In the latest episode of the ArchiTECHt Show podcast, I speak with Baidu chief scientist Andrew Ng. Among prominent AI experts, Ng has a particularly unique and global perspective, perhaps because of his broad experiences. Aside from from heading AI strategy for a massive Chinese internet company, Ng also co-founded Coursera, taught machine learning at Stanford and was an early member of the Google Brain team.

Ng covers a wide range of topics in our interview, including:

How to choose the right AI interface for the right consumer device.

The market forces driving deep learning innovation in China.

The feasibility of doing deep learning as enterprise IT.

How tech companies and governments can address job losses, and entirely new types of jobs, resulting from AI.

Keep reading for highlights from the interview, and scroll to the bottom for links to listen to the podcast pretty much everywhere else you might want to.

In the news portion of the show, co-host Barb Darrow and I talk even more about AI—specifically Amazon’s open source strategy in that space, and if it can repeat the product-first success it had in the broader cloud computing market. (If Alexa is any indication, that seems like a good bet.) Speaking of cloud and AWS, we also discuss Microsoft’s cloud earnings and whether the company will be happy with second place in the long run.

Here are some highlights from the interview with Ng, but if you’re interested in AI and deep learning—or even the global economy—you really, really, really want to listen to the whole thing. There is a lot more conversation about each of these issues.

On choosing the right interface for consumers

“It turns out that speech recognition is probably the fastest way for you to communicate with a computer or mobile device, but a screen is the fastest way for a computer to communicate with you. “… If what you want to do is listen to music, then a smart speaker, even without a screen, could do just fine. But a screen really helps you with certain interactions that I think are additive, in addition to playing music and the few things that you only need voice for.”

On Chinese market forces driving innovation

“In China, there’s a very strong desire to consume non-Chinese-language content. So that, in turn, drove a lot of innovation in machine translation. For example, recently in the U.S., there was a lot of PR about … neural machine translation . . . . What a lot of people in the U.S. don’t know is that that was already landed, shipped to product about a year before in China.”

On enterprise adoption of AI

“In terms of AI … a lot of the work that needs to be done is customization. Today, AI talent is really scarce, and one of the reasons for this ‘talent war’ is because you can’t just download an open source AI package and, quote, ‘apply’ it to your enterprise. You really need insightful AI engineers or researchers to look at the problem, figure out what data sources you have, figure out what the outcome is, and really customize the AI to your business context. “… One interesting effect is that because of the global shortage of AI talent, I find that companies — including us — have to be very disciplined in terms of what projects to go for.”

On the economic effects of AI, and how to address them

“One of the challenges about AI is that we’re remarkably good at automating things that people can do. If you look the workflow of AI research, we’re actually much more efficient at developing algorithms that try to do this that humans can do, and we’re actually less efficient at automating things that even humans cannot do. “… One unfortunate consequence of this is that it tends to place AI in growing competition with humans for jobs. There will be plenty of cases where humans and machines will work side by side and be complementary, but there will also be cases where AI or other technology will displace human labor. “…. I feel like we have a responsibility to not whitewash the jobs issue with these other problems that may or may not appear for decades or hundreds of years. I think that for the technology community to maintain the trust of the United States, or society more broadly, we should speak more honestly and openly about the jobs issue, and work on solutions.”

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