What will applied AI look like in 2022?

So-called AI chips, a new generation of hardware designed to optimally run AI-related workloads, are seeing explosive growth and innovation. Cloud mainstays such as Google and Amazon are building new AI chips for their datacenters — TPU and Trainium, respectively. Nvidia has been dominating this market and built an empire around its hardware and software ecosystem.

Intel is looking to catch up, be it via acquisitions or its own R&D. Arm’s status remains somewhat unclear, with the announced acquisition by Nvidia facing regulatory scrutiny.

Selecting what hardware to run AI workloads on can be thought of as part of the end-to-end process of AI model development and deployment, called MLOps — the art and science of bringing machine learning to production. To draw the connection with AI chips, standards and projects such as ONNX and Apache TVM can help bridge the gap and alleviate the tedious process of machine learning model deployment on various targets.

What we see as the most profound shift, however, is the emphasis on so-called data-centric AI.