Machine Learning Theory and Applications

Conclusion: The Future of Computing for Data Science?

Photo by Annamária Borsos

Classical computing has experienced remarkable progress guided by Moore's law. This law states that every two years, we double the number of transistors in a processor and at the same time increase performance by or reduce costs by twofold. This pace has slowed down over the past decade, forcing a transition. We must rethink information technology (IT) and in particular move toward heterogeneous system architectures with specific accelerators in order to meet the need for performance. The progress that has been made in raw computing power has nevertheless brought us to a point at which biologically inspired computing models are now highly regarded as the state of the art.

Artificial intelligence (AI) is an area that brings opportunities for progress but also challenges alongside. The capabilities of AI have greatly increased in their ability to interpret and analyze data. AI is also demanding in terms of computing power because of the complexity of workflows. At the same time, AI can also be applied to the management and optimization of entire IT systems.

In parallel with conventional or biologically inspired accelerators, programmable quantum computing is emerging, thanks to several decades of investment in research to overcome traditional physical limitations. This new era of computing will potentially ...