#220 Machine Learning and Artificial Intelligence (AI) with Bloomberg Beta

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Software systems that learn are on the cutting edge of practical artificial intelligence. But what’s the market for these mysterious technologies, and how will they transform the business economy? James Cham, a venture capital partner in the investment firm Bloomberg Beta, explains the machine learning market and how these technologies will change our world.

Mr. James Cham is a Partner at Bloomberg Beta L.P. At the firm, he focuses on early-stage startup investments in data-centric and machine learning-related companies. Mr. Cham was a Principal at Trinity Ventures which he joined in 2010 and focused on investments in consumer services specifically e-commerce, social media, and digital media. He was a Vice President at Bessemer Venture Partners. He focused on advanced web technologies and was instrumental in eight new investments in consumer internet services, security, and digital media sectors along with a number of seed investments. He was a part of Data Security investment team. He was a Consultant of Boston Consulting Group. At, Boston Consulting Group, he developed marketing strategies for entertainment and information technology companies.

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Extract from the transcript:

Our specific core thesis is around the future of work, and the insight is that with any new technology, whether we're talking about railroads or we're talking about electricity, it takes a few generations of managers to figure out exactly what to do with the technology, because even when something starts to work, it's not that useful until people figure out what to do with it. And so, we are now here, twenty-something years into the ubiquitous network computer, and we're at the point now where managers and organizations are figuring out how to use this technology in order to actually improve the economy. And so, here we are, and we're feeling quite excited.

So, you are very focused on machine learning and artificial intelligence. You and your partner came up with a market landscape of machine learning Can you maybe link together for us this notion of Future of Work, AI, machine learning, and similar technologies?

I end up with machine learning.

The first is that we live in a world now where everyone is a knowledge worker; and, He’s able to capture information and manipulate it.

Now, it turns out that there’s a class of people out there who use knowledge in interesting and clever ways. And those, of course, are software developers. And as a software developer, I’ve recognized who my [...] that … You know, I could easily be three or four times more productive than an average colleague, in part because I understood what could be automated, lent itself to a certain type of automation.

How did you end up getting involved in machine learning, and what’s the link to machine learning?
And the other way that things are changing is we saw four years ago that machine learning, and AI in general, we saw glimmers that as both the technology was getting better, most advanced tech companies, And when that happens, then that’s the best way to learn.

the terms “AI” and “machine learning” were out of fashion, so people would hide the fact that they were doing something that you might want to call “AI.” And so, even though Facebook and Google and Amazon were making all sorts of interesting investments, all the very startups that were working on AI-related things were kind of quiet about it. digging in order to identify interesting companies.
So, what is it about machine intelligence and these various derivatives: machine learning, AI, deep learning, cognitive computing, and so forth? Why, and in what ways, are these technologies different from traditional software?
Hmm. So, you know, at their core, we, as software developers, are giving up an awful lot of control, right? Because rather than writing and codifying business rules in systematic ways, or understandings of schemas, we are giving up some of that control to other algorithms to generate decisions that we would think that we would want to make ourselves. And, I think that that is actually quite difficult. It reminds me, in some ways, of how you asked me earlier: how do I assess machine learning startups, or how do I think about that right now? You know, it was true probably three or four years ago that I would be looking for people who came out of the best research labs, or people who came out of some of the companies that have actually been running the large-scale machine learning projects for a while. And, while that's still true, the interesting shift on my side is that I am now looking more and more for people who are trying out different business models because …