What Happens When We Merge AI and Blockchain?

Artificial Intelligence and Blockchain are two common buzzwords that we get to hear these days. While one has already reached a critical point of implementation, the other is an emerging one. While AI offers automation and machine with cognitive intelligence of humans but data capabilities beyond their power, Blockchain is more like a new filing system for digital information, which stores data in an encrypted, distributed ledger format. Through the maintenance of a decentralized database architecture by Blockchain, the record and authentication of certain operations are subject to the agreement of several parties rather than a single authority. This enables the creation of tamper-proof, highly robust databases that can be read and updated only by those with permission. Either technology has garnered much interest within the technology sphere and is catalyzing the pace of innovation while introducing radical shifts in every industry.

These two technologies can be thought of as to ends of a spectrum where AI promotes centralized intelligence on close data platforms and incentivizes consolidation of data and computing power.  In contrast, Blockchain encourages decentralized applications in an open-data environment.  AI functions as a black box, but Blockchain is transparent. However, now scientists are seeing the possibility of converging both these technologies that can lead to new avenues of potentials through a single yet powerful prototype. Though both share a characteristic degree of complexity, yet they can benefit from each other. For instance, AI’s machine learning can strengthen Blockchain’s underlying architecture, and Blockchain can make AI more coherent by understanding the reasons made by machine learning models. Also, it can document all data and variables used to arrive at a decision made under machine learning.

Blockchain with AI is already being offered as a service by major cloud providers like IBM and Microsoft. IBM is also merging Blockchain with AI via the Watson IoT group on its supercomputer IBM Watson. The following is a continuation of some applications that result from the interaction of both artificial intelligence and Blockchain.

Lower Market Entry Barriers: The AI designed apps and software endure several market barriers from lack of authentication, the involvement of intermediaries, risk of inaccuracy, monopoly ownership on data, and others. With Blockchain, these snags can be eliminated as the data is made secure, accurate, and authenticated by all the stakeholders involved.

Energy Consumption: Data mining is a highly energy-consuming process. But machine learning can help to optimize this process with lower investments in mining hardware. This is similar to Google’s successful attempt to reduce energy consumption used for cooling their data centers by 40% by training the DeepMind AI on historical data from thousands of sensors within a data center.

Higher Trustability: While Blockchain emphasizes the importance of data sharing between various clients on a particular platform, AI relies on big data for analysis, prediction, and assessment of machines and to generate the algorithms. Ocean Protocol uses this to minimize the trust required between the data owner and researcher by using data silos. It combines Blockchain as the access layer controlling access to data with AI helping researchers run algorithms on the data without revealing any information. This enables private data owners to gain the benefits of offering their data to researchers in a secure way and researches to train their models in a decentralized fashion. This also allows users to monetize data personally.

Secured Personalized Experiences: OTT platforms like Netflix and Amazon Prime are using AI with Blockchain to provide secure experience to end-users with more privacy. This is achieved when Blockchain ensures data immutability and security by employing the best cryptography encryption techniques. This reduces the instances where AI fails to serve customers with the right personalized experience due to data violations done by data intruders or others. Even Amazon and Watson use these technologies to offer customized shopping experiences.