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AI hardware is a fast-growing interest among tech media, and there is a lot of opportunity for computer hardware developers when it comes to building chipsets for AI. That said, margins for AI chipsets can differ wildly depending on the use-case for which they’re being built.

Customer data is essential for insurance firms to stay competitive in the coming decade. Insurance companies at present have backlogs of data on past and existing customers in the form of policy agreements, applications, and claims forms. They’ve also collected millions of images showing car damage, property damage, and personal injuries.

There's an entire artificial intelligence ecosystem for enterprise search. Most of this is in a purely digital world. Most vendors help with a layer of AI-enabled search that understands terms or phrases and is able to return the results or answers to questions that someone types in. But the problem is compounded when it comes to searching the physical world.

Large banks deal with millions of documents every day across their corporate offices and numerous branches. Although one might assume that these documents are digital, in many cases, even the largest banks store old physical documents in file cabinets and boxes off the bank’s premises, and even those that are kept on-site might be relegated to storage units amongst hundreds of thousands of other documents.

The financial sector has been an early adopter of AI. It is likely that the use of algorithms in trading and the fact that most large financial firms already have teams of software developers aided the transition into data science and AI applications in the industry.

In this episode of the AI in Industry podcast, we speak with Marshall Choy, VP of Product at SambaNova, an AI hardware firm based in the Bay Area. SambaNova was founded by a number of Oracle and Sun Micro Systems alumni. We speak with Choy on two fundamental questions:

Danny Lange heads up the AI efforts at Unity, one of the better-known firms in terms of simulations and computer graphics. They work in several different industries, but this week we speak mostly about automotive.

I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't.