Bull Session

Human Intelligence Vs. AI

October 5, 2017          

Episode Summary

On the podcast this week, we’ll discuss some of the areas where human intelligence may outperform AI and vice versa. AI is good at processing and discovering certain kinds of information — from data mining to predictive forecasting to optimization — while humans can add a layer of expertise, judgment, and insight. Join us as we talk about about how humans might work separately from as well as in conjunction with machines in an AI-driven future.

Resources:

Where human intelligence outperforms AI

Jon:
Welcome to episode 227 of The Digital Life, a show about our insights into the future of design and technology. I’m your host Jon Follett, and with me is Founder and co-host Dirk Knemeyer.

Dirk:
Greetings listeners.

Jon:
For our podcast topic this week, we’re going to discuss human intelligence versus artificial intelligence. AI is great at processing and discovering certain kinds of information, while human beings can add a layer of expertise, judgment, and insight. It’s easy to think of some of the areas where AI can do a good job, in particular, data mining is a huge area where AI excels. It can locate problems, it can see changes in very large volumes of data, and it can use that information to try to predict what might happen next, so that we can optimize systems based on previous information. Whereas humans, there’s an awful lot of insight and intuition and general expertise around particular topic areas that humans can bring to bear.

I know I set this up as a human versus machine thing at the very beginning, but there’s also the possibility for machines to accentuate and enhance human thinking. One interesting way that’s being approached right now is by using crowd source solutions to discover areas of information that might not be readily available. So if you don’t have that information nicely formatted in data in your database, it’s going to be awfully difficult to search that data and to optimize that if you don’t have it in there at the moment. For items like research into particularly difficult areas, areas that require a lot of expertise, sometimes crowd sourcing can yield great results and information that may not be accessible to an AI, or a Google, for instance.

Dirk, as we see more and more of the wave of Artificial Intelligence in forming the way software is getting built, and the way that we are analyzing information, how do you see humans both working in conjunction with AI, and then also perhaps other areas that we think that human beings can sort of exclusively do better than Artificial Intelligence?

Dirk:
Yeah, so in both questions it really comes down to a question of context, and humans are very good at context, Artificial Intelligence not so much. We’ve talked a lot about AI on the show over the years, and for our new listeners or just as sort of a primer, a repeat for everyone, we have to remember that right now we’re in a period of weak AI, right, which is AI is a tool. AI that’s basically created basically to do one thing and do it as well as the available technology today will make it possible. So it’s doing something very narrow and very specific.

Where context in a broader sense can be brought to bear is in General Artificial Intelligence, which is also the strong AI. And General Artificial Intelligence right now is science fiction, so we are, you know, Chris Nelson was telling me last week that there was somebody did online an aggregation basically of all of these top scientists, engineers, people who would know, and said how far are we from General AI. The average, or the mean, or the, basically it’s 50 years. 50 years, some say faster, some say slower. So, 50 years is a long freaking way out, and so that still lives in the space of science fiction. And then of course the ultimate in Artificial Intelligence will be Super Artificial intelligence, that’s an Artificial Intelligence that is basically creating a being or maybe keeping it less charged I won’t call it a being, but creating a thing that is sort of demonstrably more powerful, more intelligent, is an improvement over humanity. And that’s total science fiction, right? I mean, that’s centuries not decades, whereas general is decades and weak AI is what we have today.

I’m kind of talking about all of this from the standpoint of talking about where humans are valuable. It’s in that we understand a broader context, we’re not just one tool. We get the world in a bigger way. A weak AI, a good weak AI even today is going to smoke a human in a very limited thing that allows it just to have blinders on and not know or pay attention to anything else. Otherwise, humans are infinitely more powerful and effective. In the article that you had, we didn’t go into the specifics of the kind of work that was being done there, but what was critical and important was that broader view, that broader focus where it’s not just “this is a search for the specific thing that is only going to be found in this way.” It was instead taking a certain environment as a starting point and looking for the unexpected, looking for more.

The way that humans and AI are going to work together is that AI, and this is again, in the short term weak AI where we are today, AI is providing a tool that can go very deep, very fast, very hard, in super narrow applications. And then humans can take that and build upon it. Yeah.

Jon:
Yeah, I think it’s sort of the next generation of software in and around knowledge work that’s going to start experimenting with these different ways that human beings can sort of work with Artificial Intelligence. So the tech crunch article that we’re discussing talks about basically a crowd source research firm called Article One Partners, they’re doing some interesting work in new product innovation and whether that’s research for material science or finding prior art for patents, things like that. Where there’s sort of a broad array of possibilities to be discovered, and that they have tens of thousands of researchers that they can throw at the problem.

As I was thinking about that, the crowd source in it of itself, you know you’re using software, right, because you’re accessing this crowd via a platform, presumably, that allows a person or a company with a problem to define it and give it to not just one human mind but many human minds linked up by whatever this platform can do. So you can see whether that’s an advanced, very high-level mechanical turk type of program, versus feeding it into IBM lots and where a lot of already digitized data is available to be searched.

Those two models, the big pile of data versus the crowd source research, I think there are interesting ways that that could come together, depending on how much the crowd is able to digitize their discoveries as they’re doing it, versus doing all the analysis and synthesis in their heads and just giving the customer or a client the end results, right?

Dirk:
Yeah.

Jon:
So it’s those interactions that I think, and those possibilities that are going to be a part of this next wave of software, and is really going to start getting interesting because we’re going to find out really quickly what humans are good at and where the machines can be helpful and perhaps eliminate certain parts of jobs that otherwise would be repetitive, or time consuming without producing value. But one of the other insights from this is that knowledge workers I think are going to have a big surprise in finding out what they get paid for in the future, versus what is a commodity. And the things that we think are quite valuable right now could very well be the very things that get automated first. And it’s the context, as you said, that’s probably going to end up driving a lot of the human side of the equation, the understanding of context as, you know what I mean.

Dirk:
Yeah, yeah, you know I think in the short term the sort of tasks and jobs that are being lost aren’t the ones that we perceive as more valuable, right? It’s in sort of the longer term of as weak AI gets better, while still being weak, that we are sort of surprised by the types of jobs and tasks that are being taken away. The unknown is, what is clear is that things that are repetitive, things that are rote, things that are sort of simple structure, you know I mean the, we talked about on the previous show like you no longer have to cut something out of a Photoshopped document, it will just auto-magically happen, which saves a lot of time, which means you need less designers to do, or production artists to do a certain amount of stuff.

The question is then, are there more things that are being found that are thinking things, that are real knowledge work for those individuals to do, or is there just less work for that type of a person? And there’s just right now some unknowns there. I mean, for me at least, I haven’t studied it or thought about it enough, but it could go one of a couple of things at the extremes. We start to do a lot more thinking knowledge stuff, and we’re putting our minds to work in sort of exhilarating ways that have unpredictable and potentially wonderful impacts in the world. Or on the other extreme, there’s just not enough work and designers are going to have to go to, I was going to say slinging coffee or something, but those jobs are going to be gone too, and quickly. I don’t know where they’d go, but it won’t necessarily be the exciting tra-la-la of “Jeez we’re all using our big brains in bigger ways.”

Jon:
Right, so you gave sort of the two options, the exciting work that challenges us, and the being put out of work, and I pick both right, so I think both of those things are going to happen. And I wonder, in some ways, if this isn’t the sort of the first industrial revolution for knowledge workers, right? So the-

Dirk:
The first industrial revolution for knowledge workers, okay.

Jon:
Right, so you have the first industrial revolution displacing weavers, right, and the famous Luddites, or you know, sort of disaffected workers because the loom could do a chunk of the work that they were used to getting paid for. So famously, you’re a Luddite if you don’t like technology. As we digitized and created this new realm of knowledge work, we’ve essentially created the craft, the weaver’s craft of the early 21st Century and I wonder if AI isn’t the loom that’s dropping down on the knowledge workers, unbeknownst to us, and all of a sudden we’re going to discover that there’s this digital loom that is changing our lives in unexpected ways. That’s what I meant.

Dirk:
Sounds like a good way to end the show.

Jon:
All right. Listeners, remember that while you’re listening to the show you can follow along with the things that we’re mentioning here in real-time. Just head over to thedigitalife.com, that’s just one L in The Digital Life, and go to the page for this episode. We’ve included links to pretty much everything mentioned by everybody, so it’s a rich information resource to take advantage of while you’re listening, or afterward if you’re trying to remember something that you liked. You can find The Digital Life on iTunes, SoundCloud, Stitcher, PlayerFM, and Google Play. And if you’d like to follow us outside of the show, you can follow me on Twitter @JonFollett, that’s J-O-N-F-O-L-L-E-T-T, and of course the whole show is brought to you by GoInvo, which you can check out at goinvo.com, that’s G-O-I-N-V-O dot com. Dirk?

Dirk:
You can follow me on Twitter @DKnemeyer, that’s @D-K-N-E-M-E-Y-E-R, and thanks so much for listening.

Jon:
So that’s it for episode 227 of The Digital Life. For Dirk Knemeyer, I’m Jon Follett, and we’ll see you next time.

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