The proliferation of big data coupled with programmatic interfaces to utilise this information is leading to a quantum leap in several fields. Whereas at one time problem-solving was reliant on analytic models, today it is increasingly possible to create an infrastructure that solves these problems through synthetic means. This has been demonstrated recently by the rise of artificial intelligence chess programs, which are outperforming traditional chess software reliant on brute force calculation.
This has massive implications for both scientific and technological advancement. Human emphasis has always been highly centred on creating new axioms and models in order to solve major problems. But now simply collating and analysing the data, and using intelligent systems, could prove to be far more effective.
Mathematics in particular has always relied on the so called ‘Eureka moment’, in which major breakthroughs were derived from pre-existing functions and rules. New laws were found in physics, from which new knowledge and understanding were later derived. And computational science also relied on the development of new models, which then helped us to understand what would be ultimately computable.
Big data allied with the rapidly evolving artificial intelligence technology is completely changing this reality. The world of the Internet, and the information that it produces, is superseding such conventional methodology.
For example, a major retailer such as Amazon is in possession of vast amounts of data on its customers. It knows who buys what, and how its consumers act while on its website. But there are no laws underpinning this behaviour, meaning that understanding the data and structure inherent within Amazon operations requires a more evolved form of thinking and reasoning.
Moving from analytic to synthetic problem-solving will become an increasingly prominent aspect of big data. Everything from the creation of data, to the storage of data, and finally to the interfaces that scientists use to interact with data will be digitised and automated going forward, and this will produce more sophisticated understanding, and more preferable outcomes.
There are endless fields in which this reality is now being reflected in corporate behaviour. Even in something such as sports, major franchises are collecting vast amounts of data, knowing that analysing this information could give them a competitive edge. For example, professional sports teams go to great lengths in order to track every one of their players in every second that they spend on the field of play. This data is then used to literally better understand how games actually work.
We are also seeing huge emphasis in not only collecting as much raw big data as possible, but also in automating the actionability of this data. In high-tech industry, big data is increasingly viewed as a necessary evolution, as we shift from analytic to synthetic problem-solving. It definitely seems that the problems of the future will be solved by a much more sophisticated and data-rich approach than has been possible previously.