Andra Keay examines why robotics keeps chasing humanoid forms despite practical limits, and connects the field's obsession with human-like machines to the same biases that keep women and people of color underrepresented in tech.
Chapters
Join me in welcoming Andrea Kaye to— Thank you. That's a big introduction, and we don't have a lot of time. So let's work backwards. First off, it's great to see so many women here because— yes, awesome— because the reality is usually that you will not, that you will walk into rooms, it's all men. Also, if you are of color, you're not represented terribly well either. And oddly enough, in spite of the fact that I believe California has the highest Latino Mexican population in the US, you will not see yourself evenly represented in the entrepreneur or in the investment communities. So at the moment, this is causing a lot of fuss. And people are saying, Silicon Valley's broken. It's about to get fixed. fixed, but I'm not holding my breath. OK. I think this is one of those little waves where the most obvious, egregious problems get shot down in flames, and everybody pats themselves on the back and says, see, we fixed it. Life is moving forward. Isn't everything wonderful? And you're here because you believe in creating a better future. But this is one of those cognitive biases that we fall into all the time, which oddly enough is what got me interested in robotics. Because we're humans and we're building imitation people. And they are completely flawed with all of our cognitive biases. And yet at the same time, robotics and AI provides an incredible mirror into our cognitive biases, into how we think, the way we think, what constructs thought. And we don't actually have a very good idea about what makes people think. So I think it's a little bit silly of us to think that we can create any sort of artificial intelligence of a general purpose. We're doing quite well with what we call weak or narrow AI, but that holy grail of artificial general intelligence, I figure if we don't know how we think, how can we build something that thinks better? Cognitive biases. Take one step forward. forward, the next step is going to be forward. Okay? This is something we see all the time, that techno-optimism. This is good, the next thing is going to be better, and we're going to keep moving in that direction. People warn you a lot that we don't naturally understand exponential thinking, but I think we don't necessarily understand setbacks and how they continually happen, and we have to be ready to be on the back foot. And moving forward. And so this comes back to women in technology. We look at things like the overall numbers. And one of the reasons there's a fuss at the moment is people have finally twigged that 30 years ago there were more women in technology than there are now. Because a whole generation thought, well, we've improved things. In the '70s, people got a lot more women in STEM and sciences. Things would just keep getting better. But it didn't. So a lot of people started to say, well, hold on, what happened? We went into university. And here's the thing. We put a lot of women into the STEM pipeline. But they don't stay. So I think tech is a bit of the problem. The tech industry. Tech investment. Tech careers. So everybody here. And I say that, okay, women in tech is not a woman's problem, okay? And yet we treat it that way. It's a tech problem, okay? It's a tech problem because tech companies can't hire. There are not enough people out there to do all of the fantastic jobs building great products. So why are we ignoring so many large chunks of the population? I mean, straight there you have Self-motivation for everybody to make women in tech a problem for everybody in tech. And for all of you, your peer group is going to be a significant step forward for you. So it helps if you support everybody in your peer group. And I think you have to doubly support people that are not usually represented. Okay? And that's kind of working backwards to why I do robotics. And I'm going to play mystery box with this presentation. I don't really know what I've got on this one. But we have a fascination with humanoid robots that goes back thousands of years. And we have been building humanoid constructs, statues, where the story is the statue comes to life, or even mechanical systems. Back to the early Greek engineers who were building steam and water-powered systems to make moving statues. Some of them were used for agriculture, some of them were used for religion. We had Chinese tea ceremony robots. We had people building walking carriage robots. Back in 1491, Leonardo da Vinci built the Mechanical Knight. So robots, it's not new. It's something we've wanted for a very, very long time. How close are we? Well, World's Fair back in the '30s, Westinghouse built a humanoid robot and dog called Sparky. Sparky. And Electro, this guy, was a domestic appliance robot. I think it's kind of ironic because built by Westinghouse, this appliance robot smoked, drank, and sang. Not much help around the house. But a great attraction at the World's Fair. I believe Sparky was actually run over by a car when he escaped. But you couldn't write that story. This is a selection of the headlines right now. So what has changed? You know, we've been fascinated about robots for hundreds of years. We've been building them since the '30s. In fact, even before that, Nikola Tesla was building robots. And he said— maybe I've even got a slide of his somewhere in here, maybe not— but he said that Hey, building robots, it's a done deal. I've done it. It's simple. We just need to push the technology a little bit harder. And I foresee that by the 21st century, the world will be full of robots. And they will free mankind to pursue higher aspirations. And this is the techno-optimist view of the future and how if you build one robot, then everybody's just going to keep building more and it's going to get better and better. Well, we hit roadblocks. So the robotics—
