03 Don Norman: Human(ity) Centred AI – Pt.2

that's what we're going to be doing with our machines, and that will make a lot of the complexity that we worried about, remembering what every control does, or remembering how I should do something, I don't need to know that anymore. I just tell the system, no, you didn't do it right, do it again. In fact, I'm writing, I'm giving a commencement addressing on Sunday just a few days away. I wrote it and I thought but it's much too long.

And so I asked, I, I'm in Microsoft Word and I just said, Would you summarize this, make it shorter? I have a, I have, I don't want the talk to be more than 20 words, 20 minutes. So why don't you aim for 15 minutes? And I said, okay. And it did it. And I look it over and I say, oh, well, you cut out some of the stuff I thought was important, that's okay. it's because what we're doing it's my assistant. I'm then still in charge.

And it's like when I asked an assistant, would you please summarize this and make it shorter? And I look at it and I say, well. you didn't do it quite right. I can either say, here's what I want you to do, or I can say, okay, that's good enough that now it's easy for me to make it the way I want it to be. And that's going to happen more and more.

I We're writing all these documents for my charity and trying to send them out and explain to people and one of them, one of the people who did things says, Oh, but all the headings are wrong. They're not standardized. We don't even use the same font or the same way of describing things. And he, and I don't have the time to do it. Well, we could ask our machines, Hey, clean up the headings, clean up the heading style and clean up the typography.

And what are differences is going to make in our world?

because, you can go back to this debate between and the specific again. Right. So, you've got a lot of excitement around interfaces and agents where don't have to think about which tool you just ask. You, you ask the chatbot question or you give it a task

It has changed the world. But it took another 15 years to do it. 15 to 20 years before it did. It was a camera that didn't require film. Many people don't even know Apple came out with one of the very first consumer digital

Ten bytes Ten. Not ten thousand, not ten million. Ten. And so, you know, no wonder it did fail. The world wasn't ready for it. the same with the camera. I said, Once we started putting a camera in, I said, Oh, that's wonderful for tourist pictures and for, reminding you of something, but you'll never get a good picture. And I said, It's not my opinion, it's a law of physics, that receptor is so small, lucky if it gets one photon on each of the pixels.

And so it's just not enough, they're not big enough to give you a high quality picture. I was correct, but what I didn't know was that new computational methods will come around so that today we have these sales tiny receptors with millions and millions of pixels. What they can learn to do is they learn to combine the pixels into combinations. And so if you combine four pixels as if it's as if the receptor was four times the area.

Or you take three or four pictures rapidly again, it's as if you had four times the area and today we get camera pictures out of your little camera that wow they're really high quality And so, that's ai all that's ai there are lots of things that are AI and today's AI has no understanding. It's not, it's, I keep telling people it's artificial.

And it looks through of human writing, almost, and finds what is, what word is most common to follow those particular three. it has four, it does the same thing with four, and five, and six, and it makes a wonderful, document out of it. Well written, good English, good language, good grammar, but it's all made up, it's all nonsense. And sometimes it's great insight, sometimes we call it hallucination, but no, it's not hallucination, that's the way it works.

And And that it has lots of flaws, but you transform those flaws into benefits for education Because you're trying to teach the people to use it to enhance what they do which includes taking a look at what this device has created for you and Analyzing it and deciding whether it makes sense whether it's whether there's facts behind it or not So it actually enhances your ability and skills it's a really good assistant. It's simply It's intelligence is not human intelligence.

And what should I ask them for? And you could, it really helps you do lesson plans, helps you evaluate students, but most important of all, tells you where the students are weak or where they're strong and where they need some work. Or where they can go ahead faster.

So at least it'll be me. And my company, the Nielsen Norman Group, is, does lots and lots of publications and so we are experimenting with taking again, putting that into our AI system and so when people want to know something instead of, well, which one should I read, they can ask questions and they'll get an answer from our huge collection of helpful articles. But again, that means my cell phone, I'll ask.

Because the computation can be done either on the device you're using, or it can be done in the cloud.

But lots of iOSs I use the agents in my house entirely for setting alarms. Like, just for this podcast, I set an alarm ten minutes before the podcast was a start to give me time to get set up. and I also, we every so often ask it questions when we're at dinner and talking with my wife and I and we wonder about the meaning of something I'll ask it. And that's very useful.

but, so I think that what's happening is we're seeing that devices that have some kind of knowledge base behind them and therefore can answer questions or do tasks for you, I are really valuable, and because all they require is to talk to them, or type if you want to then, and eventually it'll be even that, they'll see what you're doing and they can suggest something. it can be any kind of device. It can be a general purpose device, it can be a specialized device.

You might have a camera located by your front doors, so it might be, It can tell you when somebody is coming into the front door, but maybe it can even tell you what kind of a person it is. I don't mean face recognition, I mean it might say, oh, this is the postman delivering a package, or etc. This is a bunch of children. And I could just imagine all sorts of new things that we couldn't do today that will be empowered by these new types of computational devices.

By the way, here's where designers should play a role. are always going to take away their jobs and, Oh, how am I going to do? And I say, well, you should learn to use them because it will make your job easier or different. But actually, why aren't designers in charge? aren't designers thinking of these new things that will really help people and design them?

And have companies build them and have, we should be telling people what you can do with some of these new kinds of computational devices and maybe what kind of small physical device you might want to have around with you that you carry with you or have it placed in the house that will help it. And the next step coming along, by the way, is just starting.

If you've seen what's happening in robotics, the Robots we have, well, when we start connecting robots with these new types of deep learning models, you're going to see another huge jump in capability, because they will understand how the world works. Today they just, all they know is because they've been reading, but pretty soon they'll be able to actually change the move and manipulate the world. I think that's really exciting what's

We sort of say, yeah, that sounds good, but let me, and so, what's nice is they now, a lot of the systems give you the references that they've used to say what they've said, you have to go and look at those references and then decide if that references yes, I can see where it came from, this reference, but do I trust this reference material?

to doing, So machines are great at Pattern recognition, rapid Data analysis and synthesis, reliable repetition, working around the clock, things like that. Humans tend to be much better at the softer and more intangible stuff, like

And there's a lot of concern about the fake evidence that's coming out and trying to put in what it's called guardrails and other ways of preventing that or maybe the system should check itself. You'd need sort of an overriding system, if you will, of the ego of the system that, before it puts out anything, it does a quick internet search to see if those are really correct statements. It's the same with arithmetic.

Computers are fantastic at doing arithmetic and calculus and they can, you know, we have all sorts of powerful calculators that will solve calculus equations and algebraic equations, but how come the GPTs are unbelievably bad at it? They can't even count. But the solution is pretty trivial. When it sees that they're doing something that requires mathematics, it should call on the operator. If you will, the specialized tool to do the mathematics. So, Hey, you know what? Here we go again.

Just like we have information appliances for people to do specialized things. Well, the AIs are going to have to have specialized appliances too so they could do specialized things like get the spelling right or get the mathematics right, or, you know, get the facts right about who really was the early presidents or prime minister or whatever. And that's happening. That's starting to happen. But it's really hard. I was about to say, it's really hard to predict the future.

And I was going to say, and, but there's a well known saying that says It's easy to predict the future. do it all the time. The difficulty is getting it right. And so, I hesitate to predict and friends who are in that business, they don't call it prediction, they call it forecasting. And what they try to do is think about all of the things that are happening today.

And all of the implications of them, and the many different directions in which this could cause behavior to go or societies to move, or the world to move. so that we're ready, no matter what happens, we tend to be ready for it. But we don't know which of these many ways will actually happen.

What impressed me about Sal Khan's book was that he was sort of a skeptic. But he really had this vision of trying to harness it for the improvement in education, and that's what he cares about. he and his team have been trying to develop tools that focus on that one topic. Now that one topic is a very difficult, complex topic with many, many different issues.

But by focusing on that and creating AIs that are particularly well trained in just in education and in the concepts that which could be anything. Could be language, could be history, could be humanities, could be science. That you can actually get more of the benefits and less of the negatives. And as I said before, learning how to transform the negatives into positives because they're good learning exercises.

One of my radiologist friends, when I asked him about that, and he's, considered one of the better radiologists in the country, uh, does it mean that it will do all the, you know, it will do all the diagnosis for you, what kind, what, you know. he said, thank goodness. Because he used to deal with patients. And now what he has to do is sit in the room all day looking at screens and trying to

I think it came under The broad heading of intelligent amplification. But you said, My, my theme is not anti technological, It's pro human.

But basically, you walked around all day doing nothing except looking for little issues that needed help. So you were the assistant to the machine instead of the other way around. But the result to society was that cloth was now much less expensive than ever before. And that created lots of new opportunities for people to make, use cloth in many, many ways that were too expensive to do before. And so, new jobs and new opportunities arose. And so, to society there was huge benefit.

To the people who lost their jobs, There was a huge negative what we're trying to do today Is we are it is absolutely the case that people will lose jobs will replace the kinds of jobs and the question is though as we find new opportunities because New things are done now that are faster better cheaper Which is therefore enable all sorts of new activities to do that were too expensive before too difficult and Can we make it so that a lot of the people who are displaced are able to be part of

this the new regime That make use of it that's that could be hard because sometimes requires different kinds of knowledge and skills and the people who are being displaced are too old or Didn't don't have the right plan or education to acquire them that's I think the challenge That we do want to make this be a collaborative effort And but we have to also think of the humane impact. But one thing that's in our favor, that these kinds of revolutions take decades to happen.

when we say it may replace jobs, it's not going to replace those jobs immediately. A lot of companies are trying to do that, but it's, they're just, fooling themselves. They're saying, our expenses are too high and we have now AI and so we can get rid of, you know, five, fifty percent of these people and they get rid of them. Guess what? There's nothing, the new AIs will not replace what they've lost. And so that, that's just bad, stupid thinking.

so they, If you feel that you are having trouble with your breasts, it tells you how to take a picture of your breasts and send them, and it does, then it does a quick analysis, and tries to say that's okay. it, you know, the problem is this, and it will go away, or you should treat it this, or we're not sure, but here's where you should go to your physician, and here's what you should tell your physician. In other words, it's doing triage.

And so what triage does is it takes the cases that we see are important. We send them to the experts, but cases that are not important, that could be solved much more, you know, simply at home we'll tell you how to do it at home and you have to make the system so that it, because it will make errors, but you know, those of us in the science know there are two kinds of errors you can falsely say something that isn't true, or you can miss things that are true.

And so you want to minimize the misses. And so you just have to be conservative in what you tell people. That if there's any doubt, you send them to the physician.

Or said, um, you ought to go to the emergency room. That would be really useful to know.

And so we can tell by how hard you're pushing. But we can tell how hard you're pushing by how it, how much the vibration is changing. And then, your finger is right on the camera, and so we also turn on the camera light, and it hits your skin and back into the camera, and therefore we can see your pulse. And therefore we can tell when the pulse stops, which means, okay, that's high, that's the high point of your blood pressure, and we can tell when it resumes again.

Okay. And they basically do a blood pressure test.

so Design designers where I'd like lots of people about the impacts of AI. What's your thoughts about how, Where AI

Ask yourself, hmm. What can I do with this that we couldn't do before? These are opportunities. And why aren't designers, instead of complaining about it, and saying it's all biased, and we have to get rid of this problem, or that problem, or whatever, well, those are correct. But, first of all, that's an opportunity for you to help solve those problems.

second of all, it's an opportunity for you to figure out exciting things I can do that I could never do before, that I could make into something valuable for people. And that's where I want to see designers leading the way. Right now we have technologists leading the way. Technologists who say, Oh, I have a technology. I, you know, I have a hammer. I'm running around looking for a nail to bang it into. I have a technology. I'm looking around somewhere.

No, no. What designers often start with is understanding people, issues in everyday life. And we find some pro, some issue that bothers people. People may not be complaining, by the way, because they think, well, that's the way your life is. It doesn't have to be that way. we're really good at spotting those things and then figuring out something that we can do that will help them. And AI is one of those things that we might be able to use to help in a lot of these different issues.

So think of it as a great opportunity.

You know, if you take a look at architecture, and for that matter the car design, the exterior body of a shell of a car, they used to be all straight edges, because that was the only tools we had that could bend metal, right, and make it in rectangular forms, or with straight edges and corners and so on. as new materials come out, and new methods of manufacturing come out, they can be any shape in the world that you think of.

And with additive printing, additive manufacturing methods, you can have things with, that are, you get rid of stuff, material that isn't used, you know, so you can have big holes inside the material, which makes things lighter, and doesn't lose the strength. And, well, yeah, so think about new things, and new tools, and new ways of doing things. And AI is the same way. Can it solve all problems? No. it can actually do things that, and I can't tell you what it's going to be.

I rely on your creativity to discover them, and you get me excited about it.

I'm sure. Just as we wrap up, do you have any just general reflections on great examples of human or humanity centered AI or any sort of principles or rules of thumb that you would like to leave the audience with?

But it still has already given me lots of ideas of things that we can do in other domains besides the ones that he covers. the other thing, and I've been having a big debate with with my friend Ben Schneiderman about this. I'm, I was an advisor to a company called L. E. Q. E L I and then the letter Q. It's an Israeli company that makes agents uh, assistants for people Who don't have any social, don't have any social interaction. may be they have mental problems. They may be old and ill.

Most of them tend to be old and ill. And they're often in retirement homes or sometimes in their own home, but all alone. This is a system that is meant to be to help you. To say, oh, you know, there's an exercise class in a half hour. You should really get up. Come on, get set up. It's really important that you go and do, go to this exercise class. Or or isn't it time you called your daughter? Here, I can call your daughter for you. Would you like to talk to your daughter?

And or it's time to take your pills. Or, you haven't had your meal yet. And you really need to eat something. Now, This can be really valuable for people who lack social contact. Can lack it for lots of reasons, including maybe they just have severe depression. And even though there are lots of people who are trying to help them and so on they turn them all down. sometimes they often interact with these machines in ways they never would with people. it can be very valuable.

And, and so the system is actually being accepted by lots of retirement centers and wellness centers around the world. And, but, you know, Ben Schneiderman thinks, no, this is all deficient. You can't have real empathy. You can't have a real relationship with a machine. I say, I don't even want to argue that point, Ben. I'm telling you, tell you, there are situations where there's no alternative and this is a better alternative than having nothing people really like that.

And actually, but as I was reading Khan's book about education, is saying the same thing. students who have problems, can't understand material in class, often are ashamed to talk about it to their parents, ashamed to talk about it to their teachers, or even to their friends, because they think, oh, I guess I'm stupid, and I can't understand it, because it's all confusing.

But they will talk to their, to a computer agent, who becomes, actually gets to know each, gets to know you after a while, and knows exactly what your issues are, or what you like, and so, When you can't understand this algebra problem, it says, Well, you're, you love to play football, or, which in Britain would be soccer. Um, well, think of it as a, suppose you're in the middle of the game, and this happens, and then that happens, and this happens. How do you know what you should do next?

And, uh, and use, and use it as a way of introducing the same arithmetic problem that you're having trouble with. um, and that's what these agents can do wonderfully. And it's, it's, it's not a religious issue and it's not a moral issue, is that, um, they're always available. So, sometimes you don't want to talk to a person, you're ashamed. Or sometimes you don't have anybody around when you need them the most, like in the middle of the night or in the weekend or whatever.

And so, I think there's a lot of good that's starting to come out of these agents. Right now, they're not very good. Uh, but the, once you start adding GPT and other similar things to them, and again, where they're trained not on the world's, you know, knowledge which is filled with biases and, you know, uh, but rather on carefully databases, I think wonderful things will happen. I mean,

And, but, uh, You know, if I publish in, in one of the scientific journals or academic journals, it might get read by, uh, a thousand or two thousand people. And wow, that's a, that's a large number of readers. And I publish in LinkedIn and it might get read by thousand, Thousand people.