Ensuing deep learning models that run on a mobile phone

With a goal of leveraging the innovation that takes place in Silicon Valley by collaborating when a new product is taking shape, Nissan’s manager of data initiatives, Najam Baig joins us.

He shares that company makes cars not AI so his job is to bring in experts. He’s an expert in understanding the tools that he has and employing the right tool at the right time for the job at hand. His intent is to be as pragmatic as possible.

That said he’s focused on the internal data that he’s got at his fingertips- like supply chain information-and couples that with external data such as social listening to inform the enterprise. He’s realizing that deep learning model compute power is starting to reach an inflexion point “making them lightweight enough that they could run on a mobile phone.” He’s using his empirical knowledge of what’s happened in technology over the past two decades to posit that theory.

And it’s not just him that can make the connection on the pace of technology evolution, “just the other day in the office we were talking about how when USBs, when we brought USB floppy drives into the enterprise we had to get permission that we could copy data from that onto our floppy drives. So they would restrict that.”

“We go from there to today where you have cloud sharing enabled. It's not just the fact that the kind of devices changes, it's made it ubiquitous where you don't even need those kinds of devices anymore.That's hilarious.”

It’s not only the technology or the platform, it’s divining an understanding of what you are trying to accomplish and realizing that reality. Once the reality is realized, apply the most advanced yet reasonable technology that you have ‘on the day’ with the understanding that everything about that technology will change. 

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