Unlike blockchain technology and IoT, AI — which, in one sense, is about creating computer applications that act as smart as humans — is not a new concept. Research began in academia in the 1950s, and the subject was popularized in the 1968 science fiction movie “2001: A Space Odyssey,” featuring the humanlike HAL 9000 computer. Usable computing systems running AI programs emerged in the 1980s, in the form of expert systems that were able to apply pre-programmed knowledge and make rules-based business decisions. In 1997, an IBM AI system called “Deep Blue” beat reigning world champion Gary Kasparov at chess.

Today, AI — and a related focus on machine learning (allowing computers to learn based on supplied data) — is rapidly evolving due to the development of high-performance microprocessors that are able to work quickly with very large amounts of in-memory data.

Data is a key ingredient of approaches to developing AI and machine learning, which are now being applied to a wide variety of uses, from stock trading to chatbots to self-driving cars. There is barely a business or human activity that is not considered as a target for AI in future years and decades.

This is where blockchain technology comes in, which has its own role to play in the world of data. While work on aligning blockchain and AI technology is still emerging, a few development threads now exist and 2018 looks to be a year when progress toward convergence will accelerate.

Enter Blockchains

Blockchain technology’s ability to guarantee the accuracy of data makes it useful for a number of AI applications, both for feeding data into AI systems and for recording results from them.

For example, CognitiveScale, an AI startup that’s backed by IBM, Intel, Microsoft and USAA, among others, is leveraging blockchain technology to securely store the results of an AI application that it built for regulatory compliance in the financial markets world. That’s an industry with a lot of regulatory scrutiny, so being able to store AI-derived decisions securely helps market participants stay on top of onerous reporting requirements.

IBM is marrying both its blockchain offering (based on the open-source Hyperledger Fabric codebase) and its Watson AI platform for a range of industries. One early project involves Everledger, which is applying blockchain technology to track the provenance of luxury items, including diamonds.

Leveraging Everledger’s data store of individual diamond characteristics (more than a million of them, secured by IBM’s blockchain), Watson is applying knowledge of thousands of regulations to ensure that diamonds comply with United Nations edicts preventing the sale of conflict minerals.

Some see certain implementations of blockchain technology as benefiting the development of AI applications, which improve as more data is made available to them for training the machine-learning models upon which they are built.

The Importance of Data

In a lengthy blog post, Trent McConaghy, founder and CTO of BigchainDB, set out his reasoning for why blockchain-enabled, decentralized networks encourage the creation of available data — essentially because individual data silos are replaced by a shared and accurate ledger — which can be leveraged to train better AI models. For example, a consortium of banks sharing credit card usage data via a shared ledger might be expected to lead to improved, faster and more accurate fraud detection.

BigchainDB is currently rolling out a database that combines the capacity and performance of NoSQL database technology with the immutability and consensus of blockchain approaches. Already, the company is working on enterprise implementations in partnership with the likes of Capgemini, Daimler, Porsche and Toyota.

Moreover, the company is also deploying its technology to underpin the Interplanetary Database (IPDB), a publicly owned and governed database network that it sees as becoming the database for a “decentralized world computer,” much like the platform the Ethereum community is creating.

Most recently, BigchainDB began to evangelize the open Ocean Protocol, designed to facilitate data exchange as an enabler for AI applications. Core software that supports the protocol leverages the IPDB. A test network for the protocol should be launched by the middle of 2018.

According to data compiled by Pitchbook for Reuters, venture capital investment in crypto and blockchain startups has reached $850 million so far this year.

The Enterprise Ethereum Alliance has announced a "Token Taxonomy Initiative" to develop universal definitions for tokens to encourage their interchangeability across blockchain platforms. Members of the initiative include Microsoft, R3, ConsenSys, IBM, EY, Accenture and Intel.

Gemini Trust, a New York-based cryptocurrency exchange, has announced support for Segregated Witness (SegWit) addresses and transaction batching. As a result, customers can now use SegWit addresses for bitcoin deposits and withdrawals, ideally improving processing times and lowering bitcoin withdrawal fees.

Food producer Nestlé and retailer Carrefour will equip the packaging of a French instant mashed potato product with a QR code that provides blockchain-based data about its origins to consumers. The pilot was developed in conjunction with IBM Food Trust.