Blockchain and AI are currently the biggest buzzwords in the tech industry. Unfortunately, for the same reason, they are often misunderstood -- or intentionally misused to create hype around a company or product.
But what happens when the two technologies are combined? Is the amalgamation just a bid to get extra attention or are there real use cases for the merger? And are there tasks that only a combination of the two technologies can accomplish?
Let's examine the limitations of blockchain and AI and how a combination of the two could add up to more than the sum of its parts.
Common limitations of blockchain and AI
Blockchain offers a decentralized platform where no single entity is in charge. The platform is controlled by independent nodes that collaborate to record what has happened on the system and govern what actions are allowed.
Best known as the technology that powered bitcoin, blockchain offers an immutable record of every transaction, ensuring that all nodes have the same version of the truth and no records are tampered with. That makes it a relatively fail-safe and hack-proof method for storing and transferring monetary value.
But to ensure this safety, the nodes have to go through huge calculations to ensure the validity of the transactions. Blockchain's mechanism for ensuring safety is also its weakness, as it limits scalability. The same is true for blockchain's immutability; every record needs to store the entire history of all transactions.
The problems associated with AI are different. AI needs data to operate, but getting good data can be problematic. For instance, hackers can alter the data a machine is trained on with a data poisoning attack. Collecting data from clients is also problematic, especially in light of data privacy laws such as Europe's GDPR. Finally, most of the data needed for effective AI is owned by large organizations, such as Google and Facebook.
"This limits innovation in AI and its broader use for social good, as these organizations only focus on commercial uses of AI," said Sushil Prabhu, CEO of OpenCrowd. "The barrier for smaller firms in AI is very high, as data is not available."
But Prabhu believes there is a solution: "Blockchain can solve the data availability issue in AI by democratizing data access and enabling incentive models for owners to share data."
Benefits of AI and blockchain combined
Data stored on the blockchain has an owner -- the users who created that data. Blockchain allows users to choose how their data is shared, who it is shared with and, eventually, how it is used.
The technology allows users to earn from their data rather than rely on intermediaries, such as banks, payment providers and credit cards. This is good for users, but it has another advantage in that transactions are also more in line with the GDPR guidelines that require consent from people whose data is recorded.
How do these attributes address the data limitations that tend to keep AI breakthroughs in the hands of all but the biggest tech companies?
Because there are no sovereign authorities in blockchain, many different platforms can interconnect without risking biased or unfair treatment or lockout. This means data will no longer be a central pool owned by one entity. Rather, it can be a much bigger medium, capable of connecting different systems that any party could use for their AI training purposes.
Blockchain's interconnecting networks could be used to solve complex problems that require massive amounts of data, according to Steve Deng, Ph.D., the chief AI scientist at Matrix AI Network, a global, open source, blockchain-based platform that combines AI and blockchain. Computing power has become a global resource.
"Blockchains offer a brand new opportunity to mobilize such a global resource to build the biggest computer ever," Deng said. "With such a computer at hand, for example, we can conquer many essential problems such as whole-brain simulation to understand human intelligence and gene regulatory network analysis to understand the interaction of gene and environment for any given person."
Blockchain's immutability is also useful in AI deployments. According to Basit Hussain, Ph.D., a technology advisor at Playpal: "The decision path taken by the AI is not always understandable, but by recording the decisions on the blockchain, the decision logic can be backtracked and improved."
Artificial intelligence also requires high computational power and, sometimes, parallel processing.
"The distributed approach allows smart allocation of resources from a far-flung network," Hussain said.
Much as blockchain can help AI, AI can also help blockchain. As Deng explains, AI can be used to select blockchain supernodes.
"The AI algorithm guarantees the fairness of the selection process [by] leveraging deep learning techniques to identify transaction intent using neuro-linguistic programming descriptions," he said, referring to the work being done at his company.
What to watch out for with AI and blockchain combinations
As was mentioned earlier, blockchain also has its limitations.
"[Blockchain] should not be considered as a data store," Prabhu said. "Solutions should consider the performance limitations of current blockchain technology and open visibility of the public ledger for sharing confidential/identifying data."
While the data that is stored on the blockchain can be encrypted, it is still not a good idea to store it publicly. Lucas Lu, Ph.D., co-founder of CyberMiles, advised that "confidential data should be stored off-chain in databases, while their cryptographic hash and public keys are available on the blockchain for the public to check."
Furthermore, the scalability and power consumption of blockchains can be a problem. Many projects working on that issue -- trying different algorithms such as directed acyclic graphs or hybrid approaches.
Eventually, using blockchain for AI comes down to what AI you need and how you can acquire data. Personal data is becoming more valuable every passing day -- that's why we are seeing so many breaches. Blockchain offers a secure way to guarantee ownership of data -- and where data goes, AI will follow.
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