The trick to understanding AI for entrepreneurs

Artificial intelligence remains a buzzword in many entrepreneurial and marketing circles, especially in D.C., but does it really have a high chance of stealing your job, or even revolutionizing your industry? Lauren Maffeo, software-as-a-service expert and senior content analyst at GetApp, provides some comforting answers: good changes are coming, but your job is probably safe.

ABERMAN: Well, I find that for many people, they conflate artificial intelligence and machine learning. I’m not sure they really understand the difference, or what the heck they’re talking about when they say artificial intelligence. Help us out a bit.

MAFFEO: You’re exactly right about that. Machine learning is often spoken about as something that is separate from artificial intelligence itself, but it’s actually a type of artificial intelligence that essentially retrains itself. So, machine learning is based on getting, ideally, large amounts of data, and then making decisions and informed choices based on that data, either through perception or prediction. And so. a really common example of machine learning at work today is Netflix.

Whenever you give Netflix any signals about the content that you like or dislike, you are giving the algorithm in Netflix more data that it can train itself on, and then it can, ideally, make more tailored predictions about the type of content that you would like or dislike as a consumer. There are some limitations with this, though. Machine learning algorithms can be easily fooled because they don’t understand nuance.

And so, if you were to program in content that you actually dislike, it would subsequently give you more content that you dislike, because again, those machines don’t understand the social nuance of how they’re being used, which again makes them susceptible to bad actors, either in a minor case like Netflix or with a more important technology.

ABERMAN: Like what happened with Google?

MAFFEO: Exactly.

ABERMAN: With the experiment around image recognition.

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MAFFEO: Exactly. And another common example of that, in a negative way, is Tay, which is a bot that Microsoft produced a few years ago, where it was trained to take in data from users on social media. And a few bad actors chose to feed it very racist content, and so then it subsequently learned what it was given, and then started producing racist content in and of itself.

Because the machines really only know what they are able to learn from, and that’s another thing that I think is a common of misconception about AI. It’s the fact that people think of it already as this standalone technology that is independently intelligent from humans, and we’re not there yet.

ABERMAN: No, we’re not there yet, but where we are, I think, is that artificial intelligence software is able to mimic, more and more often, the processes that we go through as humans to gather information, and make recommendations to a friend, or find a law case that’s relevant, or see what the disease state is in somebody.

I have heard many suggest that the technology, that machine learning and artificial intelligence, has gotten to a point where it really is creating job displacement. But I’ve also heard people, and read reports from significant consulting firms like PricewaterhouseCoopers, or McKinsey, say that it’s going to create an explosion of new jobs. I know you’re following this a lot, and you’ve written on it. What’s your view? Is it a good or bad thing?

MAFFEO: This is another area where there’s a lot of nuance that often gets lost in media coverage of AI. I think the most important thing for people to know is that, you can’t conflate narrow AI with general AI. Narrow AI is artificial intelligence that is programmed to solve one very particular problem. So, an example in the popular world is Deepmind, which programmed an algorithm that beat the world champion at Go, which is a very complicated game, and that was considered a huge breakthrough in AI technology, because of how complex the game of Go is.

But the thing to know about that algorithm is that, even though it performed better than a human in that case, it can only play Go. That’s the only thing the algorithm can do, it doesn’t have the range of capabilities, and certainly not the range of emotions, that a human does. And so, therefore, it’s not better than a human. What you’re talking about is general AI, which is artificial intelligence that purports to have the same range of depth, emotion, and capabilities as a human, and again, we’re not there yet. So, when we talk about AI replacing jobs, basically what it’s going to do, for the foreseeable future, is replace rules-based repetitive tasks, and that can be anything from, for example, driving a vehicle, to programming.

I mean, already, if you talk to any software developer, they want automate as much as they possibly can, but it doesn’t necessarily mean that AI is going to take the jobs of software developers. If you look at the long term forecasts for job growth, two thirds of future jobs within the next five to seven years are forecast to be cognitive-based, which means that they are not rules-based and repetitive, and they’re therefore less susceptible to automation.

The challenge is that, we don’t know what a lot of those jobs will be, and they will require constant retraining. And that’s something that, I think, is a risk with AI that is not discussed often enough. It’s the lack of education for these types of jobs right now, but also in the future, and there’s huge market demand for AI skills, but there isn’t a lot of information or education out there to help people fulfill those jobs. So, I think that’s the bigger risk than AI taking jobs from humans.

ABERMAN: Effectively, what you’re telling me is that, you should teach people how to think.

MAFFEO: Exactly. I mean, that really is what it comes down to. I think the high-level cognitive, strategic thinking is really the most valuable skill in the market, today, because again, there are so many tasks within different professions, from accounting to software development, that are being automated right now, and they will continue to be automated. And so, your job, as a professional, is going to change rapidly, because you will be focused on the more high-level strategic thinking.

ABERMAN: Well, Lauren, thanks a lot for taking time to come on and talk about this important issue. It was very insightful, and I’m sure we’ll have you on again. That was Lauren Maffeo, folks, we’re glad to have her. Thanks for being with us.

MAFFEO: Thanks for having me.

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