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Concept of artificial intelligence: an AI dummy superimposed on a green circuit board.

Artificial intelligence

Does the future still need us? That was the question computer scientist Bill Joy famously posed a couple of decades ago. If the things we read in the media about artificial intelligence can be believed, the answer's a definite "no." Smart computers already grade our exams, help us choose our next YouTube video, and decide what we can post on social media; and they'll soon be doing much more, from powering vast armies of robot soldiers to safely steering our cars and trucks. But just because machines like these do seemingly smart things, does it follow that they really are intelligent? And, even if they can be described that way, is their growing ability a sure signpost to a future of redundant, human stupidity? What exactly is artificial intelligence and why should we care?

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Artwork: Can we imagine a world where computers can imagine a world without us? Composite photo by incorporating dummy photo by Conrad Johnson courtesy of US Army and DVIDS.

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  1. What is artificial intelligence?
  2. How will we know when we've finally created something intelligent?
  3. Types of artificial intelligence
  4. What is AI used for in the real world?
  5. AI in action
  6. Why bother with artificial intelligence?
  7. What next for artificial intelligence?
  8. AI timeline: A brief history of artificial intelligence
  9. Find out more

What is artificial intelligence?

Some of the real (human) minds that have wrestled with the problem of artificial (machine) intelligence have offered deceptively simple definitions:

But what is intelligence?

Of course, to understand definitions like these, we need to reflect a little bit on what we mean by intelligence. When we say someone is "clever," typically we mean they're good at brainy, book-type stuff, although you can certainly be a clever football player, pastry chef, or just about anything else. If we say someone is "talented," we mean they have some sort of natural ability—a born head-start that makes them better than average.

So what does intelligence mean? Effectively, it's a talent for cleverness that you can choose to deploy however you want. You're not just clever at being a lawyer or a historian: you have a general aptitude for stuff that you can apply in different ways at different times. You're like an actor who can play many roles. Today you might be a world-class lawyer; tomorrow, you might turn your hand to learning classical piano or becoming a chess grand master. Like an actor, you have an overarching talent and a conscious, free-willed ability to play any part you choose. So intelligence is a kind of general-purpose thinking talent underpinned by qualities like a good memory, excellent language and reasoning skills, an ability to communicate with other people, often combined with creativity, emotional awareness, morality, self-awareness, and so on. A clever person might be good at just one thing (because they've been doing it a long time) and flounder when they try something else; an intelligent person has the potential to be good at anything and everything (or, at least, many different things), even when they've not been explicitly trained in those things.

Four examples of human intelligence: map-reading, fire-fighting, flying a plane, playing football.

Photo: Map-reading, fire-fighting, flying a plane, playing football—which of these things takes intelligence? They all do. We could build "clever" computers or robots to do all these things, but they still might not be what we consider "intelligent." Yet if we could build a machine that could do all these things, and many more, without any human intervention, would that be truly "intelligent"... or is there more to it than that? Photos by William Johnson, Christopher Quail, Trevor T. McBride, and Trevor Cokley, all courtesy of US Air Force.

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But "general problem-solving ability" might seem like a vague and woolly definition of intelligence—and some people prefer to be more specific. So, in the field of psychology (the science of human behavior), you'll find more than a few people willing to define intelligence as the ability to pass intelligence (IQ) tests. That's a circular definition, but it's not as cynical as it sounds. An intelligence test is something specific, well known, and well studied. You could take a typical IQ test and break it up into all the different puzzles it contains. You could figure out step-by-step ways of solving each one and train a computer in how to do it. Give your computer an IQ test and it ought to score pretty highly, in theory making it an artificially intelligent machine, at least by Marvin Minsky's definition. Well, no. Because if you gave it a slightly different test it hadn't been trained for, it would probably have no idea what to do and fail, without any of the crushing self-awareness that a human would have in the same situation. Still, we might say it has some of the qualities of an intelligent machine or an intelligent human.

Strong AI

If we define intelligence this way, it means machines with artificial intelligence would have similar general-purpose problem-solving ability: they might behave as human-like brains in computer-like boxes. This way of conceiving things is sometimes called strong AI or artificial general intelligence (AGI) and it's what most people assume we mean when we talk about intelligent machines: if not machine replicas of humans, machines with most (or all of) the intellectual qualities of a thinking human being—and sometimes their emotional and creative qualities too.

Weak AI

The idea of making machines that can behave in clever ways to solve specific, limited problems (like playing chess, driving a car, operating a factory robot, or whatever) is called weak AI or artificial narrow intelligence (ANI). Most of the vaguely "intelligent" computers and robots that we come across at the moment fall into this category. A chess-playing computer like IBM's Deep Blue made world headlines when it defeated grand master Gary Kasparov back in 1997, but much of its cleverness was down to brute-force computation—and just imagine it trying to drive a car down the freeway. By the same token, self-driving Google cars look pretty impressive, but don't expect them to sit down across the chess board anytime soon.

What's the point of AI?

A checkerboard pattern of computers and brains, illustrating the concept of AI.

Strong versus weak, general versus narrow, human-like versus machine-like, intelligent versus merely clever—there are all sorts of ways of considering "degrees" of artificial intelligence. We can picture a scale ranging from simple, narrow artificial cleverness (clanking, preprogrammed, machine-like ability to carry out specific tasks, like a Roomba vacuum cleaner scrabbling round your living room without any awareness of what it's doing); through chess-playing computers, self-driving cars, and robots that can rescue people from disaster zones; right up to a supremely, generally artificially intelligent computer that's smart enough to reprogram itself, recognize its flaws and design better versions of itself, and even has some sort of consciousness of what it's doing.

Yet many would argue that this isn't a simple scale, however, and that AGI is entirely different from ANI (qualitatively different), and not just "turbocharged" ANI (quantitatively different). And if that's true, it definitely doesn't follow that our ultimate goal should be to create the most generally, artificially intelligent, humanlike computer we possibly can; that's where much of the debate about AI gets bogged down. Our first goal is to understand our goal. If we want a rescue robot that we can deploy in emergencies to pull people out of a burning building, it needs a selected range of useful behaviours. It probably doesn't need to play chess, drive cars, or speak Spanish, though the more things it can do, obviously the better it can improvise in unpredictable situations, just like humans.

One goal of AI is simply to do something that humans do as well as they do it, without getting hung up on trying to do everything that humans do better than they do them or doing it exactly as a human would. "Strong" is not automatically better than "weak," even if the use of those emotionally loaded words make it sound so. Weak is a misnomer. There's nothing "weak" about a world-class human chess player if all you want is someone who can play chess. A "weak" Roomba robot can make a very nice job of cleaning your carpet. You can take a very pragmatic approach to AI, in other words, without worrying excessively about wider philosophical questions, even if those things are of great interest to philosophers. In other words, you can program a computer to play chess without sitting down to define the word "intelligent" or worrying about just how intelligent your computer will turn out to be. The important thing is to be true to our own goals, whatever they might be.

One of the often-overlooked goals of AI—overlooked in the popular media, at least—is to shed light on how our own brains work through the scientific study of how machines could mimic them. Since the 1950s, psychology has undergone a "cognitive" revolution in which quite a bit of the mystery hidden in our heads has been unraveled by research that assumes brains process information in similar ways to computers. AI research sheds more light on cognitive psychology and neuropsychology in a similar way: by figuring out how machines could be intelligent, we learn more about how humans are already intelligent.

How will we know when we've finally created something intelligent?

If our goal is to make intelligent machines, how will we know when we've achieved it? We could see see if they pass something called the Turing test, but that doesn't guarantee either "intelligence" or "understanding"...

The Turing test

This all-important question was what Alan Turing honed in on in a scientific paper he penned in 1950 called "Computing Machinery and Intelligence," which gave rise to a famous scientific experiment now called the Turing test (which Turing himself called the "imitation game"). [4] The basic idea is to compare how well a human and an "intelligent" computer can pass the same, real-life test: having a conversation. The experimenter sits in a room chatting with someone, through their computer, who is sitting outside and out of sight. What the experimenter doesn't know is whether they're chatting with another human or with a computer programmed to analyze the conversation and respond like a human. Simply speaking, if they're chatting with a computer and the experimenter thinks they're chatting with a human, they might as well be chatting with a human—and, in that case, we can consider the computer intelligent. In effect, Turing replaced the abstract question "Can a machine think?" with the much more practical question "Can a machine imitate a human?"

The basic concept of the Turing test or Imitation Game

Artwork: The Turing test. Suppose you're sitting at the red computer in the red room, communicating (through on-screen chat) with either a computer or another person in the blue room. If you're chatting to a computer, but it can convince you you're chatting to another person, we can regard that computer as intelligent.

Testing the test

Turing's test is ingenious. Rather than quibbling over the definition of intelligence, it offers a simple comparison as an acceptable test: can a computer pass itself off as a human? We could reasonably argue that this isn't, in fact, a valid test of intelligence. It's a test of what computer scientists call natural language processing (NLP)—and related cognitive abilities like logical reasoning, judgment, and memory—but is it a useful test of intelligence? Perhaps that's not the point. If our interest is in developing "weaker" forms of AI, such as superbly safe and dependable self-driving cars, it really doesn't matter whether they can chat to humans—and maybe we could consider them intelligent (usefully clever) all the same?

Why should human intelligence be the yardstick for machine intelligence? Why should a human definition of human intelligence be the yardstick? What if we compared an expertly designed and trained self-driving car with a teenager who'd had a couple of driving lessons. Would we consider the teenager unintelligent because they couldn't drive as well? Who would you rather be driven by: a human who could pass an intelligence test with a certain score or an expert driver, human or machine, trained in the best possible way?

One objection to the Turing test is that it encourages us not to develop intelligent machines but simply machines that can pass the Turing test; not to develop "intelligent" machines but machines that are as plausibly human as possible. Passing the Turing test may not be the be-all and end-all of intelligence any more than scoring highly on IQ tests is the be-all and end-all of academic success—or a prediction for leading a happy and successful life. There are plenty of things we need to do in our world that don't necessarily need general-purpose human intelligence—and maybe solving those problems in the best possible way, rather than the most human way, should be our real focus?

The Chinese room

Even if a machine can pass the Turing test, that doesn't mean it has any conscious awareness of what it's doing or that it's consciously passing itself off as a human (in the way that an actor might play a role). You can imagine a machine that's given a huge database of every possible conversation anyone has ever had in the whole of history so all it has to do is look up what the human says to it and, having analyzed what's already been said to establish some context, offer a plausible reply from its almost infinite repertoire.

This is a variation on another thought experiment called the Chinese room, devised in 1980 by philosopher John Searle as an objection to the whole idea of strong AI. [5] He argued that a machine could do something apparently very intelligent (such as holding a Turing test conversation) just by following rules and without understanding what it was doing in any way. He imagined the machine to be like an English-speaking person sitting in a room being fed sentences in Chinese that they didn't understand on pieces of paper posted under the door. The person would look up the sentences in a huge book of rules, find appropriate responses, and pass those back under the same door. Just because the person can flawlessly answer questions in Chinese, Searle argues, doesn't mean they understand a word of what they're doing. A machine programmed to translate Chinese is different from a human who understands Chinese and translates as a byproduct of that.

So what?

These sorts of tests and thought experiments rapidly bog down in semantic arguments over definitions of things like intelligence, knowledge, and understanding, which may be intensely interesting to philosophers but aren't necessarily that important for solving practical, real-world problems. Searle's Chinese room continues to stir up philosophical debate about the meaning of understanding, intelligence, syntax, semantics, the relationship of the mind to the body, and very much more besides.

Some agree with Searle's position, highlighting what they see as the absurdity of AI geeks claiming that computer models of human behavior are essentially no different from the behavior they simulate. Others, such as MIT robot scientist Rodney Brookes, believe these sorts of arguments are based on an unshakable (and often unscientific) belief that humans are somehow special. [6] That makes the whole argument essentially circular: machines can't be intelligent because humans are special; humans are special because machines can't be as intelligent as them—is more or less how it goes.

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Types of artificial intelligence

Since the dawn of the field in the 1950s, most real-world, AI computer programs have fallen into several broad types, which (for the sake of simplicity) I'm going to categorize into just three: heuristic search, expert systems, and machine learning. There are also hybrid systems that combine two or more of them. Let's consider these in turn.

Heuristic search

How do you build a computer that can play chess? If you play chess yourself, your strategy is probably to arrive at each move by considering every move you could possibly make, moves those might lead to, and so on, running your lithe, monkey mind along a tree of possibilities until you figure out the move most likely to win the game. If you have unlimited brainpower, you could theoretically consider every possible move and rank them accordingly. But, do the math, and you'll find the number of possible moves is well beyond the limits of memory and time. According to Marvin Minsky, writing about this problem back in 1966, we're talking something like 10120 moves, whereas even a simpler game like checkers can come in at 1040 possible moves. [7]

Chess game with black and white pieces

Photo: Chess-playing computers typically use heuristic search. Modern chess programs often work the same way as similar programs designed in the 1960s, but because today's machines are much more powerful, they can consider far more moves in the same amount of time. That's essentially why today's computers are better than yesterday's.

Our brains can't process so much stuff—or anything like it. So instead of considering every possible move, you (or a computer) can use basic rules of thumb to narrow down the searches you make, turning an overwhelming problem into something your brain (or a processor chip) can reasonably handle. This is called a heuristic search. An obvious heuristic most of us apply in game situations, if only out of consideration for the people we're playing with, is to spend at least a certain amount of time looking for moves but not too much. Or, getting more complex, you might use strategies like trying to dominate the center of the board, recognizing certain key board patterns, or preserving important pieces like your bishops and queen at the expense of losing less-valuable pieces. The key point about heuristic search is that it settles on a good-enough solution in the time available rather than trying to find the one and only perfect outcome.

Heuristic search is great for logical board games like chess, checkers, Scrabble, and so on, which involve considering lots of very similar potential moves, but how useful is it in the real world? You can imagine an artificially intelligent app that searches through thousands of houses and flats for sale or rent to help you find the best one using a heuristic approach to narrow things down. Instead of showing you everything, it might show you homes like ones you've tended to look at before within a certain radius or price bracket. But what about more complex problems like offering legal advice, figuring out what's wrong with a broken-down car, or diagnosing illnesses?

Expert systems

You can probably see straight away that medical diagnosis doesn't necessarily lend itself to a simple heuristic search: if you're a critically ill patient, you're not looking for a good-enough diagnosis in the time available; you want the right diagnosis, however long it takes. Your doctor doesn't think "Well I'll consider the first 10 diseases that pop into my head and then just pick the most likely one."

Expert systems (sometimes called knowledge based systems or KBS) are computer programs designed to go beyond simple search and decision making using more detailed "if X then Y" analysis and reasoning. Typically they have a database of knowledge gleaned from studying real, human experts and a separate system that can reason by dipping into that knowledge. The limitation of expert systems, and it's not always a problem, is that a computer trained in one domain of knowledge (like legal advice or medical diagnosis) isn't any use to us in a different field. It's perfectly possible for humans to change careers and switch from being brain surgeons to corporate lawyers, but expert-system machines can't voluntarily do the same thing without swapping their databases. Indeed, some medical knowledge is so very complex, so expert, that even an expert system trained in one medical field (say, cancer) might be of limited use in another medical field (emergency medicine). While that's true of human doctors, the key difference is that human experts tend to recognize their own limitations and know when to ask for help; machine experts don't know when they're making incompetent decisions.

Machine learning

Machine learning is one of the buzzwords of cutting-edge AI, although it's actually a very old term that dates back to the 1950s. In practice, it often means training a neural network (very loosely, a hugely simplified computer model of a brain-like structure, made from layers of interconnected cells called "units") on millions, billions, or trillions of examples of something so it can quickly recognize or classify something it hasn't seen before. So, trained to look at millions of pictures of tables and chairs, it can tell you whether a photo it's never seen before shows a table or a chair—and that's how the automatic photo classification algorithms work on your phone. Machine learning explains how Google can filter out explicit adult images from your search results if you don't want your family to see them: algorithms trained on adult photos can spot tell-tale signs in other photos as people upload them. Machine learning also underpins the automated translation you can find on Google, Bing, and Skype. Programs like these are now so good that they can convert almost any language into a fairly decent (at least understandable) translation of any other language without "understanding" a word of either; they're great examples of Searle's Chinese room, except they're capable of operating in any tongue you like.

One of the characteristics of machine learning programs is—the clue is in the name—that they learn as they go along. So unlike a preprogrammed expert system, they get better and better at what they do the more they do it, just like a real human, to the point where they can do whatever you program them to do better than you can do it yourself.

A few more things are worth quickly noting. Although the terms "machine learning" and "neural network" sound like they stem from psychology, they're much more to do with complex math and statistics. And when we talk about "neural networks" being "brain-like," that's really just an analogy. There's not necessarily anything brain-like in a neural network (synapses that work like one-way streets and chemical neurotransmitters are two very obvious differences, to start with). What neural machine learning and human brains do have in common is that they both process large amounts of data in parallel (or "pseudo-parallel," in the case of neural networks, which are usually models of parallel, brain-like structures implemented on traditional, serial computers).

Three AI approaches to tree-climbing: heuristic search; expert system; and machine learning.

Artwork: How would an AI computer/robot go about climbing a tree? Left: It could use heuristic search to consider a number of the most likely paths along the branches; Middle: It could use an expert system database of knowledge about trees and climbing, and some IF... THEN... rules to arrive at the best route by reasoning; Right: Using machine learning, perhaps it could analyze thousands of photos of people climbing trees to figure out a good route when presented with a new photo of a tree?

Hybrid systems

I've adopted a fairly arbitrary, three-way classification here purely for the purposes of an easy-to-understand explanation. But you can classify AI however you wish. Heuristic search and expert systems are examples of what some call symbolic AI or Good Old-Fashioned AI (GOFAI), and work in a classically, cognitive fashion like the "this-leads-to-that" flowchart diagrams we draw to explain simple computer programs. Machine learning, on the other hand, proceeds in a more parallel, brain-like, "connectionist" fashion, without obvious serial logic. Symbolic AI systems work things out by serial, logic reasoning using a limited amount of data, while connectionist systems use parallel processing on massive amounts of data.

Simple diagram showing the difference between serial and parallel processing in computer systems.

Artwork: Serial versus parallel processing. In traditional symbolic AI (such as an expert system), one step proceeds logically after another; neural-network machine learning uses parallel processing (although, confusingly, it's usually modeled on traditional computers that work through serial processing!

If you're trying to build a self-driving car or a robot that can rescue people from buildings, you're going to be using elements of the three previous types of AI in different ways, at different times. So hybrid systems are increasingly interesting to researchers who might once have worked exclusively with either symbolic, serial AI or parallel, connectionist, machine-learning systems. DeepMind's Atari-game playing system is one very recent example of a hybrid system that works partly through symbolic AI and partly through a neural network.

Self-driving cars rely on machine learning to interpret images of the streets they're driving down in real time. We've all see those Google captcha tests that get us to prove we're not robots by classifying fire hydrants, bridges, bicycles, and taxis. That exercise is part of Google's effort to train machine learning systems to recognize different objects that their cars will be able to tap into later. But driving isn't just about recognizing objects; you also need to recognize and analyze situations. For example, if you're driving along and you see a round red object rolling out into the road, you might think "Oh look, there's a ball", but what you should really think is "That ball probably belongs to a child and if the ball's rolled onto the road, a child may be right behind it, so I need to slow down and be prepared to stop". This is more like expert system decision making. A self-driving car will probably always have human occupants and a backup human driver to get it out of trouble, so it doesn't need to be completely autonomous in the same way as a robot soldier, which might need to extricate itself from a wider variety of unexpected situations. So, revisiting our original idea of artificial intelligence as a spectrum between narrow and general, we can see that the more autonomous and general purpose a computer or machine, the wider the range of different AI tactics or techniques it's likely to need to draw on. And it will also need the ability to figure out which type of thinking to use in different situations.

One key problem—for all types of AI—is representing some aspect of the world in a way that a computer system can understand and process. At a simple level, if you're using a neural network to recognize faces, how exactly do you "translate" holistic faces into discrete bits of data that the network can work with? And how do you convert the computer network's output into a form that makes sense to humans?

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What is AI used for in the real world?

When John McCarthy died in 2011, an obituary in the British Independent newspaper noted how he'd once observed that a major breakthrough in the field could come in anything from "five to 500 years." McCarthy set his sights on the very distant horizon of strong AI and despaired at researchers who satisfied themselves with narrower goals. As he wrote in 2006:

"I have to admit dissatisfaction with the lack of ambition displayed by most of my fellow AI researchers....For example, the language used by the Deep Blue program that defeated world chess champion Garry Kasparov cannot be used to express "I am a chess program, but consider many more irrelevant moves than a human does." and draw conclusions from it. The designers of the program did not see a need for this capability." [8]

Just because we don't have computers that are smart enough to go head-to-head with humans at anything and everything, it doesn't mean we've made no progress with artificial intelligence because, as we've seen already, strong AI was not always the goal. Look around the modern world and you'll see endless applications of less spectacular (but still very impressive) artificial intelligence, including things like Alexa, Siri, and machine-driven customer agents, automated stock trading, "Things you might like" recommendations on Amazon and eBay, online advertising software that shows you ads based on who you are and what you're likely to buy, vacuum cleaning Roomba robots that use increasingly intelligent tactics to clean more efficiently, and much more.

AI in action

Here are some diverse examples of AI in action I've pulled out from recent news stories:

Why bother with artificial intelligence?

Roomba vacuum cleaning robot shown from the underside

Photo: Roomba vacuum cleaning robots use increasingly sophisticated models to clean your home. We'd hardly call them intelligent, but why would we want to?

In a world packed with intelligent humans, why bother developing intelligent machines? Is it a sign of our own intelligence that we recognize our potential stupidity as a limitation we can overcome? Or yet another sign of human arrogance—that we somehow always think we can do better than nature? Is the argument between "supporters" and "opponents" of machine intelligence two different sides of that arrogance: that humans are special because we think we can develop machines better than ourselves but can't... or special because we really can't? Is the quest for artificial intelligence rather like the quest for the perfect move in a long and difficult game of chess... something we feel that's out there somewhere, always just beyond reach? Or is it more like a heuristic search in a board game, where quick, practical, good-enough solutions to limited problems are better than waiting around for perfection?

AI raises all kinds of philosophical questions, but it raises social problems too, like whether smart factory robots and machine-learning computer systems will put "intelligent" people out of work. There are ethical problems as well. For example, people are already asking difficult questions about the legal responsibilities of self-driving cars. If you get mown down by a car like this on a crosswalk, is the car somehow to blame? Is the passive "driver" sitting with their hands on a dummy steering wheel to blame for not intervening? Or is the designer to blame even if they're on the other side of the world? If robot soldiers kill civilians by accident, does the buck stop with the general who gave the order or the engineers who built and programmed the machines?

One of the big problems we're already seeing is that algorithms using machine-learning can come up with decisions that we have no way to understand or challenge. We know the human assumptions on which the algorithms are based, but there's no easy way of seeing why a neural network trained on billions of disparate items of data will, for example, wrongly flag a world-famous war photo as pornography or scandalously miscategorize a photo of three black teenagers. In the second case, after Google's algorithms tagged people as gorillas, Google didn't even bother trying to fix the problem: it simply stopped its algorithms labeling any photo as a gorilla, chimpanzee, or monkey. The AI problem was too hard to solve, so they solved an easier problem instead.

What next for artificial intelligence?

This dispute is unresolved—and perhaps unresolvable... no-one knows, for sure, whether an AGI could really be intelligent.

Professor Margaret A. Boden [9]

While pragmatic computer scientists get on with building ever cleverer machines, philosophers continue to wrestle with endless variations on essentially the same stale question: whether ingenious (or brute-force) computational "cleverness" (the cake of AI) can truly replicate human "intelligence" if it can't replicate quintessential, more subtle human qualities like understanding, empathy, morality, emotion, creativity, free-will, and consciousness (the all-important icing). Perhaps understanding the difference between computational "cleverness" and human "intelligence" is the real Turing test?

Arriving where we started, let's ask again: does the future really need us? Arguably, the biggest limitation of today's relatively weak AI systems is that they don't recognize their own limitations: for that, they still need us. Until artificially intelligent machines are smart enough to understand how really stupid they can be, perhaps they pose no ultimate threat to us humans.

In the longer term, is humankind at risk from ultra-intelligent AGI machines—or is that just science fiction nonsense, as skeptics like Hubert Dreyfus were arguing over a half century ago? Are such ideas a dangerous distraction from a far more plausible threat: how many livelihoods are at risk as machine-learning-type algorithms become increasingly "clever" at doing jobs we once regarded as absolutely human? The jury is still divided. Where pessimists like Bill Joy have warned that AI is a Pandora's box, optimists, such as AI "prophet" Ray Kurzweil, look to the singularity—effectively, where machines surpass human intelligence—and a bold, rosy future where "stupid," intractable human problems like war and poverty are deftly swept aside by brainy machines. Meanwhile, pragmatic robot scientists such as MIT's Rodney Brooks argue that machine intelligence is simply the latest human technology that helps people overcome their all-too-human limitations: "We will become a merger between flesh and machines. We will have the best that machineness has to offer but we will also have our bioheritage to augment whatever level of machine technology we have so far developed." [10]

So the question isn't really whether the future still needs us; it's "What kind of future do we want?"—and how can we use technologies like AI to bring it about?

AI timeline: A brief history of artificial intelligence

Early days


The rise of intelligent machines

Modern AI

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  1.    What is AI? Basic Questions by John McCarthy.
  2.    Semantic Information Processing by Marvin Minsky. MIT Press, 1968, p.v.
  3.    [PDF] Computing Machinery and Intelligence by Alan Turing, Mind 49, pp.433–460.
  4.    [PDF] Computing Machinery and Intelligence by Alan Turing, Mind 49, pp.433–460.
  5.     Searle, J., 1980, "Minds, Brains and Programs," Behavioral and Brain Sciences, 3: pp/417–57. For a broader discussion, see The Chinese Room Argument by David Cole. Stanford Encyclopedia of Philosophy, 2004/2020.
  6.    Robot: The Future of Flesh and Machines by Rodney Brooks. Penguin, 2002, pp.179–180.
  7.    Artificial intelligence by Marvin Minsky. Scientific American, September 1966.
  8.    The Philosophy of AI and the AI of Philosophy by John McCarthy, Stanford University, June 2006.
  9.    Artificial Intelligence: A Very Short Introduction by Margaret A. Boden. Oxford, 2018, p.129.
  10.    Robot: The Future of Flesh and Machines by Rodney A. Brooks. Allen Lane/Penguin, 2002., p.x.
  11.    Discourse on Method and The Meditations by René Descartes. Penguin, 1968, p.73.

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