Group items tagged

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.

That allows quantum computers to make many calculations in one pass, while digital ones have to perform each calculation separately. By speeding up computation, quantum computers could potentially solve big, complex problems in fields like chemistry and materials science that are out of reach today.

When Google researchers made their supremacy claim in 2019, they said their quantum computer performed a calculation in 3 minutes 20 seconds that would take about 10,000 years on a state-of-the-art conventional supercomputer.

The IBM researchers in the new study performed a different task, one that interests physicists. They used a quantum processor with 127 qubits to simulate the behavior of 127 atom-scale bar magnets — tiny enough to be governed by the spooky rules of quantum mechanics — in a magnetic field. That is a simple system known as the Ising model, which is often used to study magnetism.

This problem is too complex for a precise answer to be calculated even on the largest, fastest supercomputers.

On the quantum computer, the calculation took less than a thousandth of a second to complete. Each quantum calculation was unreliable — fluctuations of quantum noise inevitably intrude and induce errors — but each calculation was quick, so it could be performed repeatedly.

Indeed, for many of the calculations, additional noise was deliberately added, making the answers even more unreliable. But by varying the amount of noise, the researchers could tease out the specific characteristics of the noise and its effects at each step of the calculation.“We can amplify the noise very precisely, and then we can rerun that same circuit,” said Abhinav Kandala, the manager of quantum capabilities and demonstrations at IBM Quantum and an author of the Nature paper. “And once we have results of these different noise levels, we can extrapolate back to what the result would have been in the absence of noise.”In essence, the researchers were able to subtract the effects of noise from the unreliable quantum calculations, a process they call error mitigation.

Altogether, the computer performed the calculation 600,000 times, converging on an answer for the overall magnetization produced by the 127 bar magnets.

Although an Ising model with 127 bar magnets is too big, with far too many possible configurations, to fit in a conventional computer, classical algorithms can produce approximate answers, a technique similar to how compression in JPEG images throws away less crucial data to reduce the size of the file while preserving most of the image’s details

Certain configurations of the Ising model can be solved exactly, and both the classical and quantum algorithms agreed on the simpler examples. For more complex but solvable instances, the quantum and classical algorithms produced different answers, and it was the quantum one that was correct.

Thus, for other cases where the quantum and classical calculations diverged and no exact solutions are known, “there is reason to believe that the quantum result is more accurate,”

Mr. Anand is currently trying to add a version of error mitigation for the classical algorithm, and it is possible that could match or surpass the performance of the quantum calculations.

In the long run, quantum scientists expect that a different approach, error correction, will be able to detect and correct calculation mistakes, and that will open the door for quantum computers to speed ahead for many uses.

Error correction is already used in conventional computers and data transmission to fix garbles. But for quantum computers, error correction is likely years away, requiring better processors able to process many more qubits

“This is one of the simplest natural science problems that exists,” Dr. Gambetta said. “So it’s a good one to start with. But now the question is, how do you generalize it and go to more interesting natural science problems?”

Those might include figuring out the properties of exotic materials, accelerating drug discovery and modeling fusion reactions.

The real payoff in subscribing to the idea of a brain as a computer would come from using that idea to profitably guide research. In an article last fall in the journal Science, two of my colleagues (Adam Marblestone of M.I.T. and Thomas Dean of Google) and I endeavored to do just that, suggesting that a particular kind of computer, known as the field programmable gate array, might offer a preliminary starting point for thinking about how the brain works.

FIELD programmable gate arrays consist of a large number of “logic block” programs that can be configured, and reconfigured, individually, to do a wide range of tasks. One logic block might do arithmetic, another signal processing, and yet another look things up in a table. The computation of the whole is a function of how the individual parts are configured. Much of the logic can be executed in parallel, much like what happens in a brain.

our suggestion is that the brain might similarly consist of highly orchestrated sets of fundamental building blocks, such as “computational primitives” for constructing sequences, retrieving information from memory, and routing information between different locations in the brain. Identifying those building blocks, we believe, could be the Rosetta stone that unlocks the brain.

it is unlikely that we will ever be able to directly connect the language of neurons and synapses to the diversity of human behavior, as many neuroscientists seem to hope. The chasm between brains and behavior is just too vast.

Our best shot may come instead from dividing and conquering. Fundamentally, that may involve two steps: finding some way to connect the scientific language of neurons and the scientific language of computational primitives (which would be comparable in computer science to connecting the physics of electrons and the workings of microprocessors); and finding some way to connect the scientific language of computational primitives and that of human behavior (which would be comparable to understanding how computer programs are built out of more basic microprocessor instructions).

If neurons are akin to computer hardware, and behaviors are akin to the actions that a computer performs, computation is likely to be the glue that binds the two.

The crash, which killed all 49 people on board as well as one person on the ground, should never have happened.

aptain’s response to the stall warning, the investigators reported, “should have been automatic, but his improper flight control inputs were inconsistent with his training” and instead revealed “startle and confusion.

Automation has become so sophisticated that on a typical passenger flight, a human pilot holds the controls for a grand total of just three minutes.

We humans have been handing off chores, both physical and mental, to tools since the invention of the lever, the wheel, and the counting bead.

And that, many aviation and automation experts have concluded, is a problem. Overuse of automation erodes pilots’ expertise and dulls their reflexes,

No one doubts that autopilot has contributed to improvements in flight safety over the years. It reduces pilot fatigue and provides advance warnings of problems, and it can keep a plane airborne should the crew become disabled. But the steady overall decline in plane crashes masks the recent arrival of “a spectacularly new type of accident,”

“We’re forgetting how to fly.”

The experience of airlines should give us pause. It reveals that automation, for all its benefits, can take a toll on the performance and talents of those who rely on it. The implications go well beyond safety. Because automation alters how we act, how we learn, and what we know, it has an ethical dimension. The choices we make, or fail to make, about which tasks we hand off to machines shape our lives and the place we make for ourselves in the world.

What pilots spend a lot of time doing is monitoring screens and keying in data. They’ve become, it’s not much of an exaggeration to say, computer operators.

Examples of complacency and bias have been well documented in high-risk situations—on flight decks and battlefields, in factory control rooms—but recent studies suggest that the problems can bedevil anyone working with a computer

That may leave the person operating the computer to play the role of a high-tech clerk—entering data, monitoring outputs, and watching for failures. Rather than opening new frontiers of thought and action, software ends up narrowing our focus.

A labor-saving device doesn’t just provide a substitute for some isolated component of a job or other activity. It alters the character of the entire task, including the roles, attitudes, and skills of the people taking part.

when we work with computers, we often fall victim to two cognitive ailments—complacency and bias—that can undercut our performance and lead to mistakes. Automation complacency occurs when a computer lulls us into a false sense of security. Confident that the machine will work flawlessly and handle any problem that crops up, we allow our attention to drift.

Automation bias occurs when we place too much faith in the accuracy of the information coming through our monitors. Our trust in the software becomes so strong that we ignore or discount other information sources, including our own eyes and ears

Automation is different now. Computers can be programmed to perform complex activities in which a succession of tightly coordinated tasks is carried out through an evaluation of many variables. Many software programs take on intellectual work—observing and sensing, analyzing and judging, even making decisions—that until recently was considered the preserve of humans.

Automation turns us from actors into observers. Instead of manipulating the yoke, we watch the screen. That shift may make our lives easier, but it can also inhibit the development of expertise.

Since the late 1970s, psychologists have been documenting a phenomenon called the “generation effect.” It was first observed in studies of vocabulary, which revealed that people remember words much better when they actively call them to mind—when they generate them—than when they simply read them.

When you engage actively in a task, you set off intricate mental processes that allow you to retain more knowledge. You learn more and remember more. When you repeat the same task over a long period, your brain constructs specialized neural circuits dedicated to the activit

What looks like instinct is hard-won skill, skill that requires exactly the kind of struggle that modern software seeks to alleviate.

In many businesses, managers and other professionals have come to depend on decision-support systems to analyze information and suggest courses of action. Accountants, for example, use the systems in corporate audits. The applications speed the work, but some signs suggest that as the software becomes more capable, the accountants become less so.

You can put limits on the scope of automation, making sure that people working with computers perform challenging tasks rather than merely observing.

Experts used to assume that there were limits to the ability of programmers to automate complicated tasks, particularly those involving sensory perception, pattern recognition, and conceptual knowledge

Who needs humans, anyway? That question, in one rhetorical form or another, comes up frequently in discussions of automation. If computers’ abilities are expanding so quickly and if people, by comparison, seem slow, clumsy, and error-prone, why not build immaculately self-contained systems that perform flawlessly without any human oversight or intervention? Why not take the human factor out of the equation?

The cure for imperfect automation is total automation.

That idea is seductive, but no machine is infallible. Sooner or later, even the most advanced technology will break down, misfire, or, in the case of a computerized system, encounter circumstances that its designers never anticipated. As automation technologies become more complex, relying on interdependencies among algorithms, databases, sensors, and mechanical parts, the potential sources of failure multiply. They also become harder to detect.

conundrum of computer automation.

Because many system designers assume that human operators are “unreliable and inefficient,” at least when compared with a computer, they strive to give the operators as small a role as possible.

People end up functioning as mere monitors, passive watchers of screens. That’s a job that humans, with our notoriously wandering minds, are especially bad at

people have trouble maintaining their attention on a stable display of information for more than half an hour. “This means,” Bainbridge observed, “that it is humanly impossible to carry out the basic function of monitoring for unlikely abnormalities.”

a person’s skills “deteriorate when they are not used,” even an experienced operator will eventually begin to act like an inexperienced one if restricted to just watching.

You can program software to shift control back to human operators at frequent but irregular intervals; knowing that they may need to take command at any moment keeps people engaged, promoting situational awareness and learning.

What’s most astonishing, and unsettling, about computer automation is that it’s still in its early stages.

most software applications don’t foster learning and engagement. In fact, they have the opposite effect. That’s because taking the steps necessary to promote the development and maintenance of expertise almost always entails a sacrifice of speed and productivity.

Learning requires inefficiency. Businesses, which seek to maximize productivity and profit, would rarely accept such a trade-off. Individuals, too, almost always seek efficiency and convenience.

Abstract concerns about the fate of human talent can’t compete with the allure of saving time and money.

The small island of Igloolik, off the coast of the Melville Peninsula in the Nunavut territory of northern Canada, is a bewildering place in the winter.

, Inuit hunters have for some 4,000 years ventured out from their homes on the island and traveled across miles of ice and tundra to search for game. The hunters’ ability to navigate vast stretches of the barren Arctic terrain, where landmarks are few, snow formations are in constant flux, and trails disappear overnight, has amazed explorers and scientists for centuries. The Inuit’s extraordinary way-finding skills are born not of technological prowess—they long eschewed maps and compasses—but of a profound understanding of winds, snowdrift patterns, animal behavior, stars, and tides.

The Igloolik hunters have begun to rely on computer-generated maps to get around. Adoption of GPS technology has been particularly strong among younger Inuit, and it’s not hard to understand why.

But as GPS devices have proliferated on Igloolik, reports of serious accidents during hunts have spread. A hunter who hasn’t developed way-finding skills can easily become lost, particularly if his GPS receiver fails.

The routes so meticulously plotted on satellite maps can also give hunters tunnel vision, leading them onto thin ice or into other hazards a skilled navigator would avoid.

An Inuit on a GPS-equipped snowmobile is not so different from a suburban commuter in a GPS-equipped SUV: as he devotes his attention to the instructions coming from the computer, he loses sight of his surroundings. He travels “blindfolded,” as Aporta puts it

A unique talent that has distinguished a people for centuries may evaporate in a generation.

Computer automation severs the ends from the means. It makes getting what we want easier, but it distances us from the work of knowing. As we transform ourselves into creatures of the screen, we face an existential question: Does our essence still lie in what we know, or are we now content to be defined by what we want?

Code is too hard to think about. Before trying to understand the attempts themselves, then, it’s worth understanding why this might be: what it is about code that makes it so foreign to the mind, and so unlike anything that came before it.

Technological progress used to change the way the world looked—you could watch the roads getting paved; you could see the skylines rise. Today you can hardly tell when something is remade, because so often it is remade by code.

Software has enabled us to make the most intricate machines that have ever existed. And yet we have hardly noticed, because all of that complexity is packed into tiny silicon chips as millions and millions of lines of cod

The programmer, the renowned Dutch computer scientist Edsger Dijkstra wrote in 1988, “has to be able to think in terms of conceptual hierarchies that are much deeper than a single mind ever needed to face before.” Dijkstra meant this as a warning.

As programmers eagerly poured software into critical systems, they became, more and more, the linchpins of the built world—and Dijkstra thought they had perhaps overestimated themselves.

What made programming so difficult was that it required you to think like a computer.

“The problem is that software engineers don’t understand the problem they’re trying to solve, and don’t care to,” says Leveson, the MIT software-safety expert. The reason is that they’re too wrapped up in getting their code to work.

Though he runs a lab that studies the future of computing, he seems less interested in technology per se than in the minds of the people who use it. Like any good toolmaker, he has a way of looking at the world that is equal parts technical and humane. He graduated top of his class at the California Institute of Technology for electrical engineering,

“The serious problems that have happened with software have to do with requirements, not coding errors.” When you’re writing code that controls a car’s throttle, for instance, what’s important is the rules about when and how and by how much to open it. But these systems have become so complicated that hardly anyone can keep them straight in their head. “There’s 100 million lines of code in cars now,” Leveson says. “You just cannot anticipate all these things.”

a nearly decade-long investigation into claims of so-called unintended acceleration in Toyota cars. Toyota blamed the incidents on poorly designed floor mats, “sticky” pedals, and driver error, but outsiders suspected that faulty software might be responsible

software experts spend 18 months with the Toyota code, picking up where NASA left off. Barr described what they found as “spaghetti code,” programmer lingo for software that has become a tangled mess. Code turns to spaghetti when it accretes over many years, with feature after feature piling on top of, and being woven around

Using the same model as the Camry involved in the accident, Barr’s team demonstrated that there were actually more than 10 million ways for the onboard computer to cause unintended acceleration. They showed that as little as a single bit flip—a one in the computer’s memory becoming a zero or vice versa—could make a car run out of control. The fail-safe code that Toyota had put in place wasn’t enough to stop it

. In all, Toyota recalled more than 9 million cars, and paid nearly $3 billion in settlements and fines related to unintended acceleration.

The problem is that programmers are having a hard time keeping up with their own creations. Since the 1980s, the way programmers work and the tools they use have changed remarkably little.

“Visual Studio is one of the single largest pieces of software in the world,” he said. “It’s over 55 million lines of code. And one of the things that I found out in this study is more than 98 percent of it is completely irrelevant. All this work had been put into this thing, but it missed the fundamental problems that people faced. And the biggest one that I took away from it was that basically people are playing computer inside their head.” Programmers were like chess players trying to play with a blindfold on—so much of their mental energy is spent just trying to picture where the pieces are that there’s hardly any left over to think about the game itself.

The fact that the two of them were thinking about the same problem in the same terms, at the same time, was not a coincidence. They had both just seen the same remarkable talk, given to a group of software-engineering students in a Montreal hotel by a computer researcher named Bret Victor. The talk, which went viral when it was posted online in February 2012, seemed to be making two bold claims. The first was that the way we make software is fundamentally broken. The second was that Victor knew how to fix it.

This is the trouble with making things out of code, as opposed to something physical. “The complexity,” as Leveson puts it, “is invisible to the eye.”

in early 2012, Victor had finally landed upon the principle that seemed to thread through all of his work. (He actually called the talk “Inventing on Principle.”) The principle was this: “Creators need an immediate connection to what they’re creating.” The problem with programming was that it violated the principle. That’s why software systems were so hard to think about, and so rife with bugs: The programmer, staring at a page of text, was abstracted from whatever it was they were actually making.

“Our current conception of what a computer program is,” he said, is “derived straight from Fortran and ALGOL in the late ’50s. Those languages were designed for punch cards.”

WYSIWYG (pronounced “wizzywig”) came along. It stood for “What You See Is What You Get.”

Victor’s point was that programming itself should be like that. For him, the idea that people were doing important work, like designing adaptive cruise-control systems or trying to understand cancer, by staring at a text editor, was appalling.

With the right interface, it was almost as if you weren’t working with code at all; you were manipulating the game’s behavior directly.

When the audience first saw this in action, they literally gasped. They knew they weren’t looking at a kid’s game, but rather the future of their industry. Most software involved behavior that unfolded, in complex ways, over time, and Victor had shown that if you were imaginative enough, you could develop ways to see that behavior and change it, as if playing with it in your hands. One programmer who saw the talk wrote later: “Suddenly all of my tools feel obsolete.”

hen John Resig saw the “Inventing on Principle” talk, he scrapped his plans for the Khan Academy programming curriculum. He wanted the site’s programming exercises to work just like Victor’s demos. On the left-hand side you’d have the code, and on the right, the running program: a picture or game or simulation. If you changed the code, it’d instantly change the picture. “In an environment that is truly responsive,” Resig wrote about the approach, “you can completely change the model of how a student learns ... [They] can now immediately see the result and intuit how underlying systems inherently work without ever following an explicit explanation.” Khan Academy has become perhaps the largest computer-programming class in the world, with a million students, on average, actively using the program each month.

The ideas spread. The notion of liveness, of being able to see data flowing through your program instantly, made its way into flagship programming tools offered by Google and Apple. The default language for making new iPhone and Mac apps, called Swift, was developed by Apple from the ground up to support an environment, called Playgrounds, that was directly inspired by Light Table.

“Typically the main problem with software coding—and I’m a coder myself,” Bantegnie says, “is not the skills of the coders. The people know how to code. The problem is what to code. Because most of the requirements are kind of natural language, ambiguous, and a requirement is never extremely precise, it’s often understood differently by the guy who’s supposed to code.”

In a pair of later talks, “Stop Drawing Dead Fish” and “Drawing Dynamic Visualizations,” Victor went one further. He demoed two programs he’d built—the first for animators, the second for scientists trying to visualize their data—each of which took a process that used to involve writing lots of custom code and reduced it to playing around in a WYSIWYG interface.

Victor suggested that the same trick could be pulled for nearly every problem where code was being written today. “I’m not sure that programming has to exist at all,” he told me. “Or at least software developers.” In his mind, a software developer’s proper role was to create tools that removed the need for software developers. Only then would people with the most urgent computational problems be able to grasp those problems directly, without the intermediate muck of code.

Victor implored professional software developers to stop pouring their talent into tools for building apps like Snapchat and Uber. “The inconveniences of daily life are not the significant problems,” he wrote. Instead, they should focus on scientists and engineers—as he put it to me, “these people that are doing work that actually matters, and critically matters, and using really, really bad tools.”

Bantegnie’s company is one of the pioneers in the industrial use of model-based design, in which you no longer write code directly. Instead, you create a kind of flowchart that describes the rules your program should follow (the “model”), and the computer generates code for you based on those rules

In a model-based design tool, you’d represent this rule with a small diagram, as though drawing the logic out on a whiteboard, made of boxes that represent different states—like “door open,” “moving,” and “door closed”—and lines that define how you can get from one state to the other. The diagrams make the system’s rules obvious: Just by looking, you can see that the only way to get the elevator moving is to close the door, or that the only way to get the door open is to stop.

. In traditional programming, your task is to take complex rules and translate them into code; most of your energy is spent doing the translating, rather than thinking about the rules themselves. In the model-based approach, all you have is the rules. So that’s what you spend your time thinking about. It’s a way of focusing less on the machine and more on the problem you’re trying to get it to solve.

“Everyone thought I was interested in programming environments,” he said. Really he was interested in how people see and understand systems—as he puts it, in the “visual representation of dynamic behavior.” Although code had increasingly become the tool of choice for creating dynamic behavior, it remained one of the worst tools for understanding it. The point of “Inventing on Principle” was to show that you could mitigate that problem by making the connection between a system’s behavior and its code immediate.

On this view, software becomes unruly because the media for describing what software should do—conversations, prose descriptions, drawings on a sheet of paper—are too different from the media describing what software does do, namely, code itself.

for this approach to succeed, much of the work has to be done well before the project even begins. Someone first has to build a tool for developing models that are natural for people—that feel just like the notes and drawings they’d make on their own—while still being unambiguous enough for a computer to understand. They have to make a program that turns these models into real code. And finally they have to prove that the generated code will always do what it’s supposed to.

tice brings order and accountability to large codebases. But, Shivappa says, “it’s a very labor-intensive process.” He estimates that before they used model-based design, on a two-year-long project only two to three months was spent writing code—the rest was spent working on the documentation.

uch of the benefit of the model-based approach comes from being able to add requirements on the fly while still ensuring that existing ones are met; with every change, the computer can verify that your program still works. You’re free to tweak your blueprint without fear of introducing new bugs. Your code is, in FAA parlance, “correct by construction.”

“people are not so easily transitioning to model-based software development: They perceive it as another opportunity to lose control, even more than they have already.”

The bias against model-based design, sometimes known as model-driven engineering, or MDE, is in fact so ingrained that according to a recent paper, “Some even argue that there is a stronger need to investigate people’s perception of MDE than to research new MDE technologies.”

“Human intuition is poor at estimating the true probability of supposedly ‘extremely rare’ combinations of events in systems operating at a scale of millions of requests per second,” he wrote in a paper. “That human fallibility means that some of the more subtle, dangerous bugs turn out to be errors in design; the code faithfully implements the intended design, but the design fails to correctly handle a particular ‘rare’ scenario.”

Newcombe was convinced that the algorithms behind truly critical systems—systems storing a significant portion of the web’s data, for instance—ought to be not just good, but perfect. A single subtle bug could be catastrophic. But he knew how hard bugs were to find, especially as an algorithm grew more complex. You could do all the testing you wanted and you’d never find them all.

An algorithm written in TLA+ could in principle be proven correct. In practice, it allowed you to create a realistic model of your problem and test it not just thoroughly, but exhaustively. This was exactly what he’d been looking for: a language for writing perfect algorithms.

TLA+, which stands for “Temporal Logic of Actions,” is similar in spirit to model-based design: It’s a language for writing down the requirements—TLA+ calls them “specifications”—of computer programs. These specifications can then be completely verified by a computer. That is, before you write any code, you write a concise outline of your program’s logic, along with the constraints you need it to satisfy

Programmers are drawn to the nitty-gritty of coding because code is what makes programs go; spending time on anything else can seem like a distraction. And there is a patient joy, a meditative kind of satisfaction, to be had from puzzling out the micro-mechanics of code. But code, Lamport argues, was never meant to be a medium for thought. “It really does constrain your ability to think when you’re thinking in terms of a programming language,”

Code makes you miss the forest for the trees: It draws your attention to the working of individual pieces, rather than to the bigger picture of how your program fits together, or what it’s supposed to do—and whether it actually does what you think. This is why Lamport created TLA+. As with model-based design, TLA+ draws your focus to the high-level structure of a system, its essential logic, rather than to the code that implements it.

But TLA+ occupies just a small, far corner of the mainstream, if it can be said to take up any space there at all. Even to a seasoned engineer like Newcombe, the language read at first as bizarre and esoteric—a zoo of symbols.

this is a failure of education. Though programming was born in mathematics, it has since largely been divorced from it. Most programmers aren’t very fluent in the kind of math—logic and set theory, mostly—that you need to work with TLA+. “Very few programmers—and including very few teachers of programming—understand the very basic concepts and how they’re applied in practice. And they seem to think that all they need is code,” Lamport says. “The idea that there’s some higher level than the code in which you need to be able to think precisely, and that mathematics actually allows you to think precisely about it, is just completely foreign. Because they never learned it.”

“In the 15th century,” he said, “people used to build cathedrals without knowing calculus, and nowadays I don’t think you’d allow anyone to build a cathedral without knowing calculus. And I would hope that after some suitably long period of time, people won’t be allowed to write programs if they don’t understand these simple things.”

Programmers, as a species, are relentlessly pragmatic. Tools like TLA+ reek of the ivory tower. When programmers encounter “formal methods” (so called because they involve mathematical, “formally” precise descriptions of programs), their deep-seated instinct is to recoil.

Formal methods had an image problem. And the way to fix it wasn’t to implore programmers to change—it was to change yourself. Newcombe realized that to bring tools like TLA+ to the programming mainstream, you had to start speaking their language.

he presented TLA+ as a new kind of “pseudocode,” a stepping-stone to real code that allowed you to exhaustively test your algorithms—and that got you thinking precisely early on in the design process. “Engineers think in terms of debugging rather than ‘verification,’” he wrote, so he titled his internal talk on the subject to fellow Amazon engineers “Debugging Designs.” Rather than bemoan the fact that programmers see the world in code, Newcombe embraced it. He knew he’d lose them otherwise. “I’ve had a bunch of people say, ‘Now I get it,’” Newcombe says.

In the world of the self-driving car, software can’t be an afterthought. It can’t be built like today’s airline-reservation systems or 911 systems or stock-trading systems. Code will be put in charge of hundreds of millions of lives on the road and it has to work. That is no small task.

quantum computing relies on the fact that subatomic particles inhabit a range of states. Different relationships among the particles may coexist, as well. Those probable states can be narrowed to determine an optimal outcome among a near-infinitude of possibilities, which allows certain types of problems to be solved rapidly.

“This is a revolution not unlike the early days of computing,” he said. “It is a transformation in the way computers are thought about.”

It could be possible, for example, to tell instantly how the millions of lines of software running a network of satellites would react to a solar burst or a pulse from a nuclear explosion — something that can now take weeks, if ever, to determine.

Mr. Brownell, who joined D-Wave in 2009, was until 2000 the chief technical officer at Goldman Sachs. “In those days, we had 50,000 servers just doing simulations” to figure out trading strategies, he said. “I’m sure there is a lot more than that now, but we’ll be able to do that with one machine, for far less money.”

If Microsoft’s work pans out, he said, the millions of possible combinations of the proteins in a human gene could be worked out “fairly easily.”

Quantum computing has been a goal of researchers for more than three decades, but it has proved remarkably difficult to achieve. The idea has been to exploit a property of matter in a quantum state known as superposition, which makes it possible for the basic elements of a quantum computer, known as qubits, to hold a vast array of values simultaneously.

There are a variety of ways scientists create the conditions needed to achieve superposition as well as a second quantum state known as entanglement, which are both necessary for quantum computing. Researchers have suspended ions in magnetic fields, trapped photons or manipulated phosphorus atoms in silicon.

In the D-Wave system, a quantum computing processor, made from a lattice of tiny superconducting wires, is chilled close to absolute zero. It is then programmed by loading a set of mathematical equations into the lattice. The processor then moves through a near-infinity of possibilities to determine the lowest energy required to form those relationships. That state, seen as the optimal outcome, is the answer.

In coming years, the approach will make possible a new generation of artificial intelligence systems that will perform some functions that humans do with ease: see, speak, listen, navigate, manipulate and control.

“We’re moving from engineering computing systems to something that has many of the characteristics of biological computing,” said Larry Smarr

The new approach, used in both hardware and software, is being driven by the explosion of scientific knowledge about the brain. Kwabena Boahen, a computer scientist who leads Stanford’s Brains in Silicon research program, said that is also its limitation, as scientists are far from fully understanding how brains function.

They are not “programmed.” Rather the connections between the circuits are “weighted” according to correlations in data that the processor has already “learned.” Those weights are then altered as data flows in to the chip, causing them to change their values and to “spike.” That generates a signal that travels to other components and, in reaction, changes the neural network, in essence programming the next actions much the same way that information alters human thoughts and actions.

Traditional computers are also remarkably energy inefficient, especially when compared to actual brains, which the new neurons are built to mimic. I.B.M. announced last year that it had built a supercomputer simulation of the brain that encompassed roughly 10 billion neurons — more than 10 percent of a human brain. It ran about 1,500 times more slowly than an actual brain. Further, it required several megawatts of power, compared with just 20 watts of power used by the biological brain.

Running the program, known as Compass, which attempts to simulate a brain, at the speed of a human brain would require a flow of electricity in a conventional computer that is equivalent to what is needed to power both San Francisco and New York, Dr. Modha said.

in general what he argues is that if you take a look at animal cognition, human too, it's computational systems. Therefore, you want to look the units of computation. Think about a Turing machine, say, which is the simplest form of computation, you have to find units that have properties like "read", "write" and "address." That's the minimal computational unit, so you got to look in the brain for those. You're never going to find them if you look for strengthening of synaptic connections or field properties, and so on. You've got to start by looking for what's there and what's working and you see that from Marr's highest level.

it's basically in the spirit of Marr's analysis. So when you're studying vision, he argues, you first ask what kind of computational tasks is the visual system carrying out. And then you look for an algorithm that might carry out those computations and finally you search for mechanisms of the kind that would make the algorithm work. Otherwise, you may never find anything.

"Good Old Fashioned AI," as it's labeled now, made strong use of formalisms in the tradition of Gottlob Frege and Bertrand Russell, mathematical logic for example, or derivatives of it, like nonmonotonic reasoning and so on. It's interesting from a history of science perspective that even very recently, these approaches have been almost wiped out from the mainstream and have been largely replaced -- in the field that calls itself AI now -- by probabilistic and statistical models. My question is, what do you think explains that shift and is it a step in the right direction?

AI and robotics got to the point where you could actually do things that were useful, so it turned to the practical applications and somewhat, maybe not abandoned, but put to the side, the more fundamental scientific questions, just caught up in the success of the technology and achieving specific goals.

The approximating unanalyzed data kind is sort of a new approach, not totally, there's things like it in the past. It's basically a new approach that has been accelerated by the existence of massive memories, very rapid processing, which enables you to do things like this that you couldn't have done by hand. But I think, myself, that it is leading subjects like computational cognitive science into a direction of maybe some practical applicability... ..in engineering? Chomsky: ...But away from understanding.

I was very skeptical about the original work. I thought it was first of all way too optimistic, it was assuming you could achieve things that required real understanding of systems that were barely understood, and you just can't get to that understanding by throwing a complicated machine at it.

if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it's way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won't get the kind of understanding that the sciences have always been aimed at -- what you'll get at is an approximation to what's happening.

Suppose you want to predict tomorrow's weather. One way to do it is okay I'll get my statistical priors, if you like, there's a high probability that tomorrow's weather here will be the same as it was yesterday in Cleveland, so I'll stick that in, and where the sun is will have some effect, so I'll stick that in, and you get a bunch of assumptions like that, you run the experiment, you look at it over and over again, you correct it by Bayesian methods, you get better priors. You get a pretty good approximation of what tomorrow's weather is going to be. That's not what meteorologists do -- they want to understand how it's working. And these are just two different concepts of what success means, of what achievement is.

if you get more and more data, and better and better statistics, you can get a better and better approximation to some immense corpus of text, like everything in The Wall Street Journal archives -- but you learn nothing about the language.

the right approach, is to try to see if you can understand what the fundamental principles are that deal with the core properties, and recognize that in the actual usage, there's going to be a thousand other variables intervening -- kind of like what's happening outside the window, and you'll sort of tack those on later on if you want better approximations, that's a different approach.

take a concrete example of a new field in neuroscience, called Connectomics, where the goal is to find the wiring diagram of very complex organisms, find the connectivity of all the neurons in say human cerebral cortex, or mouse cortex. This approach was criticized by Sidney Brenner, who in many ways is [historically] one of the originators of the approach. Advocates of this field don't stop to ask if the wiring diagram is the right level of abstraction -- maybe it's no

if you went to MIT in the 1960s, or now, it's completely different. No matter what engineering field you're in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it. But that's a very different approach. And it resulted maybe from the fact that really for the first time in history, the basic sciences, like physics, had something really to tell engineers. And besides, technologies began to change very fast, so not very much point in learning the technologies of today if it's going to be different 10 years from now. So you have to learn the fundamental science that's going to be applicable to whatever comes along next. And the same thing pretty much happened in medicine.

that's the kind of transition from something like an art, that you learn how to practice -- an analog would be trying to match some data that you don't understand, in some fashion, maybe building something that will work -- to science, what happened in the modern period, roughly Galilean science.

it turns out that there actually are neural circuits which are reacting to particular kinds of rhythm, which happen to show up in language, like syllable length and so on. And there's some evidence that that's one of the first things that the infant brain is seeking -- rhythmic structures. And going back to Gallistel and Marr, its got some computational system inside which is saying "okay, here's what I do with these things" and say, by nine months, the typical infant has rejected -- eliminated from its repertoire -- the phonetic distinctions that aren't used in its own language.

people like Shimon Ullman discovered some pretty remarkable things like the rigidity principle. You're not going to find that by statistical analysis of data. But he did find it by carefully designed experiments. Then you look for the neurophysiology, and see if you can find something there that carries out these computations. I think it's the same in language, the same in studying our arithmetical capacity, planning, almost anything you look at. Just trying to deal with the unanalyzed chaotic data is unlikely to get you anywhere, just like as it wouldn't have gotten Galileo anywhere.

with regard to cognitive science, we're kind of pre-Galilean, just beginning to open up the subject

You can invent a world -- I don't think it's our world -- but you can invent a world in which nothing happens except random changes in objects and selection on the basis of external forces. I don't think that's the way our world works, I don't think it's the way any biologist thinks it is. There are all kind of ways in which natural law imposes channels within which selection can take place, and some things can happen and other things don't happen. Plenty of things that go on in the biology in organisms aren't like this. So take the first step, meiosis. Why do cells split into spheres and not cubes? It's not random mutation and natural selection; it's a law of physics. There's no reason to think that laws of physics stop there, they work all the way through. Well, they constrain the biology, sure. Chomsky: Okay, well then it's not just random mutation and selection. It's random mutation, selection, and everything that matters, like laws of physics.

What I think is valuable is the history of science. I think we learn a lot of things from the history of science that can be very valuable to the emerging sciences. Particularly when we realize that in say, the emerging cognitive sciences, we really are in a kind of pre-Galilean stage. We don't know wh

at we're looking for anymore than Galileo did, and there's a lot to learn from that.

Our Connections With Others Make Life More Meaningful

Supporting Others Is Linked to a Longer Life

05 of 05 It's Possible to Become More Empathetic

as long as enough of us continue keeping our data under our own control, we can still do so. And we had better do so, or the option may disappear."

"Jeopardy," with its puns and strangely phrased clues, seemed too hard for a computer. IBM already had teams building machines to answer questions, and their performance, in speed and precision, came nowhere close to even a moderately informed human. How could the next machine grow so much smarter?

He was comfortable conversing about everything from the details of computational linguistics to the evolution of life on Earth and the nature of human thought. This made him an ideal ambassador for a "Jeopardy"-playing machine. After all, his project would raises all sorts of issues, and fears, about the role of brainy machines in society. Would they compete for jobs? Could they establish their own agendas, like the infamous computer, HAL, in "2001: A Space Odyssey," and take control? What was the future of knowledge and intelligence, and how would brains and machines divvy up the cognitive work?

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.

upervised learning depends on annotated data: images, audio or text that is painstakingly labeled by hordes of workers. They circle people or outline bicycles on pictures of street traffic. The labeled data is fed to computer algorithms, teaching the algorithms what to look for. After ingesting millions of labeled images, the algorithms become expert at recognizing what they have been taught to see.

There is also reinforcement learning, with very limited supervision that does not rely on training data. Reinforcement learning in computer science,

is modeled after reward-driven learning in the brain: Think of a rat learning to push a lever to receive a pellet of food. The strategy has been developed to teach computer systems to take actions.

My money is on self-supervised learning,” he said, referring to computer systems that ingest huge amounts of unlabeled data and make sense of it all without supervision or reward. He is working on models that learn by observation, accumulating enough background knowledge that some sort of common sense can emerge.

redict outcomes and choose a course of action. “Everybody agrees we need predictive learning, but we disagree about how to get there,”

A more inclusive term for the future of A.I., he said, is “predictive learning,” meaning systems that not only recognize patterns but also p

A huge fraction of what we do in our day-to-day jobs is constantly refining our mental models of the world and then using those mental models to solve problems,” he said. “That encapsulates an awful lot of what we’d like A.I. to do.”Image

Currently, robots can operate only in well-defined environments with little variation.

“Our working assumption is that if we build sufficiently general algorithms, then all we really have to do, once that’s done, is to put them in robots that are out there in the real world doing real things,”

When Google’s algorithm detects a search term for which this distilled information is available, the search engine is trained to go fetch it rather than merely present links to Web pages. “There has been a shift in our thinking,” said Scott Huffman, an engineering director in charge of search quality at Google. “A part of our resources are now more human curated.”

“Our engineers evolve the algorithm, and humans help us see if a suggested change is really an improvement,” Mr. Huffman said.

Ben Taylor, 25, is a product manager at FindTheBest, a fast-growing start-up in Santa Barbara, Calif. The company calls itself a “comparison engine” for finding and comparing more than 100 topics and products, from universities to nursing homes, smartphones to dog breeds. Its Web site went up in 2010, and the company now has 60 full-time employees. Mr. Taylor helps design and edit the site’s education pages. He is not an engineer, but an English major who has become a self-taught expert in the arcane data found in Education Department studies and elsewhere. His research methods include talking to and e-mailing educators. He is an information sleuth.

“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.”

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.

Does your house need a new coat of paint? Don’t bother, just enter it into your public database and you have a nice new mint green paint job that everyone will see. Want to redecorate? Do it with computer graphics. You can have a birdbath in the front yard inhabited by Disneyesque animals who frolic and play. Even indoors, don’t buy artwork, just download it from the net and have it appear where you want.

Got a zit? No need to cover up with Clearsil, just erase it from your public face and people will see the improved version. You can dress up your clothes and hairstyle as well.

Of course, anyone can turn off their enhancements and see the plain old reality, but most people don’t bother most of the time because things are ugly that way.

Some of the kids attending Fairmont Junior High do so remotely. They appear as "ghosts", indistinguishable from the other kids except that you can walk through them. They go to classes and raise their hands to ask questions just like everyone else. They see the school and everyone at the school sees them. Instead of visiting friends, the kids can all instantly appear at one another’s locations.

The computer synthesizing visual imagery is able to call on the localizer network for views beyond what the person is seeing. In this way you can have 360 degree vision, or even see through walls. This is a transparent society with a vengeance!

The cumulative effect of all this technology was absolutely amazing and completely believable

One thing that was believable is that it seemed that a lot of the kids cheated, and it was almost impossible for the adults to catch them. With universal network connectivity it would be hard to make sure kids are doing their work on their own. I got the impression the school sort of looked the other way, the idea being that as long as the kids solved their problems, even if they got help via the net, that was itself a useful skill that they would be relying on all their lives.