This past year has seen the implementation of artificial intelligence (AI) and blockchain, across a varied range of solutions and application within several industries. What is yet to be explored is a fusion of the two. This merger can allow enterprises to create a composable business that has a high service delivery. Blockchain is basically […]
This past year has seen the implementation of artificial intelligence (AI) and blockchain, across a varied range of solutions and application within several industries. What is yet to be explored is a fusion of the two. This merger can allow enterprises to create a composable business that has a high service delivery. Blockchain is basically an immutable ledger that is open and decentralized, with strong controls for privacy and data encryption. Smart contracts, trusted computing and proof of work are some of the features that contravene traditional centralized transactions, making blockchain truly transformative.
AI, on the other hand, is a general term for varying subsets of technologies, it revolves around the premise of building machines that can perform tasks requiring some level of human intelligence. Some of the technologies striving to make this a reality include deep learning, artificial neural networks and machine learning. By utilizing AI-based technologies, organizations have been able to accomplish everything from the automation of mundane tasks, to the study of black holes. These technologies are disruptive in their own rights, a framework that harnesses the best of both worlds could be radically transformational.
As the use of AI continues to become more mainstream in high profile and public facing services, like defense, medical treatment and self-driving cars, multiple concerns have been raised what is going on under the hood. The AI black-box suffers from an explainability problem. If people are willing to place their lives in the hands of AI powered devices and applications, then they naturally want to understand how the technology makes its decisions. Having a clear audit trail not only improves the trustworthiness of the data and the models, but also gives a clear route to trace back the machine learning process.
Additionally, machine learning algorithms rely on information fed into it to help shape the decision making process. A shift from fragmented databases maintained by individual entities to comprehensive databases maintained by consumers can increase the amount of data available to predictive marketing systems and other recommendation engines. Resulting in a measurable improvement of accuracy. Google’s Deep Mind is already deploying blockchains to offer improved protection for user’s health data used in their AI engines, to make their infrastructure mode transparent. Currently, intelligent personal assistants are appealing to consumers, and users will generally sacrifice privacy for the sake of convenience; overlooking what data is being collected from their device, how it is secured, or how it compromises their privacy. An amalgamation of AI and blockchain can reinvent the way information is exchanged. Machine learning can swift through and digest vast amounts of data shared via blockchain’s decentralized structure.
The synthesis of blockchain and AI opens the door for the decentralization of authentication, compute power and data. When data is centrally stored, a breach will always be an imminent threat. Blockchain decentralizes user data, thus reducing the number of fraudsters or hackers trying to gain access and take advantage of the systems. Machine learning algorithms are capable of monitoring the system for any behavioral anomalies, becoming more accurate as their intelligence improves. This completely dismantles the inherent vulnerability of centralized databases, forcing cyber attackers to challenge not one, but multiple points of access, which is exponentially more difficult. Blockchain and AI combine to offer a strong shield against cyber attacks. Aside from enhanced attack-defense mechanisms, decentralization translates to higher amounts of data being processed and more efficient AI networks begin built. Imagine a peer-to-peer connection that has been streamlined with natural language processing, image recognition, and multidimensional data transformations in real time.
An AI-powered blockchain is scalable based on the number of users. AI adds an aspect of computational intelligence that can optimize transaction data in blocks and make the entire process faster. A 2016 report from Deloitte estimated that the cost of validating transactions on a blockchain stood at a whooping $600m annually. A large potion of the cost was generated by specialized computing components which consume a lot of energy while performing mining operations. An AI based blockchain model has the ability to help enterprises set up a low energy consumption model, by allowing specific nodes to initially perform larger tasks and alert miners to halt less crucial transactions. Enterprises will be able to achieve the latency required for performing transactions faster without making any structural changes in their architecture. A machine learning and blockchain combo might also be the key to figuring out how to leverage the worlds idle computing power.
Both AI and blockchains are technologies that aim to improve the capabilities of the other, while also providing opportunities for better accountability and oversight. AI reinforces blockchains framework, and together they solve several of the challenges that come with securely sharing information over the IoT. Blockchain provides a decentralized ledger of all transactions, while AI offers intelligent analytics and real-time decision-making. This allows users to take back control and ownership of their personal data and open the door for more effective security measures. Datasets that are currently only available to tech giants will be put in the hands of the community, subsequently accelerating AI adoption. Sectors such as telecom, financial services, supply chain intelligence, and retail in general are primed for the adoption of both technologies, with health care following suit.