(At first, it may seem that this requires a lot of programming. The irony, however, is that neural networks require no programming at all. The only thing that the neural network does is rewire itself, by changing the strength of certain pathways each time it makes a right decision. So programming is nothing; changing the network is everything.)
Science-fiction writers once envisioned that robots on Mars would be sophisticated humanoids, walking and moving just like us, with complex programming that gave them human intelligence. The opposite has happened. Today the grandchildren of this approach—like the Mars Curiosity rover—are now roaming over the surface of Mars. They are not programmed to walk like a human. Instead, they have the intelligence of a bug, but they do quite fine in this terrain. These Mars rovers have relatively little programming; instead, they learn as they bump into obstacles.
ARE ROBOTS CONSCIOUS?
Perhaps the clearest way to see why true robot automatons do not yet exist is to rank their level of consciousness. As we have seen in Chapter 2, we can rank consciousness in four levels. Level 0 consciousness describes thermostats and plants; that is, it involves a few feedback loops in a handful of simple parameters such as temperature or sunlight. Level I consciousness describes insects and reptiles, which are mobile and have a central nervous system; it involves creating a model of your world in relationship to a new parameter, space. Then we have Level II consciousness, which creates a model of the world in relationship to others of its kind, requiring emotions. Finally we have Level III consciousness, which describes humans, who incorporate time and self-awareness to simulate how things will evolve in the future and determine our own place in these models.
We can use this theory to rank the robots of today. The first generation of robots were at Level 0, since they were static, without wheels or treads. Today’s robots are at Level I, since they are mobile, but they are at a very low echelon because they have tremendous difficulty navigating in the real world. Their consciousness can be compared to that of a worm or slow insect. To fully produce Level I consciousness, scientists will have to create robots that can realistically duplicate the consciousness of insects and reptiles. Even insects have abilities that current robots do not have, such as rapidly finding hiding places, locating mates in the forest, recognizing and evading predators, or finding food and shelter.
As we mentioned earlier, we can numerically rank consciousness by the number of feedback loops at each level. Robots that can see, for example, may have several feedback loops because they have visual sensors that can detect shadows, edges, curves, geometric shapes, etc., in three-dimensional space. Similarly, robots that can hear require sensors that can detect frequency, intensity, stress, pauses, etc. The total number of these feedback loops may total ten or so (while an insect, because it can forage in the wild, find mates, locate shelter, etc., may have fifty or more feedback loops). A typical robot, therefore, may have Level I:10 consciousness.
Robots will have to be able to create a model of the world in relation to others if they are to enter Level II consciousness. As we mentioned before, Level II consciousness, to a first approximation, is computed by multiplying the number of members of its group times the number of emotions and gestures that are used to communicate between them. Robots would thus have a consciousness of Level II:0. But hopefully, the emotional robots being built in labs today may soon raise that number.
Current robots view humans as simply a collection of pixels moving on their TV sensors, but some AI researchers are beginning to create robots that can recognize emotions in our facial expressions and tone of voice. This is a first step toward robots’ realizing that humans are more than just random pixels, and that they have emotional states.
In the next few decades, robots will gradually rise in Level II consciousness, becoming as intelligent as a mouse, rat, rabbit, and then a cat. Perhaps late in this century, they will be as intelligent as a monkey, and will begin to create goals of their own.
Once robots have a working knowledge of common sense and the Theory of Mind, they will be able to run complex simulations into the future featuring themselves as the principal actors and thus enter Level III consciousness. They will leave the world of the present and enter the world of the future. This is many decades beyond the capability of any robot today. Running simulations of the future means that you have a firm grasp of the laws of nature, causality, and common sense, so that you can anticipate future events. It also means that you understand human intentions and motivations, so you can predict their future behavior as well.
The numerical value of Level III consciousness, as we mentioned, is calculated by the total number of causal links one can make in simulating the future in a variety of real-life situations, divided by the average value of a control group. Computers today are able to make limited simulations in a few parameters (e.g., the collision of two galaxies, the flow of air around an airplane, the shaking of buildings in an earthquake), but they are totally unprepared to simulate the future in complex, real-life situations, so their level of consciousness would be something like Level III:5.
As we can see, it may take many decades of hard work before we have a robot that can function normally in human society.
SPEED BUMPS ON THE WAY
So when might robots finally match and exceed humans in intelligence? No one knows, but there have been many predictions. Most of them rely on Moore’s law extending decades into the future. However, Moore’s law is not a law at all, and in fact it ultimately violates a fundamental physical law: the quantum theory.
As such, Moore’s law cannot last forever. In fact, we can already see it slowing down now. It might flatten out by the end of this or the next decade, and the consequences could be dire, especially for Silicon Valley.
The problem is simple. Right now, you can place hundreds of millions of silicon transistors on a chip the size of your fingernail, but there is a limit to how much you can cram onto these chips. Today the smallest layer of silicon in your Pentium chip is about twenty atoms in width, and by 2020 that layer might be five atoms across. But then Heisenberg’s uncertainty principle kicks in, and you wouldn’t be able to determine precisely where the electron is and it could “leak out” of the wire. (See the Appendix, where we discuss the quantum theory and the uncertainty principle in more detail.) The chip would short-circuit. In addition, it would generate enough heat to fry an egg on it. So leakage and heat will eventually doom Moore’s law, and a replacement will soon be necessary.
If packing transistors on flat chips is maxing out in computing power, Intel is making a multibillion-dollar bet that chips will rise into the third dimension. Time will tell if this gamble pays off (one major problem with 3-D chips is that the heat generated rises rapidly with the height of the chip).
Microsoft is looking into other options, such as expanding into 2-D with parallel processing. One possibility is to spread chips horizontally in a row. Then you break up a software problem into pieces, sort out each piece on a small chip, and reassemble it at the end. However, it may be a difficult process, and software grows at a much slower pace than the supercharged exponential rate we are accustomed to with Moore’s law.
These stopgap measures may add years to Moore’s law. But eventually, all this must pass, too: the quantum theory inevitably takes over. This means that physicists are experimenting with a wide variety of alternatives after the Age of Silicon draws to a close, such as quantum computers, molecular computers, nanocomputers, DNA computers, optical computers, etc. None of these technologies, however, is ready for prime time.
THE UNCANNY VALLEY
But assume for the moment that one day we will coexist with incredibly sophisticated robots, perhaps using chips with molecular transistors instead of silicon. How closely do we want our robots to look like us? Japan is the world’s leader in creating robots that resemble cuddly pets and children, but their designers are careful not to make their robots appear too human, which can be unnerving. This phenomenon
was first studied by Dr. Masahiro Mori in Japan in 1970, and is called the “uncanny valley.” It posits that robots look creepy if they look too much like humans. (The effect was actually first mentioned by Darwin in 1839 in The Voyage of the Beagle and again by Freud in 1919 in an essay titled “The Uncanny.”) Since then, it has been studied very carefully not just by AI researchers but also by animators, advertisers, and anyone promoting a product involving humanlike figures. For instance, in a review of the movie The Polar Express, a CNN writer noted, “Those human characters in the film come across as downright … well, creepy. So The Polar Express is at best disconcerting, and at worst, a wee bit horrifying.”
According to Dr. Mori, the more a robot looks like a human, the more we feel empathy toward it, but only up to a point. There is a dip in empathy as the robot approaches actual human appearance—hence the uncanny valley. If the robot looks very similar to us save for a few features that are “uncanny,” it creates a feeling of revulsion and fear. If the robot appears 100 percent human, indistinguishable from you and me, then we’ll register positive emotions again.
This has practical implications. For example, should robots smile? At first, it seems obvious that robots should smile to greet people and make them feel comfortable. Smiling is a universal gesture that signals warmth and welcome. But if the robot smile is too realistic, it makes people’s skin crawl. (For example, Halloween masks often feature fiendish-looking ghouls that are grinning.) So robots should smile only if they are childlike (i.e., with big eyes and a round face) or are perfectly human, and nothing in between. (When we force a smile, we activate facial muscles with our prefrontal cortex. But when we smile because we are in a good mood, our nerves are controlled by our limbic system, which activates a slightly different set of muscles. Our brains can tell the subtle difference between the two, which was beneficial for our evolution.)
This effect can also be studied using brain scans. Let’s say that a subject is placed into an MRI machine and is shown a picture of a robot that looks perfectly human, except that its bodily motions are slightly jerky and mechanical. The brain, whenever it sees anything, tries to predict that object’s motion into the future. So when looking at a robot that appears to be human, the brain predicts that it will move like a human. But when the robot moves like a machine, there is a mismatch, which makes us uncomfortable. In particular, the parietal lobe lights up (specifically, the part of the lobe where the motor cortex connects with the visual cortex). It is believed that mirror neurons exist in this area of the parietal lobe. This makes sense, because the visual cortex picks up the image of the humanlike robot, and its motions are predicted via the motor cortex and by mirror neurons. Finally, it is likely that the orbitofrontal cortex, located right behind the eyes, puts everything together and says, “Hmmm, something is not quite right.”
Hollywood filmmakers are aware of this effect. When spending millions on making a horror movie, they realize that the scariest scene is not when a gigantic blob or Frankenstein’s monster pounces out of the bushes. The scariest scene is when there is a perversion of the ordinary. Think of the movie The Exorcist. What scene made moviegoers vomit as they ran to escape the theater or faint right in their seats? Was it the scene when a demon appears? No. Theaters across the world erupted in shrill screams and loud sobs when Linda Blair turned her head completely around.
This effect can also be demonstrated in young monkeys. If you show them pictures of Dracula or Frankenstein, they simply laugh and rip the pictures apart. But what sends these young monkeys screaming in terror is a picture of a decapitated monkey. Once again, it is the perversion of the ordinary that elicits the greatest fear. (In Chapter 2, we mentioned that the space-time theory of consciousness explains the nature of humor, since the brain simulates the future of a joke, and then is surprised to hear the punch line. This also explains the nature of horror. The brain simulates the future of an ordinary, mundane event, but then is shocked when things suddenly become horribly perverted.)
For this reason, robots will continue to look somewhat childlike in appearance, even as they approach human intelligence. Only when robots can act realistically like humans will their designers make them look fully human.
SILICON CONSCIOUSNESS
As we’ve seen, human consciousness is an imperfect patchwork of different abilities developed over millions of years of evolution. Given information about their physical and social world, robots may be able to create simulations similar (or in some respects, even superior) to ours, but silicon consciousness might differ from ours in two key areas: emotions and goals.
Historically, AI researchers ignored the problem of emotions, considering it a secondary issue. The goal was to create a robot that was logical and rational, not scatterbrained and impulsive. Hence, the science fiction of the 1950s and ’60s stressed robots (and humanoids like Spock on Star Trek) that had perfect, logical brains.
We saw with the uncanny valley that robots will have to look a certain way if they’re to enter our homes, but some people argue that robots must also have emotions so that we can bond with, take care of, and interact productively with them. In other words, robots will need Level II consciousness. To accomplish this, robots will first have to recognize the full spectrum of human emotions. By analyzing subtle facial movements of the eyebrows, eyelids, lips, cheeks, etc., a robot will be able to identify the emotional state of a human, such as its owner. One institution that has excelled in creating robots that recognize and mimic emotion is the MIT Media Laboratory. I have had the pleasure of visiting the laboratory, outside Boston, on several occasions, and it is like visiting a toy factory for grown-ups. Everywhere you look, you see futuristic, high-tech devices designed to make our lives more interesting, enjoyable, and convenient.
As I looked around the room, I saw many of the high-tech graphics that eventually found their way into Hollywood movies like Minority Report and AI. As I wandered through this playground of the future, I came across two intriguing robots, Huggable and Nexi. Their creator, Dr. Cynthia Breazeal, explained to me that these robots have specific goals. Huggable is a cute teddy bear–like robot that can bond with children. It can identify the emotions of children; it has video cameras for eyes, a speaker for its mouth, and sensors in its skin (so it can tell when it is being tickled, poked, or hugged). Eventually, a robot like this might become a tutor, babysitter, nurse’s aide, or a playmate.
Nexi, on the other hand, can bond with adults. It looks a little like the Pillsbury Doughboy. It has a round, puffy, friendly face, with large eyes that can roll around. It has already been tested in a nursing home, and the elderly patients all loved it. Once the seniors got accustomed to Nexi, they would kiss it, talk to it, and miss it when it had to leave. (See Figure 12.)
Dr. Breazeal told me she designed Huggable and Nexi because she was not satisfied with earlier robots, which looked like tin cans full of wires, gears, and motors. In order to design a robot that could interact emotionally with people, she needed to figure out how she could get it to perform and bond like us. Plus, she wanted robots that weren’t stuck on a laboratory shelf but could venture out into the real world. The former director of MIT’s Media Lab, Dr. Frank Moss, says, “That is why Breazeal decided in 2004 that it was time to create a new generation of social robots that could live anywhere: homes, schools, hospitals, elder care facilities, and so on.”
At Waseda University in Japan, scientists are working on a robot that has upper-body motions representing emotions (fear, anger, surprise, joy, disgust, sadness) and can hear, smell, see, and touch. It has been programmed to carry out simple goals, such as satisfying its hunger for energy and avoiding dangerous situations. Their goal is to integrate the senses with the emotions, so that the robot acts appropriately in different situations.
Figure 12. Huggable (top) and Nexi (bottom), two robots built at the MIT Media Laboratory that were explicitly designed to interact with humans via emotions. (illustration credit 10.1)
(illustration c
redit 10.2)
Not to be outdone, the European Commission is funding an ongoing project, called Feelix Growing, which seeks to promote artificial intelligence in the UK, France, Switzerland, Greece, and Denmark.
EMOTIONAL ROBOTS
Meet Nao.
When he’s happy, he will stretch out his arms to greet you, wanting a big hug. When he’s sad, he turns his head downward and appears forlorn, with his shoulders hunched forward. When he’s scared, he cowers in fear, until someone pats him reassuringly on the head.
He’s just like a one-year-old boy, except that he’s a robot. Nao is about one and a half feet tall, and looks very much like some of the robots you see in a toy store, like the Tranformers, except he’s one of the most advanced emotional robots on earth. He was built by scientists at the UK’s University of Hertfordshire, whose research was funded by the European Union.
His creators have programmed him to show emotions like happiness, sadness, fear, excitement, and pride. While other robots have rudimentary facial and verbal gestures that communicate their emotions, Nao excels in body language, such as posture and gesture. Nao even dances.
Unlike other robots, which specialize in mastering just one area of the emotions, Nao has mastered a wide range of emotional responses. First, Nao locks onto visitors’ faces, identifies them, and remembers his previous interactions with each of them. Second, he begins to follow their movements. For example, he can follow their gaze and tell what they are looking at. Third, he begins to bond with them and learns to respond to their gestures. For example, if you smile at him, or pat him on his head, he knows that this is a positive sign. Because his brain has neural networks, he learns from interactions with humans. Fourth, Nao exhibits emotions in response to his interactions with people. (His emotional responses are all preprogrammed, like a tape recorder, but he decides which emotion to choose to fit the situation.) And lastly, the more Nao interacts with a human, the better he gets at understanding the moods of that person and the stronger the bond becomes.