According to Moore’s law (1965), the rate at which our technology is growing is adhering to an exponential curve, with our computers being twice as fast every two years, while the production costs are halving. Even if, nowadays, there are good reasons to doubt the predictive validity of this very notion, the last three decades have empirically proved it rather right. Already it seems a very old memory when the new 1,44MB floppy discs came out or when they got replaced by the pioneering CDs and DVDs. I mean the Internet, as we know it, exists since the early 90s.
Within the last decade, twentieth-century sci-fi concepts, such as human-like robots and smart computers, have migrated from the realm of fantasy to the realm of scientific reality. Consider your smartwatch, these self-driving trucks transporting goods across countries, Bitcoin or, even, Sophia, the first-ever robot to receive human citizenship.
All these innovations have been realized thanks to the unprecedented growth of the field of computer science. Despite it being a young discipline compared to older sciences of the likes of mathematics or physics, computer science has utilized and combined advancements in cryptography, algorithmic algebra, statistical inferencing, and machine learning to yield two rather contemporary yet revolutionary products; artificial intelligence (AI) and distributed ledger technology (DLT).
On the one hand, DLT or commonly referred to as blockchain is a digital distributed ledger that is decentralized, transparent, and immutable and is used to store cryptographically encrypted data. Essentially, it is a networking architecture that ensures the safeguarding of time-stamped data in a public and open-access fashion, while also allowing for peer-to-peer transactions as it is eliminating intermediaries that traditionally mediate such transactions, i.e. financial institutions, retailers, etc. The ledger’s immutability and transparency functions as the “trusted party” that allows for such disintermediation.
On the other hand, AI technology is based on the mimicry of human intelligence functions, such as logic, analytical thinking, and pattern recognition and, thus, can be metaphorically thought of as an artificial brain able to detect relevancy among a chaotic set of information, as well as analyze, and optimize outcomes based on provided large datasets.
These two technologies have been developed on the basis of handling data in revolutionary and unique ways. The emergence of the World Wide Web came along with massive amounts of data, that is all information related to our digital lives. Until recently, the management of these huge datasets was in the hands of the very few companies that ran the industry, as all these recent data scandals, that paved the way for contemporary data regulations (such as, GDPR), have explicitly proven.
At the same time, the economic crisis of 2008 highlighted the need for a new model that would underlie both our financial mechanisms as well as our data management methods. Therefore, it became clear very fast that there has to be a more efficient and authority-independent way to handle this information. In other words, the decentralization of data and economic processes was a logical, and much needed, next step.
In my personal point of view, decentralization was already a necessary change with regard to shifting our understanding and function of politics, however, it became more apparent as a new economic paradigm. Its fundamental principle lies upon eliminating the friction that originates from unnecessary third parties that are currently mediating transactions as well as the respective data flow.
Blockchain & AI convergence
Although both AI and blockchain technologies are currently at a rather embryonic stage, it wouldn’t be frivolous to envision a joint future of these two industries. In fact, the convergence of the two industries seems inevitable as both utilize data to yield value. Blockchain enables secure storage and transparent sharing of data, while AI can analyze and generate insights from data to propagate value.
As a matter of fact, it is rather obvious that the two technologies are complementary to each other, if not dependent on each other. DLT can contribute towards a more thorough and coherent understanding of AI, which, in turn, could help humans to make sense of the whereabouts of decisions taken by machine learning algorithms. On the counterpart, AI can boost blockchain efficiency far better than humans, or even standard computing can. Considering the currently massive computational resources needed for blockchains to run on standard computers for performing rather simple tasks, AI’s potential in facilitating and easing this process is indisputable.
Current use-cases
How could, then, AI intersect with blockchain towards a convergent future?
There are already many use-cases that are utilizing the joint advantages of these two tech branches. It would go beyond the scope of the present narrative to delve deeper into a detailed overview of current use-cases. However, it is important to name a few industries that have been positively disrupted by the merging of these two technologies in order to understand the magnitude of the potential of such convergence.
Transportation, mobility, energy market, supply chain, mining, and healthcare are just a few of the many industries that have already witnessed the benefits of AI and DLT cooperation, with projects such as SingularityNET, Gainfy, Fetch.ai, and Neureal already offering a wide array of services realized by the synergy of artificial intelligence and blockchain.
Remaining thoughts & questions
Discussions about the endless possibilities that would result from the convergence of AI and DLT are fruitful yet rather diaphanous considering the current insubstantial empirical evidence.
However, as far as I am concerned, the foundational concept underlying such discussions is decentralization. From a political standpoint, decentralization is the path towards achieving increased levels of democratization, in an era where even the most democratized countries cannot escape the need for “trusted” intermediaries to determine decision- and policy-making processes.
If the ultimate goal of decentralization would be to erase all intermediaries that are currently mediating all transactions irrespectively of the industry (politics included), then we are talking about a scenario where the only intermediary would be the algorithm itself.
If that is the end scenario, then we should focus on how this algorithm should look like. That would be an interesting discussion. So, how else could this algorithm be like if it wouldn’t be AI-based?
Should a machine/algorithm be the “centralized” authority in decentralized governance? Can we ever overcome the distinction between centralized and decentralized? Frankly, there are some aspects of our nature that point, among others, towards the need for leadership, or the need for dominance over other members of the same species, but leadership and authority are two different concepts. People might perhaps think, instead of some governance run by a few oligarchs, we are now talking about governance run by a “fair/intelligent” algorithm; is this better? Is it closer to a democratic dystopia or an authoritarian utopia?
Conclusion
It seems unavoidable that in the near future two key components of several industries would be artificial intelligence and distributed ledger technology. In fact, it seems that wherever there is decentralization, there will be AI as well in one way or another.
Putting the two technologies together has the potential to use data in ways never before thought possible. Data is the key ingredient for the development and enhancement of AI algorithms, and blockchain secures this data, allowing us, humans, to audit all intermediary steps. Undoubtedly, the first steps should focus on leveraging the potential of such convergence for facilitating our financial practices that, in turn, will impact several other industries.
The ultimate challenge, though, seems to be how this very merging of these two powerful yet young technologies can elevate our political consciousness, and shift it towards collectively achieving higher life quality standards both for our species and our planet.
