Erik Brynjolfsson, MIT Sloan School professor, explains how rapid advances in machine learning are presenting new opportunities for businesses. He breaks down how the technology works and what it can and can’t do (yet). He also discusses the potential impact of AI on the economy, how workforces will interact with it in the future, and suggests managers start experimenting now. Brynjolfsson is the co-author, with Andrew McAfee, of the HBR Big Idea article, “The Business of Artificial Intelligence.” They’re also the co-authors of the new book, Machine, Platform, Crowd: Harnessing Our Digital Future.

of the new book, Machine, Platform, Crowd: Harnessing Our Digital Future.

ERIK BRYNJOLFSSON: Yeah, it’s a funny image. It always gets a laugh and encourage people to go take a look at it. And there are lots of things that humans are pretty good at in distinguishing different kinds of images. And for a long time, machines were nowhere near as good as recently as seven, eight years ago, machines made about a 30 percent error rate on image net, this big database that Fei Fei Li created of over 10 million images. Now machines are down less, you know, less than 5%, 3-4% depending on how it’s set up. Humans still have about a 5% error rate. Sometimes they get those puppies and nothing’s wrong. Be careful what you reach for next time you’re at that breakfast bar. But that’s a good example.

 

But often they can find ways of having data that’s already tagged in some way, that’s generated from their enterprise resource planning system or from their call center. And if they’re clever, that will lead to the creation of this tag data, and I should back up a bit and say that machines, one of their big weaknesses is that they really do need tag data. That’s the most powerful kind of algorithm, sometimes called supervised learning, where humans have the advanced tag and explained what the data means.

Going forward, I think this is an example of how the bots are often good at the more routine repetitive kinds of tasks, the machines can do the ones that they have lots of data for. And the humans tend to excel at the more unusual tasks for most of us. I think that’s kind of a good trade-off. Most of us would prefer having kind of more interest in varied work lives rather than doing the same thing over and over.

SARAH GREEN CARMICHAEL: So, sales is a form of knowledge work right and you sort of gave an example there. One of the big challenges in that kind of work is that you can’t — it’s really hard to scale up one person’s productivity if you are a law firm, for example, and you want to serve more clients have to hire more lawyers. It sounds like AI could be one way to get finally around that conundrum.

ERIK BRYNJOLFSSON: Yeah AI certainly can be a big force multiplier. It’s a great way of taking some of your best, you know, lawyers or doctors and having them explain how they go about doing things and give examples of successes and the machine can learn from those and replicate it or be combined with people who are already doing the jobs and help in a way coached them or handle some of the cases that are most common.

SARAH GREEN CARMICHAEL: So, is it just about being more productive or did you see other examples of human machine collaboration that tackled different types of business challenges?

ERIK BRYNJOLFSSON: Well in some cases it’s a matter of being more productive, in many cases, a matter of doing the job better than you could before. So there are systems now that can help read medical images and diagnose cancer quite well, the best ones often are still combined with humans because the machines make different kinds of mistakes in the humans so that the machine often will create what are called false positives where it thinks there’s cancer but it’s really not and the humans are better at ruling those out. You know maybe there was an eyelash on the image or something that was getting in the way.

And so, by having the machine first filter through all the images and say hey here are the ones that look really troubling. And then having a human look at those ones and focus more closely on the ones that are problematic, you end up getting much better outcomes than if that person had to look at all the images herself or himself and maybe, maybe overlook some potentially troubling cases.

SARAH GREEN CARMICHAEL: Why now? Because people predicted for a long time that I was just around the corner and sounds like it’s finally starting to happen and really make its way into businesses. Why are we seeing this finally start to happen right now?

ERIK BRYNJOLFSSON: Yes, that’s a great question. It’s really the combination of three forces that have come together. The first one is simply that we have much better computer power than we did before. So, Moore’s Law, the doubling of computer power is part of it. There’s also specialized chips called GPUs and TPUs that are another tenfold or even a hundredfold faster than ordinary chips. As a result, training a system that might have taken a century or more if you done it with 1990s computers can be done in a few days today.

And so obviously that opens up a whole new set of possibilities that just wouldn’t have been practical before. The second big force is the explosion of digital data. Data is the lifeblood of these systems, you need them to train. And now we have so many more digital images, digital transcripts, digital data from factory gauges and keeping track of information, and that all can be fed into these systems to train them.

And as I said earlier, they need lots and lots of examples. And now we have digital examples in a way we didn’t previously and in the end with the Internet are things you can imagine it’s going to be a lot more digital data going forward. And last but not least, there have been some significant improvements in the algorithms the men and women working in these fields have improved on the basic algorithms. Some of them were first developed literally 30 years ago, but they’ve now been tweaked and improved, and by having faster computers and more data you can learn more rapidly what works and what doesn’t work. When you put these three things together, computer power, more data, and better algorithms, you get sometimes as much as a millionfold improvement on some applications, for instance recognizing pedestrians as they cross the street, which of course is really important for applications like self-driving cars.

SARAH GREEN CARMICHAEL: If those are sort of the factors that are pushing us forward, what are some of the factors that might be inhibiting progress?

ERIK BRYNJOLFSSON: What’s not holding us back is the technology, what is holding us back is the imagination of business executives to use these new tools in their businesses. You know, with every general-purpose technology, whether it’s electricity or the internal combustion engine the real power comes from thinking of new ways of organizing your factory, new ways of connecting to your customers, new business models. That’s where the real value comes. And one of the reasons we were so happy to write for Harvard Business Review was to reach out to people and help them be more creative about using these tools to change the way they do business. That’s where the real value is.

SARAH GREEN CARMICHAEL: I feel like so much of the broader conversation that AI is about, will this create jobs or destroy jobs? And I’m just wondering is that a question that you get asked a lot, and are you sick of answering it?

ERIK BRYNJOLFSSON: Well of course it gets asked a lot. And I’m not sick of answering because it’s really important. I think the biggest challenge for our society over the next 10 years is going to be, how are we going to handle the economic implications of these new technologies. And you introduced me in the beginning as a cautious optimist, I think you said, and I think that’s about right. I think that if we handle this well this can and should be the best thing that ever happened to humanity.

But I don’t think it’s automatic. I’m cautious about that. It’s entirely possible for us to not invest in the kind of education and retraining of people to not do the kinds of new policies, to encourage business formation and new business models even. Income distribution has to be rethought and tax policy things like the earned income tax credit in the United States and similar wage subsidies in other countries.

There are so many areas where you can apply these technologies right now you can take courses or you can have people in your organization take courses or you can hire people at places like Udacity or fast.ai, my friend Jeremy Howard runs a great course in that area, and put it to work right away and start with something small and simple.

But definitely don’t think of this as futuristic. Don’t be put off by the science fiction movies whether, you know, the Terminator or other AI shows. That’s not what’s going on. It’s a bunch of very specific practical applications that are completely feasible in 2017.

SARAH GREEN CARMICHAEL: Erik, thanks so much for talking with us today about all of this.

ERIK BRYNJOLFSSON: It’s been a real pleasure.

SARAH GREEN CARMICHAEL: That’s Erik Brynjolfsson. He’s the director of the MIT Initiative on the Digital Economy. And he’s the co-author with Andrew McAfee of the new HBR article, ” The Business of Artificial Intelligence.”

You can read their HBR article, and also read about how Facebook uses AI and Machine learning in almost everything you see, and you can watch a video – shot in my own kitchen! – about how IBM’s Watson uses AI to create new recipes. That’s all at hbr.org/AI.

Thanks for reading.