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“It depends on what you mean by artificial intelligence.”

Computers are flexible enough to model the strange evolved convolutions of our thought, and yet responsive only to precise instructions. So if the endeavor succeeds, it will be a double victory: we will finally come to know the exact mechanics of our selves—and we’ll have made intelligent machines.

Ever since he was about 14, when he found out that his youngest sister, Molly, couldn’t understand language, because she “had something deeply wrong with her brain” (her neurological condition probably dated from birth, and was never diagnosed), he had been quietly obsessed by the relation of mind to matter.

How could consciousness be physical? How could a few pounds of gray gelatin give rise to our very thoughts and selves?

Consciousness, Hofstadter wanted to say, emerged via just the same kind of “level-crossing feedback loop.”

In 1931, the Austrian-born logician Kurt Gödel had famously shown how a mathematical system could make statements not just about numbers but about the system itself.

But then AI changed, and Hofstadter didn’t change with it, and for that he all but disappeared.

By the early 1980s, the pressure was great enough that AI, which had begun as an endeavor to answer yes to Alan Turing’s famous question, “Can machines think?,” started to mature—or mutate, depending on your point of view—into a subfield of software engineering, driven by applications.

Take Deep Blue, the IBM supercomputer that bested the chess grandmaster Garry Kasparov. Deep Blue won by brute force.

Hofstadter wanted to ask: Why conquer a task if there’s no insight to be had from the victory? “Okay,” he says, “Deep Blue plays very good chess—so what? Does that tell you something about how we play chess? No. Does it tell you about how Kasparov envisions, understands a chessboard?”

AI started working when it ditched humans as a model, because it ditched them. That’s the thrust of the analogy: Airplanes don’t flap their wings; why should computers think?

It’s a compelling point. But it loses some bite when you consider what we want: a Google that knows, in the way a human would know, what you really mean when you search for something

Cognition is recognition,” he likes to say. He describes “seeing as” as the essential cognitive act: you see some lines a

How do you make a search engine that understands if you don’t know how you understand?

s “an A,” you see a hunk of wood as “a table,” you see a meeting as “an emperor-has-no-clothes situation” and a friend’s pouting as “sour grapes”

That’s what it means to understand. But how does understanding work?

analogy is “the fuel and fire of thinking,” the bread and butter of our daily mental lives.

there’s an analogy, a mental leap so stunningly complex that it’s a computational miracle: somehow your brain is able to strip any remark of the irrelevant surface details and extract its gist, its “skeletal essence,” and retrieve, from your own repertoire of ideas and experiences, the story or remark that best relates.

in Hofstadter’s telling, the story goes like this: when everybody else in AI started building products, he and his team, as his friend, the philosopher Daniel Dennett, wrote, “patiently, systematically, brilliantly,” way out of the light of day, chipped away at the real problem. “Very few people are interested in how human intelligence works,”

For more than 30 years, Hofstadter has worked as a professor at Indiana University at Bloomington

The quick unconscious chaos of a mind can be slowed down on the computer, or rewound, paused, even edited

project out of IBM called Candide. The idea behind Candide, a machine-translation system, was to start by admitting that the rules-based approach requires too deep an understanding of how language is produced; how semantics, syntax, and morphology work; and how words commingle in sentences and combine into paragraphs—to say nothing of understanding the ideas for which those words are merely conduits.

, Hofstadter directs the Fluid Analogies Research Group, affectionately known as FARG.

Parts of a program can be selectively isolated to see how it functions without them; parameters can be changed to see how performance improves or degrades. When the computer surprises you—whether by being especially creative or especially dim-witted—you can see exactly why.

When you read Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, which describes in detail this architecture and the logic and mechanics of the programs that use it, you wonder whether maybe Hofstadter got famous for the wrong book.

ut very few people, even admirers of GEB, know about the book or the programs it describes. And maybe that’s because FARG’s programs are almost ostentatiously impractical. Because they operate in tiny, seemingly childish “microdomains.” Because there is no task they perform better than a human.

“The entire effort of artificial intelligence is essentially a fight against computers’ rigidity.”

“Nobody is a very reliable guide concerning activities in their mind that are, by definition, subconscious,” he once wrote. “This is what makes vast collections of errors so important. In an isolated error, the mechanisms involved yield only slight traces of themselves; however, in a large collection, vast numbers of such slight traces exist, collectively adding up to strong evidence for (and against) particular mechanisms.

So IBM threw that approach out the window. What the developers did instead was brilliant, but so straightforward,

The technique is called “machine learning.” The goal is to make a device that takes an English sentence as input and spits out a French sentence

What you do is feed the machine English sentences whose French translations you already know. (Candide, for example, used 2.2 million pairs of sentences, mostly from the bilingual proceedings of Canadian parliamentary debates.)

By repeating this process with millions of pairs of sentences, you will gradually calibrate your machine, to the point where you’ll be able to enter a sentence whose translation you don’t know and get a reasonable resul

Google Translate team can be made up of people who don’t speak most of the languages their application translates. “It’s a bang-for-your-buck argument,” Estelle says. “You probably want to hire more engineers instead” of native speakers.

But the need to serve 1 billion customers has a way of forcing the company to trade understanding for expediency. You don’t have to push Google Translate very far to see the compromises its developers have made for coverage, and speed, and ease of engineering. Although Google Translate captures, in its way, the products of human intelligence, it isn’t intelligent itself.

“Did we sit down when we built Watson and try to model human cognition?” Dave Ferrucci, who led the Watson team at IBM, pauses for emphasis. “Absolutely not. We just tried to create a machine that could win at Jeopardy.”

For Ferrucci, the definition of intelligence is simple: it’s what a program can do. Deep Blue was intelligent because it could beat Garry Kasparov at chess. Watson was intelligent because it could beat Ken Jennings at Jeopardy.

“There’s a limited number of things you can do as an individual, and I think when you dedicate your life to something, you’ve got to ask yourself the question: To what end? And I think at some point I asked myself that question, and what it came out to was, I’m fascinated by how the human mind works, it would be fantastic to understand cognition, I love to read books on it, I love to get a grip on it”—he called Hofstadter’s work inspiring—“but where am I going to go with it? Really what I want to do is build computer systems that do something.

Peter Norvig, one of Google’s directors of research, echoes Ferrucci almost exactly. “I thought he was tackling a really hard problem,” he told me about Hofstadter’s work. “And I guess I wanted to do an easier problem.”

Of course, the folly of being above the fray is that you’re also not a part of it

As our machines get faster and ingest more data, we allow ourselves to be dumber. Instead of wrestling with our hardest problems in earnest, we can just plug in billions of examples of them.

Hofstadter hasn’t been to an artificial-intelligence conference in 30 years. “There’s no communication between me and these people,” he says of his AI peers. “None. Zero. I don’t want to talk to colleagues that I find very, very intransigent and hard to convince of anything

Everything from plate tectonics to evolution—all those ideas, someone had to fight for them, because people didn’t agree with those ideas.

Academia is not an environment where you just sit in your bath and have ideas and expect everyone to run around getting excited. It’s possible that in 50 years’ time we’ll say, ‘We really should have listened more to Doug Hofstadter.’ But it’s incumbent on every scientist to at least think about what is needed to get people to understand the ideas.”

designer of the test-taking program, noted that even a simple task for children, like understanding the meaning of an arrow in the context of a test diagram, was not yet something the most advanced A.I. programs could do reliably.

scientific workshops intended to develop more accurate methods than the Turing test for measuring the capabilities of artificial intelligence programs.

Researchers in the field are now developing a wide range of gauges to measure intelligence — including the Allen Institute’s standardized-test approach and a task that Dr. Marcus proposed, which he called the “Ikea construction challenge.” That test would provide an A.I. program with a bag of parts and an instruction sheet and require it to assemble a piece of furniture.

First proposed in 2011 by Hector Levesque, a University of Toronto computer scientist, the Winograd Schema Challenge would pose questions that require real-world logic to A.I. programs. A question might be: “The trophy would not fit in the brown suitcase because it was too big. What was too big, A: the trophy or B: the suitcase?” Answering this question would require a program to reason spatially and have specific knowledge about the size of objects.

Within the A.I. community, discussions about software programs that can reason in a humanlike way are significant because recent progress in the field has been more focused on improving perception, not reasoning.

GeoSolver, or GeoS, was described at the Conference on Empirical Methods on Natural Language Processing in Lisbon this weekend. It operates by separately generating a series of logical equations, which serve as components of possible answers, from the text and the diagram in the question. It then weighs the accuracy of the equations and tries to discern whether its interpretation of the diagram and text is strong enough to select one of the multiple-choice answers.

Ultimately, Dr. Marcus said, he believed that progress in artificial intelligence would require multiple tests, just as multiple tests are used to assess human performance.

“There is no one measure of human intelligence,” he said. “Why should there be just one A.I. test?”

In the 1960s, Hubert Dreyfus, a philosophy professor at the University of California, Berkeley, expressed this skepticism most clearly when he wrote, “Believing that writing these types of programs will bring us closer to real artificial intelligence is like believing that someone climbing a tree is making progress toward reaching the moon.”

Industry leaders believe the new A.I. systems could be as important as the introduction of the web browser in the early 1990s and could lead to breakthroughs in areas ranging from drug research to education.

But gnawing at many industry insiders is a fear that they are releasing something dangerous into the wild. Generative A.I. can already be a tool for misinformation. Soon, it could be a risk to jobs. Somewhere down the line, tech’s biggest worriers say, it could be a risk to humanity.

“It is hard to see how you can prevent the bad actors from using it for bad things,” Dr. Hinton said.

After the San Francisco start-up OpenAI released a new version of ChatGPT in March, more than 1,000 technology leaders and researchers signed an open letter calling for a six-month moratorium on the development of new systems because A.I technologies pose “profound risks to society and humanity.

Several days later, 19 current and former leaders of the Association for the Advancement of Artificial Intelligence, a 40-year-old academic society, released their own letter warning of the risks of A.I. That group included Eric Horvitz, chief scientific officer at Microsoft, which has deployed OpenAI’s technology across a wide range of products, including its Bing search engine.

Dr. Hinton, often called “the Godfather of A.I.,” did not sign either of those letters and said he did not want to publicly criticize Google or other companies until he had quit his job

Dr. Hinton, a 75-year-old British expatriate, is a lifelong academic whose career was driven by his personal convictions about the development and use of A.I. In 1972, as a graduate student at the University of Edinburgh, Dr. Hinton embraced an idea called a neural network. A neural network is a mathematical system that learns skills by analyzing data. At the time, few researchers believed in the idea. But it became his life’s work.

Dr. Hinton is deeply opposed to the use of artificial intelligence on the battlefield — what he calls “robot soldiers.”

Around the same time, Google, OpenAI and other companies began building neural networks that learned from huge amounts of digital text. Dr. Hinton thought it was a powerful way for machines to understand and generate language, but it was inferior to the way humans handled language.

In 2018, Dr. Hinton and two other longtime collaborators received the Turing Award, often called “the Nobel Prize of computing,” for their work on neural networks.

In 2012, Dr. Hinton and two of his students in Toronto, Ilya Sutskever and Alex Krishevsky, built a neural network that could analyze thousands of photos and teach itself to identify common objects, such as flowers, dogs and cars.

Then, last year, as Google and OpenAI built systems using much larger amounts of data, his view changed. He still believed the systems were inferior to the human brain in some ways but he thought they were eclipsing human intelligence in others.

“Maybe what is going on in these systems,” he said, “is actually a lot better than what is going on in the brain.”

As companies improve their A.I. systems, he believes, they become increasingly dangerous. “Look at how it was five years ago and how it is now,” he said of A.I. technology. “Take the difference and propagate it forwards. That’s scary.”

Until last year, he said, Google acted as a “proper steward” for the technology, careful not to release something that might cause harm. But now that Microsoft has augmented its Bing search engine with a chatbot — challenging Google’s core business — Google is racing to deploy the same kind of technology. The tech giants are locked in a competition that might be impossible to stop, Dr. Hinton said.

His immediate concern is that the internet will be flooded with false photos, videos and text, and the average person will “not be able to know what is true anymore.”

He is also worried that A.I. technologies will in time upend the job market. Today, chatbots like ChatGPT tend to complement human workers, but they could replace paralegals, personal assistants, translators and others who handle rote tasks. “It takes away the drudge work,” he said. “It might take away more than that.”

Down the road, he is worried that future versions of the technology pose a threat to humanity because they often learn unexpected behavior from the vast amounts of data they analyze. This becomes an issue, he said, as individuals and companies allow A.I. systems not only to generate their own computer code but actually run that code on their ow

And he fears a day when truly autonomous weapons — those killer robots — become reality.

“The idea that this stuff could actually get smarter than people — a few people believed that,” he said. “But most people thought it was way off. And I thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.”

Many other experts, including many of his students and colleagues, say this threat is hypothetical. But Dr. Hinton believes that the race between Google and Microsoft and others will escalate into a global race that will not stop without some sort of global regulation.

But that may be impossible, he said. Unlike with nuclear weapons, he said, there is no way of knowing whether companies or countries are working on the technology in secret. The best hope is for the world’s leading scientists to collaborate on ways of controlling the technology. “I don’t think they should scale this up more until they have understood whether they can control it,” he said.

Dr. Hinton said that when people used to ask him how he could work on technology that was potentially dangerous, he would paraphrase Robert Oppenheimer, who led the U.S. effort to build the atomic bomb: “When you see something that is technically sweet, you go ahead and do it.”

He does not say that anymore.

For constrained domains the GOFAI approach is a winning strategy.

there is nothing intelligent or even interesting about the brute force approach.

the dominant paradigm in AI research has largely “moved on from GOFAI to embodied, distributed intelligence.” And Faustus from Cincinnati insists that as a result “machines with bodies that experience the world and act on it” will be “able to achieve intelligence.”

The new, embodied paradigm in AI, deriving primarily from the work of roboticist Rodney Brooks, insists that the body is required for intelligence. Indeed, Brooks’s classic 1990 paper, “Elephants Don’t Play Chess,” rejected the very symbolic computation paradigm against which Dreyfus had railed, favoring instead a range of biologically inspired robots that could solve apparently simple, but actually quite complicated, problems like locomotion, grasping, navigation through physical environments and so on. To solve these problems, Brooks discovered that it was actually a disadvantage for the system to represent the status of the environment and respond to it on the basis of pre-programmed rules about what to do, as the traditional GOFAI systems had. Instead, Brooks insisted, “It is better to use the world as its own model.”

although they respond to the physical world rather well, they tend to be oblivious to the global, social moods in which we find ourselves embedded essentially from birth, and in virtue of which things matter to us in the first place.

the embodied AI paradigm is irrelevant to Watson. After all, Watson has no useful bodily interaction with the world at all.

The statistical machine learning strategies that it uses are indeed a big advance over traditional GOFAI techniques. But they still fall far short of what human beings do.

“The illusion is that this computer is doing the same thing that a very good ‘Jeopardy!’ player would do. It’s not. It’s doing something sort of different that looks the same on the surface. And every so often you see the cracks.”

Watson doesn’t understand relevance at all. It only measures statistical frequencies. Because it is relatively common to find mismatches of this sort, Watson learns to weigh them as only mild evidence against the answer. But the human just doesn’t do it that way. The human being sees immediately that the mismatch is irrelevant for the Erie Canal but essential for Toronto. Past frequency is simply no guide to relevance.

The fact is, things are relevant for human beings because at root we are beings for whom things matter. Relevance and mattering are two sides of the same coin. As Haugeland said, “The problem with computers is that they just don’t give a damn.” It is easy to pretend that computers can care about something if we focus on relatively narrow domains — like trivia games or chess — where by definition winning the game is the only thing that could matter, and the computer is programmed to win. But precisely because the criteria for success are so narrowly defined in these cases, they have nothing to do with what human beings are when they are at their best.

Far from being the paradigm of intelligence, therefore, mere matching with no sense of mattering or relevance is barely any kind of intelligence at all. As beings for whom the world already matters, our central human ability is to be able to see what matters when.

But, as we show in our recent book, this is an existential achievement orders of magnitude more amazing and wonderful than any statistical treatment of bare facts could ever be. The greatest danger of Watson’s victory is not that it proves machines could be better versions of us, but that it tempts us to misunderstand ourselves as poorer versions of them.

The single existential risk that Tegmark worries about most is unfriendly artificial intelligence. That is, when computers are able to start improving themselves, there will be a rapid increase in their capacities, and then, Tegmark says, it’s very difficult to predict what will happen.

Tegmark told Lex Berko at Motherboard earlier this year, "I would guess there’s about a 60 percent chance that I’m not going to die of old age, but from some kind of human-caused calamity. Which would suggest that I should spend a significant portion of my time actually worrying about this. We should in society, too."

"Longer term—and this might mean 10 years, it might mean 50 or 100 years, depending on who you ask—when computers can do everything we can do," Tegmark said, “after that they will probably very rapidly get vastly better than us at everything, and we’ll face this question we talked about in the Huffington Post article: whether there’s really a place for us after that, or not.”

"This is very near-term stuff. Anyone who’s thinking about what their kids should study in high school or college should care a lot about this.”

Tegmark and his op-ed co-author Frank Wilczek, the Nobel laureate, draw examples of cold-war automated systems that assessed threats and resulted in false alarms and near misses. “In those instances some human intervened at the last moment and saved us from horrible consequences,” Wilczek told me earlier that day. “That might not happen in the future.”

there are still enough nuclear weapons in existence to incinerate all of Earth’s dense population centers, but that wouldn't kill everyone immediately. The smoldering cities would send sun-blocking soot into the stratosphere that would trigger a crop-killing climate shift, and that’s what would kill us all

“We are very reckless with this planet, with civilization,” Tegmark said. “We basically play Russian roulette.” The key is to think more long term, “not just about the next election cycle or the next Justin Bieber album.”

“There are several issues that arise, ranging from climate change to artificial intelligence to biological warfare to asteroids that might collide with the earth,” Wilczek said of the group’s launch. “They are very serious risks that don’t get much attention.

a widely perceived issue is when intelligent entities start to take on a life of their own. They revolutionized the way we understand chess, for instance. That’s pretty harmless. But one can imagine if they revolutionized the way we think about warfare or finance, either those entities themselves or the people that control them. It could pose some disquieting perturbations on the rest of our lives.”

Wilczek’s particularly concerned about a subset of artificial intelligence: drone warriors. “Not necessarily robots,” Wilczek told me, “although robot warriors could be a big issue, too. It could just be superintelligence that’s in a cloud. It doesn’t have to be embodied in the usual sense.”

it’s important not to anthropomorphize artificial intelligence. It's best to think of it as a primordial force of nature—strong and indifferent. In the case of chess, an A.I. models chess moves, predicts outcomes, and moves accordingly. If winning at chess meant destroying humanity, it might do that.

Even if programmers tried to program an A.I. to be benevolent, it could destroy us inadvertently. Andersen’s example in Aeon is that an A.I. designed to try and maximize human happiness might think that flooding your bloodstream with heroin is the best way to do that.

“It’s not clear how big the storm will be, or how long it’s going to take to get here. I don’t know. It might be 10 years before there’s a real problem. It might be 20, it might be 30. It might be five. But it’s certainly not too early to think about it, because the issues to address are only going to get more complex as the systems get more self-willed.”

Even within A.I. research, Tegmark admits, “There is absolutely not a consensus that we should be concerned about this.” But there is a lot of concern, and sense of lack of power. Because, concretely, what can you do? “The thing we should worry about is that we’re not worried.”

Tegmark brings it to Earth with a case-example about purchasing a stroller: If you could spend more for a good one or less for one that “sometimes collapses and crushes the baby, but nobody’s been able to prove that it is caused by any design flaw. But it’s 10 percent off! So which one are you going to buy?”

“There are seven billion of us on this little spinning ball in space. And we have so much opportunity," Tegmark said. "We have all the resources in this enormous cosmos. At the same time, we have the technology to wipe ourselves out.”

Ninety-nine percent of the species that have lived on Earth have gone extinct; why should we not? Seeing the biggest picture of humanity and the planet is the heart of this. It’s not meant to be about inspiring terror or doom. Sometimes that is what it takes to draw us out of the little things, where in the day-to-day we lose sight of enormous potentials.

in 2001, the top 10 websites accounted for 31 percent of all U.S. page views, but, by 2010, they accounted for 75 percent of them.

The Internet has created a long tail, but almost all the revenue and power is among the small elite at the head.

Advances in artificial intelligence will accelerate this centralizing trend. That’s because A.I. companies will be able to reap the rewards of network effects. The bigger their network and the more data they collect, the more effective and attractive they become.

As a result, our A.I. future is likely to be ruled by an oligarchy of two or three large, general-purpose cloud-based commercial intelligences.”

engineers at a few gigantic companies will have vast-though-hidden power to shape how data are collected and framed, to harvest huge amounts of information, to build the frameworks through which the rest of us make decisions and to steer our choices. If you think this power will be used for entirely benign ends, then you have not read enough history.

The second implication is philosophical. A.I. will redefine what it means to be human. Our identity as humans is shaped by what machines and other animals can’t do

On the other hand, machines cannot beat us at the things we do without conscious thinking: developing tastes and affections, mimicking each other and building emotional attachments, experiencing imaginative breakthroughs, forming moral sentiments.

For the last few centuries, reason was seen as the ultimate human faculty. But now machines are better at many of the tasks we associate with thinking — like playing chess, winning at Jeopardy, and doing math.

In the age of smart machines, we’re not human because we have big brains. We’re human because we have social skills, emotional capacities and moral intuitions.

I could paint two divergent A.I. futures, one deeply humanistic, and one soullessly utilitarian.

In the cold, utilitarian future, on the other hand, people become less idiosyncratic. If the choice architecture behind many decisions is based on big data from vast crowds, everybody follows the prompts and chooses to be like each other. The machine prompts us to consume what is popular, the things that are easy and mentally undemanding.

In this future, there is increasing emphasis on personal and moral faculties: being likable, industrious, trustworthy and affectionate. People are evaluated more on these traits, which supplement machine thinking, and not the rote ones that duplicate it

In the humanistic one, machines liberate us from mental drudgery so we can focus on higher and happier things. In this future, differences in innate I.Q. are less important. Everybody has Google on their phones so having a great memory or the ability to calculate with big numbers doesn’t help as much.

In the current issue of Wired, the technology writer Kevin Kelly says that we had all better get used to this level of predictive prowess. Kelly argues that the age of artificial intelligence is finally at hand.

The Pew Research Center survey asked people different questions but found similar doubts about AI. Just 15 percent of respondents said they were more excited than concerned about the increasing use of AI in daily life.

“It’s fantastic that there is public skepticism about AI. There absolutely should be,” said Meredith Broussard, an artificial intelligence researcher and professor at New York University.

Broussard said there can be no way to design artificial intelligence software to make inherently human decisions, like grading students’ tests or determining the course of medical treatment.

Most Americans essentially agree with Broussard that AI has a place in our lives, but not for everything.

Most people said it was a bad idea to use AI for military drones that try to distinguish between enemies and civilians or trucks making local deliveries without human drivers. Most respondents said it was a good idea for machines to perform risky jobs such as coal mining.

Roman Yampolskiy, an AI specialist at the University of Louisville engineering school, told me he’s concerned about how quickly technologists are building computers that are designed to “think” like the human brain and apply knowledge not just in one narrow area, like recommending Netflix movies, but for complex tasks that have tended to require human intelligence.

“We have an arms race between multiple untested technologies. That is my concern,” Yampolskiy said. (If you want to feel terrified, I recommend Yampolskiy’s research paper on the inability to control advanced AI.)

The term “AI” is a catch-all for everything from relatively uncontroversial technology, such as autocomplete in your web search queries, to the contentious software that promises to predict crime before it happens. Our fears about the latter might be overwhelming our beliefs about the benefits from more mundane AI.

With greater accuracy, for example, marketers can comb large databases of consumer behavior to get more precise information on buying habits. And improvements in facial recognition are likely to make surveillance technology cheaper and more commonplace.

Modern artificial neural networks are composed of an array of software components, divided into inputs, hidden layers and outputs. The arrays can be “trained” by repeated exposures to recognize patterns like images or sounds.

“The point about this approach is that it scales beautifully. Basically you just need to keep making it bigger and faster, and it will get better. There’s no looking back now.”

“Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate,” Hawking warned in recent months. “Humans, who are limited by slow biological evolution, couldn't compete and would be superseded.”

There is much to be said, however, for art’s ability to evoke emotion based on common experience, sincerity, talent, and unique skill. Rich proves that AI can be used to answer complicated questions. But what we think of as creative work in the humanities is much more often about asking questions than it is about answering them.

when Mr. Nichol tweaked his requests a little, adding or subtracting a few words here or there, it provided what he wanted. When he asked for “a piano in a living room filled with sand,” the image looked more like a beach in a living room.

DALL-E is what artificial intelligence researchers call a neural network, which is a mathematical system loosely modeled on the network of neurons in the brain.

the same technology that recognizes the commands spoken into smartphones and identifies the presence of pedestrians as self-driving cars navigate city streets.

A neural network learns skills by analyzing large amounts of data. By pinpointing patterns in thousands of avocado photos, for example, it can learn to recognize an avocado.

DALL-E looks for patterns as it analyzes millions of digital images as well as text captions that describe what each image depicts. In this way, it learns to recognize the links between the images and the words.

Another area where AI can be misused is in building registries, which can then be used to target certain population groups. Crawford noted historical cases of registry abuse, including IBM’s role in enabling Nazi Germany to track Jewish, Roma and other ethnic groups with the Hollerith Machine, and the Book of Life used in South Africa during apartheid.

Donald Trump has floated the idea of creating a Muslim registry. “We already have that. Facebook has become the default Muslim registry of the world,

Crawford was concerned about the potential use of AI in predictive policing systems, which already gather the kind of data necessary to train an AI system. Such systems are flawed, as shown by a Rand Corporation study of Chicago’s program. The predictive policing did not reduce crime, but did increase harassment of people in “hotspot” areas

research from Cambridge University that showed it is possible to predict people’s religious beliefs based on what they “like” on the social network. Christians and Muslims were correctly classified in 82% of cases, and similar results were achieved for Democrats and Republicans (85%). That study was concluded in 2013,

Another worry related to the manipulation of political beliefs or shifting voters, something Facebook and Cambridge Analytica claim they can already do. Crawford was skeptical about giving Cambridge Analytica credit for Brexit and the election of Donald Trump, but thinks what the firm promises – using thousands of data points on people to work out how to manipulate their views – will be possible “in the next few years”.

“This is a fascist’s dream,” she said. “Power without accountability.”

Such black box systems are starting to creep into government. Palantir is building an intelligence system to assist Donald Trump in deporting immigrants.

Crawford argues that we have to make these AI systems more transparent and accountable. “The ocean of data is so big. We have to map their complex subterranean and unintended effects.”

Crawford has founded AI Now, a research community focused on the social impacts of artificial intelligence to do just this “We want to make these systems as ethical as possible and free from unseen biases.”

Computers already are predominant, human life already takes place mostly within them, and people are satisfied with the results.

These devices pose numerous problems. Cost is one. Like a cheap propane gauge, a traditional bike lock is a commodity. It can be had for $10 to $15, a tenth of the price of Nokē’s connected version. Security and privacy are others. The CIA was rumored to have a back door into Samsung TVs for spying. Disturbed people have been caught speaking to children over hacked baby monitors. A botnet commandeered thousands of poorly secured internet-of-things devices to launch a massive distributed denial-of-service attack against the domain-name syste

Reliability plagues internet-connected gadgets, too. When the network is down, or the app’s service isn’t reachable, or some other software behavior gets in the way, the products often cease to function properly—or at all.

Turing guessed that machines would become most compelling when they became convincing companions, which is essentially what today’s smartphones (and smart toasters) do.

But Turing never claimed that machines could think, let alone that they might equal the human mind. Rather, he surmised that machines might be able to exhibit convincing behavior.

People choose computers as intermediaries for the sensual delight of using computers

ne such affection is the pleasure of connectivity. You don’t want to be offline. Why would you want your toaster or doorbell to suffer the same fate? Today, computational absorption is an ideal. The ultimate dream is to be online all the time, or at least connected to a computational machine of some kind.

Doorbells and cars and taxis hardly vanish in the process. Instead, they just get moved inside of computers.

“Being a computer” means something different today than in 1950, when Turing proposed the imitation game. Contra the technical prerequisites of artificial intelligence, acting like a computer often involves little more than moving bits of data around, or acting as a controller or actuator. Grill as computer, bike lock as computer, television as computer. An intermediary

Or consider doorbells once more. Forget Ring, the doorbell has already retired in favor of the computer. When my kids’ friends visit, they just text a request to come open the door. The doorbell has become computerized without even being connected to an app or to the internet. Call it “disruption” if you must, but doorbells and cars and taxis hardly vanish in the process. Instead, they just get moved inside of computers, where they can produce new affections.

The present status of intelligent machines is more powerful than any future robot apocalypse.

Why would anyone ever choose a solution that doesn’t involve computers, when computers are available? Propane tanks and bike locks are still edge cases, but ordinary digital services work similarly: The services people seek out are the ones that allow them to use computers to do things—from finding information to hailing a cab to ordering takeout. This is a feat of aesthetics as much as it is one of business. People choose computers as intermediaries for the sensual delight of using computers, not just as practical, efficient means for solving problems.

This is not where anyone thought computing would end up. Early dystopic scenarios cautioned that the computer could become a bureaucrat or a fascist, reducing human behavior to the predetermined capacities of a dumb machine. Or else, that obsessive computer use would be deadening, sucking humans into narcotic detachment.Those fears persist to some extent, partly because they have been somewhat realized. But they have also been inverted. Being away from them now feels deadening, rather than being attached to them without end. And thus, the actions computers take become self-referential: to turn more and more things into computers to prolong that connection.

But the real present status of intelligent machines is both humdrum and more powerful than any future robot apocalypse. Turing is often called the father of AI, but he only implied that machines might become compelling enough to inspire interaction. That hardly counts as intelligence, artificial or real. It’s also far easier to achieve. Computers already have persuaded people to move their lives inside of them. The machines didn’t need to make people immortal, or promise to serve their every whim, or to threaten to destroy them absent assent. They just needed to become a sufficient part of everything human beings do such that they can’t—or won’t—imagine doing those things without them.

. The real threat of computers isn’t that they might overtake and destroy humanity with their future power and intelligence. It’s that they might remain just as ordinary and impotent as they are today, and yet overtake us anyway.

And he has every reason to be delighted, as the newly developed neural networks will relieve neurobiologists of many thousands of hours of monotonous work in the future. As a result, they will also reduce the time needed to decode the connectome and, perhaps also, consciousness, by many years.

David Marr, a neuroscientist colleague of Chomsky's at MIT, defined a general framework for studying complex biological systems (like the brain) in his influential book Vision,

a complex biological system can be understood at three distinct levels. The first level ("computational level") describes the input and output to the system, which define the task the system is performing. In the case of the visual system, the input might be the image projected on our retina and the output might our brain's identification of the objects present in the image we had observed. The second level ("algorithmic level") describes the procedure by which an input is converted to an output, i.e. how the image on our retina can be processed to achieve the task described by the computational level. Finally, the third level ("implementation level") describes how our own biological hardware of cells implements the procedure described by the algorithmic level.

The emphasis here is on the internal structure of the system that enables it to perform a task, rather than on external association between past behavior of the system and the environment. The goal is to dig into the "black box" that drives the system and describe its inner workings, much like how a computer scientist would explain how a cleverly designed piece of software works and how it can be executed on a desktop computer.

As written today, the history of cognitive science is a story of the unequivocal triumph of an essentially Chomskyian approach over Skinner's behaviorist paradigm -- an achievement commonly referred to as the "cognitive revolution,"

While this may be a relatively accurate depiction in cognitive science and psychology, behaviorist thinking is far from dead in related disciplines. Behaviorist experimental paradigms and associationist explanations for animal behavior are used routinely by neuroscientists

Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field's heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the "new AI" -- focused on using statistical learning techniques to better mine and predict data -- is unlikely to yield general principles about the nature of intelligent beings or about cognition.

Behaviorist principles of associations could not explain the richness of linguistic knowledge, our endlessly creative use of it, or how quickly children acquire it with only minimal and imperfect exposure to language presented by their environment.

it has been argued in my view rather plausibly, though neuroscientists don't like it -- that neuroscience for the last couple hundred years has been on the wrong track.

Implicit in this endeavor is the assumption that with enough sophisticated statistical tools and a large enough collection of data, signals of interest can be weeded it out from the noise in large and poorly understood biological systems.

Brenner, a contemporary of Chomsky who also participated in the same symposium on AI, was equally skeptical about new systems approaches to understanding the brain. When describing an up-and-coming systems approach to mapping brain circuits called Connectomics, which seeks to map the wiring of all neurons in the brain (i.e. diagramming which nerve cells are connected to others), Brenner called it a "form of insanity."

These debates raise an old and general question in the philosophy of science: What makes a satisfying scientific theory or explanation, and how ought success be defined for science?

Ever since Isaiah Berlin's famous essay, it has become a favorite pastime of academics to place various thinkers and scientists on the "Hedgehog-Fox" continuum: the Hedgehog, a meticulous and specialized worker, driven by incremental progress in a clearly defined field versus the Fox, a flashier, ideas-driven thinker who jumps from question to question, ignoring field boundaries and applying his or her skills where they seem applicable.

Chomsky's work has had tremendous influence on a variety of fields outside his own, including computer science and philosophy, and he has not shied away from discussing and critiquing the influence of these ideas, making him a particularly interesting person to interview.

If you take a look at the progress of science, the sciences are kind of a continuum, but they're broken up into fields. The greatest progress is in the sciences that study the simplest systems. So take, say physics -- greatest progress there. But one of the reasons is that the physicists have an advantage that no other branch of sciences has. If something gets too complicated, they hand it to someone else.

If a molecule is too big, you give it to the chemists. The chemists, for them, if the molecule is too big or the system gets too big, you give it to the biologists. And if it gets too big for them, they give it to the psychologists, and finally it ends up in the hands of the literary critic, and so on.

An unlikely pair, systems biology and artificial intelligence both face the same fundamental task of reverse-engineering a highly complex system whose inner workings are largely a mystery

neuroscience developed kind of enthralled to associationism and related views of the way humans and animals work. And as a result they've been looking for things that have the properties of associationist psychology.

Artificial intelligence pioneer Ray Kurzweil was among the first to recognize how the link between pattern recognition and human intelligence could be used to build the next generation of artificially intelligent machines.

where human "expertise" has always trumped machine "expertise."

It turns out patterns matter, and they matter a lot.

The more you think about it, the more you can see patterns all around you. Getting to work on time in the morning is the result of recognizing patterns in your daily commute

it's really just a matter of recognizing the right patterns faster than anyone else, and machines just have so much processing power these days it's easy to see them becoming the future doctors and lawyers of the world.

The future of intelligence is in making our patterns better, our heuristics stronger.

One thing is clear – being able to recognize patterns is what gave humans their evolutionary edge over animals.

How we refine, shape and improve our pattern recognition is the key to how much longer we'll have the evolutionary edge over machines.

But there's another reason as well: Every time computers break some new barrier, we decide—or maybe just finally get it through our thick skulls—that we set the bar too low.

the best estimates of the human brain suggest that our own processing power is about equivalent to 10 petaflops. ("Peta" comes after giga and tera.) That's a lot of flops, but last year an IBM Blue Gene/Q supercomputer at Lawrence Livermore National Laboratory was clocked at 16.3 petaflops.

in Lake Michigan terms, we finally have a few inches of water in the lake bed, and we can see it rising. All those milestones along the way—playing chess, translating web pages, winning at Jeopardy!, driving a car—aren't just stunts. They're precisely the kinds of things you'd expect as we struggle along with platforms that aren't quite powerful enough—yet. True artificial intelligence will very likely be here within a couple of decades. Making it small, cheap, and ubiquitous might take a decade more.

In other words, by about 2040 our robot paradise awaits.