Blockchain and Artificial Intelligence - The Future of Technology

What is artificial intelligence (AI)?

Artificial intelligence is the ability of a program to learn. It also means the science and engineering of intelligent computer programs. These algorithms can understand patterns, solve problems without human instruction and use large data sets. These algorithms can analyze external data inputs, learn from these data, and use the acquired knowledge while performing tasks to achieve specific goals.

At the most basic level, there are mainly two types of AI – narrow AI and strong AI.

Narrow AI targets specific or limited tasks such as facial recognition, spam filtering or playing chess. On the other hand, Strong AI has the ability to perform many different tasks instead of a single task. He could potentially have a human-level cognition and perform any mental operation that a human can. Although Narrow AIs are used today, Strong AI has not yet emerged. In fact, many experts are skeptical about whether this is possible.

While it is impossible to predict the potential impacts of strong AI, many believe that the future of blockchain and AI will be interrelated. It can be argued that these two will be the most important technologies for the coming decades.

Therefore, it is important to closely examine how AI and blockchain may interact in the future.

Synergy of AI and blockchain

AI improvements for blockchain

Mining requires large amounts of computational power and energy. Distributed ledgers sacrifice efficiency for features such as immutability and censorship resistance. AI can offer a high level of efficiency in optimizing energy consumption, which can be beneficial for improving mining algorithms.

One of the main criticisms of blockchain systems is that these systems require very high energy. Desirable crypto economics and security features create computational tasks that would not otherwise be necessary. Reducing the consumption of Proof of Work blockchains can be beneficial for the entire industry and support the widespread use of blockchains.

AI can also optimize the storage needs of blockchains. The distributed ledger can quickly grow to large sizes, as transaction history is maintained across all nodes. If the storage needs are high, the barrier to entry will be high, which can potentially reduce the decentralization of the network. AI could introduce new database sharding techniques that could make the blockchain smaller and store data more efficiently on the chain.

Decentralized data economy

Data is increasingly becoming a valuable asset that must not only be securely stored, but also shared. Efficient AI systems rely heavily on data, and data can be stored on blockchains with a very high level of reliability.

At its core, a blockchain is a secure, distributed database shared by all participants in the network. Data is kept in blocks, and each block is cryptographically linked to the previous one. This makes it extremely difficult to change stored data without damaging network consensus (for example, with a 51% attack).

Decentralized data exchanges aim to create a new data economy running on the blockchain. These exchanges can make data and data stores accessible to anyone (or anything) easily and securely. By connecting to this data economy, AI algorithms can use a larger set of external inputs and learn faster. In addition, the algorithms themselves can be traded on marketplaces. Thus, the development of algorithms that become available to a wider audience can be accelerated.

Decentralized data exchanges have the potential to revolutionize data storage. At its most basic, anyone can have the opportunity to rent their local storage for a fee (paid in tokens). As a result, existing data storage service providers are forced to improve their services to keep up with the competition.

Some of these data marketplaces are already in use, but they are not mature enough yet. AI systems also benefit if data and storage service providers are encouraged to maintain high data integrity.

Decentralized supercomputers

Training AI requires a high level of computational power as well as quality data that enables algorithms to learn. AI algorithms often use a type of computational system known as an artificial neural network (ANN). ANNs learn to perform tasks by evaluating many examples. These ANNs require a significant amount of computational power when examining millions of parameters to perform the specified task.

If data can be shared on a blockchain, why not share computing power? In some blockchain applications, users can lend the computing power of their machines to others who want to perform complex calculations through a peer-to-peer (P2P) marketplace. Users are incentivized with tokens in exchange for sharing their computing power.

AI systems can be trained on these computing platforms much more efficiently and at lower cost. While the primary focus of early uses was to create 3D computer graphics, this focus may slowly shift towards AI.

As these Decentralized Applications (DApps) evolve, companies that provide computing power may face fierce competition. Much of this computing power is used much more efficiently as users are allowed to profit by renting out their idle computing power. In theory, every CPU or GPU in the world can be run as a node in a decentralized supercomputer, if not used for anything else.

AI decisions to be more auditable

Decisions made by AI systems can be difficult for humans to understand. These algorithms work with such large amounts of data that it would be practically impossible for any human to repeat and control the giving processes as much as AI.

If decisions are recorded on a per-data point basis, there is a clear audit trail that humans can control, thereby increasing confidence in the decisions made by AI algorithms.