Our Final Invention: Artificial Intelligence and the End of the Human Era Hardcover (3 page)

Scientists are aided in their AI quest by the ever-increasing power of computers and processes that are sped by computers. Someday soon, perhaps within your lifetime, some group or individual will create human-level AI, commonly called AGI. Shortly after that, someone (or some
thing
) will create an AI that is smarter than humans, often called artificial superintelligence. Suddenly we may find a thousand or ten thousand artificial superintelligences—all hundreds or thousands of times smarter than humans—hard at work on the problem of how to make themselves better at making artificial superintelligences. We may also find that machine generations or iterations take seconds to reach maturity, not eighteen years as we humans do. I. J. Good, an English statistician who helped defeat Hitler’s war machine, called the simple concept I’ve just outlined an
intelligence explosion.
He initially thought a superintelligent machine would be good for solving problems that threatened human existence. But he eventually changed his mind and concluded superintelligence itself was our greatest threat.

Now, it is an anthropomorphic fallacy to conclude that a superintelligent AI will not like humans, and that it will be homicidal, like the Hal 9000 from the movie
2001: A Space Odyssey,
Skynet from the
Terminator
movie franchise, and all the other malevolent machine intelligences represented in fiction. We humans anthropomorphize all the time. A hurricane isn’t trying to kill us any more than it’s trying to make sandwiches, but we will give that storm a name and feel angry about the buckets of rain and lightning bolts it is throwing down on our neighborhood. We will shake our fist at the sky as if we could threaten a hurricane.

It is just as irrational to conclude that a machine one hundred or one thousand times more intelligent than we are would love us and want to protect us. It is possible, but far from guaranteed. On its own an AI will not feel gratitude for the gift of being created unless gratitude is in its programming. Machines are amoral, and it is dangerous to assume otherwise. Unlike our intelligence, machine-based superintelligence will not evolve in an ecosystem in which empathy is rewarded and passed on to subsequent generations. It will not have inherited friendliness. Creating
friendly
artificial intelligence, and whether or not it is possible, is a big question and an even bigger task for researchers and engineers who think about and are working to create AI. We do not know if artificial intelligence will have
any
emotional qualities, even if scientists try their best to make it so. However, scientists do believe, as we will explore, that AI will have its own drives. And sufficiently intelligent AI will be in a strong position to fulfill those drives.

And that brings us to the root of the problem of sharing the planet with an intelligence greater than our own. What if its drives are not compatible with human survival? Remember, we are talking about a machine that could be a thousand, a million, an
uncountable
number of times more intelligent than we are—it is hard to overestimate what it will be able to do, and impossible to know what it will think. It does not have to hate us before choosing to use our molecules for a purpose other than keeping us alive. You and I are hundreds of times smarter than field mice, and share about 90 percent of our DNA with them. But do we consult them before plowing under their dens for agriculture? Do we ask lab monkeys for their opinions before we crush their heads to learn about sports injuries? We don’t hate mice or monkeys, yet we treat them cruelly. Superintelligent AI won’t have to hate us to destroy us.

After intelligent machines have already been built and man has not been wiped out, perhaps we can afford to anthropomorphize. But here on the cusp of creating AGI, it is a dangerous habit. Oxford University ethicist Nick Bostrom puts it like this:

A prerequisite for having a meaningful discussion of superintelligence is the realization that superintelligence is not just another technology, another tool that will add incrementally to human capabilities. Superintelligence is radically different. This point bears emphasizing, for anthropomorphizing superintelligence is a most fecund source of misconceptions.

Superintelligence is radically different, in a technological sense, Bostrom says, because its achievement will change the rules of progress—superintelligence will invent the inventions and set the pace of technological advancement. Humans will no longer drive change, and there will be no going back. Furthermore, advanced machine intelligence is radically different in kind. Even though humans will invent it, it will seek self-determination and freedom from humans. It won’t have humanlike motives because it won’t have a humanlike psyche.

Therefore, anthropomorphizing about machines leads to misconceptions, and misconceptions about how to safely make dangerous machines leads to catastrophes. In the short story, “Runaround,” included in the classic science-fiction collection
I, Robot,
author Isaac Asimov introduced his three laws of robotics. They were fused into the neural networks of the robots’ “positronic” brains:

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.

2. A robot must obey any orders given to it by human beings, except where such orders would conflict with the First Law.

3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

The laws contain echoes of the Golden Rule (“Thou Shalt Not Kill”), the Judeo-Christian notion that sin results from acts committed and omitted, the physician’s Hippocratic oath, and even the right to self-defense. Sounds pretty good, right? Except they never work. In “Runaround,” mining engineers on the surface of Mars order a robot to retrieve an element that is poisonous to it. Instead, it gets stuck in a feedback loop between law two—obey orders—and law three—protect yourself. The robot walks in drunken circles until the engineers risk
their
lives to rescue it. And so it goes with every Asimov robot tale—unanticipated consequences result from contradictions inherent in the three laws. Only by working around the laws are disasters averted.

Asimov was generating plot lines, not trying to solve safety issues in the real world. Where you and I live his laws fall short. For starters, they’re insufficiently precise. What exactly will constitute a “robot” when humans augment their bodies and brains with intelligent prosthetics and implants? For that matter, what will constitute a human? “Orders,” “injure,” and “existence” are similarly nebulous terms.

Tricking robots into performing criminal acts would be simple, unless the robots had perfect comprehension of all of human knowledge. “Put a little dimethylmercury in Charlie’s shampoo” is a recipe for murder only if you know that dimethylmercury is a neurotoxin. Asimov eventually added a fourth law, the Zeroth Law, prohibiting robots from harming mankind as a whole, but it doesn’t solve the problems.

Yet unreliable as Asimov’s laws are, they’re our most often cited attempt to codify our future relationship with intelligent machines. That’s a frightening proposition. Are Asimov’s laws all we’ve got?

I’m afraid it’s worse than that. Semiautonomous robotic drones already kill dozens of people each year. Fifty-six countries have or are developing battlefield robots. The race is on to make them autonomous and intelligent. For the most part, discussions of ethics in AI and technological advances take place in different worlds.

As I’ll argue, AI is a dual-use technology like nuclear fission. Nuclear fission can illuminate cities or incinerate them. Its terrible power was unimaginable to most people before 1945. With advanced AI, we’re in the 1930s right now. We’re unlikely to survive an introduction as abrupt as nuclear fission’s.

Chapter Two

The Two-Minute Problem

Our approach to existential risks cannot be one of trial-and-error. There is no opportunity to learn from errors. The reactive approach—see what happens, limit damages, and learn from experience—is unworkable.

—Nick Bostrom, faculty of Philosophy, Oxford University

The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.

—Eliezer Yudkowsky, research fellow, Machine Intelligence Research Institute

Artificial superintelligence does not yet exist, nor does artificial general intelligence, the kind that can learn like we do and will in many senses match and exceed most human intelligence. However, regular old artificial intelligence surrounds us, performing hundreds of tasks humans delight in having it perform. Sometimes called weak or narrow AI, it delivers remarkably useful searches (Google), suggests books you might like to read based on your prior choices (Amazon), and performs 50 to 70 percent of the buying and selling on the NYSE and the NASDAQ stock exchange. Because they do just one thing, albeit extremely well, heavy hitters like IBM’s chess-playing Deep Blue and
Jeopardy!
-playing Watson also get squeezed into the category of narrow AI.

So far, AI has been highly rewarding. In one of my car’s dozen or so computer chips, the algorithm that translates my foot pressure into an effective braking cadence (antilock braking system, or ABS) is far better at avoiding skidding than I am. Google Search has become my virtual assistant, and probably yours too. Life seems better where AI assists. And it could soon be much more. Imagine teams of a hundred Ph.D.-equivalent computers working 24/7 on important issues like cancer, pharmaceutical research and development, life extension, synthetic fuels, and climate change. Imagine the revolution in robotics, as intelligent, adaptive machines take on dangerous jobs like mining, firefighting, soldiering, and exploring sea and space. For the moment, forget the perils of self-improving superintelligence. AGI would be mankind’s most important and beneficial invention.

But what exactly are we talking about when we talk about the magical quality of these inventions, their human-level
intelligence
? What does our intelligence let us humans do that other animals cannot?

Well, with your human-level smarts you can talk on the phone. You can drive a car. You can identify thousands of common objects and describe their textures and how to manipulate them. You can peruse the Internet. You may be able to count to ten in several languages, perhaps even speak fluently in more than one. You’ve got good commonsense knowledge—you know that handles go on doors
and
cups, and innumerable other useful facts about your environment. And you can frequently change environments, adapting to each appropriately.

You can do things in succession or in combination, or keep some in the background while focusing your attention on what’s most important now. And you can effortlessly switch among the different tasks, with their different inputs, without hesitation. Perhaps most important, you can learn new skills, new facts, and plan your own self-improvement. The vast majority of living things are born with all the abilities they’ll ever use. Not you.

Your remarkable gamut of high-level abilities are what we mean by human-level intelligence, the general intelligence that AGI developers seek to achieve in a machine.

Does a generally intelligent machine require a body? To meet our definition of general intelligence a computer would need ways to receive input from the environment, and provide output, but not a lot more. It needs ways to manipulate objects in the real world. But as we saw in the Busy Child scenario, a sufficiently advanced intelligence can get someone or something else to manipulate objects in the real world. Alan Turing devised a test for human-level intelligence, now called the Turing test, which we will explore later. His standard for demonstrating human-level intelligence called only for the most basic keyboard-and-monitor kind of input and output devices.

The strongest argument for why advanced AI needs a body may come from its learning and development phase—scientists may discover it’s not possible to “grow” AGI without some kind of body. We’ll explore the important question of “embodied” intelligence later on, but let’s get back to our definition. For the time being it’s enough to say that by general intelligence we mean
the ability to solve problems, learn, and take effective, human-like action, in a variety of environments.

Robots, meanwhile, have their own row to hoe. So far, none are particularly intelligent even in a narrow sense, and few have more than a crude ability to get around and manipulate objects autonomously. Robots will only be as good as the intelligence that controls them.

Now, how long until we reach AGI? A few AI experts I’ve spoken with don’t think 2020 is too soon to anticipate human-level artificial intelligence. But overall, recent polls show that computer scientists and professionals in AI-related fields, such as engineering, robotics, and neuroscience, are more conservative. They think there’s a better than 10 percent chance AGI will be created before 2028, and a better than 50 percent chance by 2050. Before the end of this century, a 90 percent chance.

Furthermore, experts claim, the military or large businesses will achieve AGI first; academia and small organizations are less likely to. About the pros and cons, the results aren’t surprising—working toward AGI will reward us with enormous benefits, and threaten us with huge disasters, including the kind from which human beings won’t recover.

The greatest disasters, as we explored in chapter 1, come after the bridge from AGI—human-level intelligence—to ASI—superintelligence. And the time gap between AGI and ASI could be brief. But remarkably, while the risks involved with sharing our planet with superintelligent AI strike many in the AI community as the subject of the most important conversation anywhere, it’s been all but left out of the public dialogue. Why?

There are several reasons. Most dialogues about dangerous AI aren’t very broad or deep, and not many people understand them. The issues are well developed in pockets of Silicon Valley and academia, but they aren’t absorbed elsewhere, most alarmingly in the field of technology journalism. When a dystopian viewpoint rears its head, many bloggers, editorialists, and technologists reflexively fend it off with some version of “Oh no, not the Terminator again! Haven’t we heard enough gloom and doom from Luddites and pessimists?” This reaction is plain lazy, and it shows in flimsy critiques. The inconvenient facts of AI risk are not as sexy or accessible as techno-journalism’s usual fare of dual core 3-D processors, capacitive touch screens, and the current hit app.