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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.
Large language models (LLMs) may not be the first thing that comes to mind when discussing applied AI. However, people in the know believe that LLMs can internalize basic forms of language, whether it’s biology, chemistry, or human language, and we’re about to see unusual applications of LLMs grow.
If LLMs are anything to go by, we can reasonably expect to see commercial applications of multimodal models in 2022.
O’Reilly’s AI Adoption in the Enterprise 2021 survey cites technology and financial services as the two domains leading AI adoption.
As for manufacturing, there are a few reasons why we choose to highlight it among the many domains trailing in AI adoption. First, it suffers a labour shortage of the kind AI can help alleviate. As many as 2.1 million manufacturing jobs could go unfilled through 2030, according to a study published by Deloitte and The Manufacturing Institute. AI solutions that perform tasks such as automated physical product inspections fall into that category.
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