What is artificial inteligence (AI) ?
Artificial intelligence is the ability of a program to learn. It is also the science behind the engineering of intelligent computer programs. AI algorithms can understand patterns and solve problems with large data sets, all without human intervention. They analyze external input data, learn from them and use this knowledge to achieve specific goals by performing tasks.
There are two main types of AI:
Narrow AI focuses on specific or limited tasks, such as face recognition, spam filtering, or playing chess. Then there is strong AI that could manage to handle a wide range of tasks rather than one specific one. It could potentially have knowledge equal to that of humankind and would be able to complete any intellectual task that a human being could. Narrow AI exists today, while strong AI has yet to be created. Be that as it may, many experts wonder if it is even possible.
A synergy of AI and blockchain technologies
Potential Blockchain improvement thanks to AI
Mining requires a lot of computing power and energy. Distributed ledges sacrifice efficiency at the expense of features such as consistency and resistance to censorship. AI can be very effective in optimizing energy consumption, which could be useful in improving mining algorithms.
One of the main counter-arguments against the use of blockchain systems is extremely high energy consumption. The required crypto economic and security features introduce redundant computational tasks. Reduction of Proof of Work blockchains consumption would benefit the industry and support blockchains' normal uptake.
AI could also optimize blockchain storage needs. Because the transaction history is stored on all nodes, the size of a distributed ledger can quickly grow to large numbers. When storage requirements are high, the entry barrier is also higher, potentially reducing network decentralization. AI could introduce new database partitioning techniques that would reduce blockchain size and streamline data storage.
Decentralized data economy
Data is becoming an increasingly valuable commodity. Therefore, it must not only be stored safely but have the ability to be exchanged. Efficient AI systems are heavily dependent on data that blockchains can store with an extremely high degree of reliability.
Blockchain is basically a secure distributed database shared by all participants in the network. Its data is stored in blocks and each block is cryptographically linked to the previous one. This makes it incredibly difficult to modify stored information without trying to “kidnap” the consensus of the network. For example, through 51% of the attack.
Decentralized data exchange aims to create a new data economy, operating at the top of blockchains. These exchanges allow easy and secure access to data and storage for anyone (or anything). When connecting to this data economy, AI algorithms could use a larger set of external inputs and learn faster. In addition, the algorithms themselves could be replaced in these decentralized data markets. This would make them more accessible to a wider audience and could accelerate their development.
Decentralized data exchange has the potential to revolutionize data storage. In principle, anyone should be able to rent local storage for a fee (paid in tokens). Existing storage service providers, in turn, would need to improve their services to remain competitive.
AI training requires not only quality data from which algorithms can learn but also immense computing power. AI algorithms often use a type of computer system known as an artificial neural network (ANN). ANN learns to perform tasks based on many examples. These ANNs often require a lot of computational power to go through millions of parameters to perform a given task.
If the data can be shared in a blockchain network, why not computing power? In some blockchain implementations, users can effectively lend the computing power of their machines in the peer-to-peer (P2P) market to those trying to perform complex calculations. In return, lending users are rewarded with tokens.
Artificial intelligence systems could be trained on these computer platforms much more efficiently and at a reduced cost. While early use cases are primarily concerned with rendering 3D computer graphics, the focus may shift toward AI.
When developing these decentralized applications (DApps), companies that provide computing power may experience an influx of competition. Allowing users to earn revenue from renting their idle computing power will be used more efficiently. Theoretically, any currently idle processor or GPU in the world can be used for this purpose and act as a node in a decentralized supercomputer.
Better audibility of AI decisions
For humans, the decisions that AI systems make can be difficult to understand. These algorithms can work with such a large amount of data that it would be practically impossible for a person to perform an audit and replicate their decision-making process.
If decisions are recorded on the basis of each data point, there is a clear control trail for people who could check them. This could increase confidence in the decisions taken by AI algorithms.
If these two technologies can fulfill their potential, they will undoubtedly have a lasting impact. While many companies use them separately, there are some interesting possible outcomes stemming from their combination.
As both technologies will evolve further, we will certainly discover other innovative ways to use blockchain technology with AI. The potential results are difficult to assess, but it is quite certain that they will lead to improvements in many aspects of our economy.