  • Mingxiao, D., Xiaofeng, M., Zhe, Z., Xiangwei, W., Qijun, C. (2017). “A Review on Consensus Algorithm of Blockchain”. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Banff Center, Banff, Canada, October 5–8, 2017
  • Nakamoto, S. (2008). “Bitcoin: A Peer-to-Peer Electronic Cash System”. White Paper.
  • O’Dwyer, K. J., Malone, D. (2014). “Bitcoin mining and its energy footprint”. 25th IET Irish Signals & Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communications Technologies (ISSC 2014/CIICT 2014), Limerick, pp. 280–285.
  • Outlier Ventures (2017). “Blockchain-Enabled Convergence”. White Paper.
  • Sasson, E. B., Chiesa, A., Garman, C., Green, M., Miers, I., Tromer, E., Virza, M. (2014). “Zerocash: Decentralized anonymous payments from bitcoin”. In Security and Privacy (SP), 2014 IEEE Symposium on, pp. 459–474.
  • Unicredit (2016). “Blockchain Technology and Applications from a Financial Perspective”. Technical Report.
  • Vasin, P. (2014). “BlackCoin’s Proof-of-Stake Protocol v2”White Paper.
  • Zyskind, G., Nathan, O., Pentland, A. (2015). “Enigma: Decentralized computation platform with guaranteed privacy”. arXiv:1506.03471.

 
Bio: Francesco Corea is a Decision Scientist and Data Strategist based in London, UK.

Original. Reposted with permission.

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