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.
ERIK BRYNJOLFSSON: We need to make a bunch of changes across the board at the policy level. Businesses need to rethink how they work. Individuals need to take personal responsibility for learning the new skills that are going to be needed going forward. If we do all those things I’m pretty optimistic.
But I wouldn’t want people to become complacent, because already over the past 10 years a lot of people have been left behind by the digital revolution that we’ve had so far. And looking forward, I’d say we ain’t seen nothing yet. We have incredibly powerful technologies especially in artificial intelligence that are opening up new possibilities. But I want us to think about how we can use technology to create shared prosperity for the many, not just the few.
SARAH GREEN CARMICHAEL: Are there tasks or jobs that machine learning, in your opinion, can’t do or won’t do?
ERIK BRYNJOLFSSON: Oh, there are so many. Just to be totally clear, most things, machine learning can’t do. It’s able to do a few narrow areas really, really well. Just like a calculator can do a few things really, really well, but humans are much more general, much more broad set of skills, and the set of skills that humans can do it is being encroached on.
Machines are taking over more and more tasks are combining, teaming up in more and more tasks but in particular, machines are not very good at very broad-scale creativity you know. Being an entrepreneur or writing a novel or developing a new scientific theory or approach, those kinds of creativity are beyond what machines can do today by and large.
Secondly, and perhaps for an even broader impact, is interpersonal skills, connecting with the humans. You know we’re wired to trust and care for it and be interested in other humans in a way that we aren’t with other machines.
So, whether it’s coaching or sales or negotiation or caring for people, persuading, people those are all areas where humans have an edge. And I think there will be an explosion of new jobs whether it’s for personal coaches or trainers or team oriented activities. I would love to see more people learning those kinds of softer skills that machines are not good at. That’s where they’ll be a lot of jobs in the future.
SARAH GREEN CARMICHAEL: I was surprised to see in the article though, that some of these AI programs are actually surprisingly good at recognizing human emotions. I was really startled by that.
ERIK BRYNJOLFSSON: I have to be careful. One of the main things I learned working with Andy and going to visit all these places is never say never, any particular thing that one of us said “oh this will never happen,” you know, we find out that someone is working in a lab.
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.