Thoughts in 2021 about Hardware in Artificial Intelligence

Three game-changing prowess

All that in your pocket
  • Big Data is paradise for the mathematical side because the “N goes to infinity” property is nice for convergence, efficiency, prediction and generalization quality. Algorithms work better and easier (with less human expertise).
  • Big Data is hell for the computer side because it requires hardware and software infrastructures that barely existed in terms of memory, programming and computing issues: space, access, speed, energy etc.
  • electronic computations were redefined by the Apple M1 technology: the same component contains the equivalent of improved CPU, GPU, RAM and extra Neural Engine without communicating through a motherboard. The induced saved heat and computation power give Apple the freedom to give up respected hardware makers such as Intel and Nvidia (in this case for old commercial reasons if my memory is correct) for running further AI features on mobile devices.
  • quantum computations were made available by Google Sycamore achieving quantum supremacy. This means it can solve problems that no classical computer can solve in any feasible amount of time. It’s very difficult to imagine how extreme the unlocked possibilities will be. Many scenarios when scientists had to say “this is not tractable, not feasible, this is impossible” will disappear for sure.
  • photonic computations are available right now thanks to the LightOn company. This technology shift is more than hundred times faster (1500 TOPS = 1500 x 10¹² operations per second) than a good electronic CPU (10 TOPS) with less the energy consumption of a human brain (20 Watts) or an office light bulb (100 Watts). To be honest, I cannot help myself from daydreaming about the AI implications.



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