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At the same time, videogames are becoming more and more sophisticated and in the future we’ll be able to have simulations of conscious entities inside them.

“Forty years ago we had Pong – two rectangles and a dot. That’s where we were. Now 40 years later, we have photorealistic, 3D simulations with millions of people playing simultaneously and it’s getting better every year. And soon we’ll have virtual reality, we’ll have augmented reality,” said Musk. “If you assume any rate of improvement at all, then the games will become indistinguishable from reality.”

“If one progresses at the current rate of technology a few decades into the future, very quickly we will be a society where there are artificial entities living in simulations that are much more abundant than human beings.

If there are many more simulated minds than organic ones, then the chances of us being among the real minds starts to look more and more unlikely. As Terrile puts it: “If in the future there are more digital people living in simulated environments than there are today, then what is to say we are not part of that already?”

Reasons to believe that the universe is a simulation include the fact that it behaves mathematically and is broken up into pieces (subatomic particles) like a pixelated video game. “Even things that we think of as continuous – time, energy, space, volume – all have a finite limit to their size. If that’s the case, then our universe is both computable and finite. Those properties allow the universe to be simulated,” Terrile said

“Is it logically possible that we are in a simulation? Yes. Are we probably in a simulation? I would say no,” said Max Tegmark, a professor of physics at MIT.

“In order to make the argument in the first place, we need to know what the fundamental laws of physics are where the simulations are being made. And if we are in a simulation then we have no clue what the laws of physics are. What I teach at MIT would be the simulated laws of physics,”

Terrile believes that recognizing that we are probably living in a simulation is as game-changing as Copernicus realizing that the Earth was not the center of the universe. “It was such a profound idea that it wasn’t even thought of as an assumption,”

That we might be in a simulation is, Terrile argues, a simpler explanation for our existence than the idea that we are the first generation to rise up from primordial ooze and evolve into molecules, biology and eventually intelligence and self-awareness. The simulation hypothesis also accounts for peculiarities in quantum mechanics, particularly the measurement problem, whereby things only become defined when they are observed.

“For decades it’s been a problem. Scientists have bent over backwards to eliminate the idea that we need a conscious observer. Maybe the real solution is you do need a conscious entity like a conscious player of a video game,

How can the hypothesis be put to the test

scientists can look for hallmarks of simulation. “Suppose someone is simulating our universe – it would be very tempting to cut corners in ways that makes the simulation cheaper to run. You could look for evidence of that in an experiment,” said Tegmark

First, it provides a scientific basis for some kind of afterlife or larger domain of reality above our world. “You don’t need a miracle, faith or anything special to believe it. It comes naturally out of the laws of physics,”

it means we will soon have the same ability to create our own simulations. “We will have the power of mind and matter to be able to create whatever we want and occupy those worlds.”

We don’t know. But one fanciful possibility is that we live in a computer simulation based on the laws of mathematics — not in what we commonly take to be the real world. According to this theory, some highly advanced computer programmer of the future has devised this simulation, and we are unknowingly part of it. Thus when we discover a mathematical truth, we are simply discovering aspects of the code that the programmer used.

the Oxford philosopher Nick Bostrom has argued that we are more likely to be in such a simulation than not. If such simulations are possible in theory, he reasons, then eventually humans will create them — presumably many of them. If this is so, in time there will be many more simulated worlds than nonsimulated ones. Statistically speaking, therefore, we are more likely to be living in a simulated world than the real one.

The jury is still out on the simulation hypothesis. But even if it proves too far-fetched, the possibility of the Platonic nature of mathematical ideas remains — and may hold the key to understanding our own reality.

This kind of modeling is already in use to study individual cellular processes like metabolism. But Dr. Covert said: “Where I think our work is different is that we explicitly include all of the genes and every known gene function. There’s no one else out there who has been able to include more than a handful of functions or more than, say, one-third of the genes.”

The simulation, which runs on a cluster of 128 computers, models the complete life span of the cell at the molecular level, charting the interactions of 28 categories of molecules — including DNA, RNA, proteins and small molecules known as metabolites, which are generated by cell processes.

They called the simulation an important advance in the new field of computational biology, which has recently yielded such achievements as the creation of a synthetic life form — an entire bacterial genome created by a team led by the genome pioneer J. Craig Venter. The scientists used it to take over an existing cell.

A decade ago, scientists developed simulations of metabolism that are now being used to study a wide array of cells, including bacteria, yeast and photosynthetic organisms. Other models exist for processes like protein synthesis.

“Right now, running a simulation for a single cell to divide only one time takes around 10 hours and generates half a gigabyte of data,” Dr. Covert wrote. “I find this fact completely fascinating, because I don’t know that anyone has ever asked how much data a living thing truly holds. We often think of the DNA as the storage medium, but clearly there is more to it than that.”

scientists chose an approach called object-oriented programming, which parallels the design of modern software systems. Software designers organize their programs in modules, which communicate with one another by passing data and instructions back and forth.

“The major modeling insight we had a few years ago was to break up the functionality of the cell into subgroups, which we could model individually, each with its own mathematics, and then to integrate these submodels together into a whole,”

Patterns that didn’t change one generation to the next, Dr. Conway called still lifes — such as the four-celled block, the six-celled beehive or the eight-celled pond. Patterns that took a long time to stabilize, he called methuselahs.

The second thing Life shows us is something that Darwin hit upon when he was looking at Life, the organic version. Complexity arises from simplicity!

I first encountered Life at the Exploratorium in San Francisco in 1978. I was hooked immediately by the thing that has always hooked me — watching complexity arise out of simplicity.

Life shows you two things. The first is sensitivity to initial conditions. A tiny change in the rules can produce a huge difference in the output, ranging from complete destruction (no dots) through stasis (a frozen pattern) to patterns that keep changing as they unfold.

Life shows us complex virtual “organisms” arising out of the interaction of a few simple rules — so goodbye “Intelligent Design.”

I’ve wondered for decades what one could learn from all that Life hacking. I recently realized it’s a great place to try to develop “meta-engineering” — to see if there are general principles that govern the advance of engineering and help us predict the overall future trajectory of technology.

Melanie Mitchell— Professor of complexity, Santa Fe Institute

Given that Conway’s proof that the Game of Life can be made to simulate a Universal Computer — that is, it could be “programmed” to carry out any computation that a traditional computer can do — the extremely simple rules can give rise to the most complex and most unpredictable behavior possible. This means that there are certain properties of the Game of Life that can never be predicted, even in principle!

I use the Game of Life to make vivid for my students the ideas of determinism, higher-order patterns and information. One of its great features is that nothing is hidden; there are no black boxes in Life, so you know from the outset that anything that you can get to happen in the Life world is completely unmysterious and explicable in terms of a very large number of simple steps by small items.

In Thomas Pynchon’s novel “Gravity’s Rainbow,” a character says, “But you had taken on a greater and more harmful illusion. The illusion of control. That A could do B. But that was false. Completely. No one can do. Things only happen.”This is compelling but wrong, and Life is a great way of showing this.

In Life, we might say, things only happen at the pixel level; nothing controls anything, nothing does anything. But that doesn’t mean that there is no such thing as action, as control; it means that these are higher-level phenomena composed (entirely, with no magic) from things that only happen.

Stephen Wolfram— Scientist and C.E.O., Wolfram Research

Brian Eno— Musician, London

Bert Chan— Artificial-life researcher and creator of the continuous cellular automaton “Lenia,” Hong Kong

it did have a big impact on beginner programmers, like me in the 90s, giving them a sense of wonder and a kind of confidence that some easy-to-code math models can produce complex and beautiful results. It’s like a starter kit for future software engineers and hackers, together with Mandelbrot Set, Lorenz Attractor, et cetera.

if we think about our everyday life, about corporations and governments, the cultural and technical infrastructures humans built for thousands of years, they are not unlike the incredible machines that are engineered in Life.

In normal times, they are stable and we can keep building stuff one component upon another, but in harder times like this pandemic or a new Cold War, we need something that is more resilient and can prepare for the unpreparable. That would need changes in our “rules of life,” which we take for granted.

Rudy Rucker— Mathematician and author of “Ware Tetralogy,” Los Gatos, Calif.

That’s what chaos is about. The Game of Life, or a kinky dynamical system like a pair of pendulums, or a candle flame, or an ocean wave, or the growth of a plant — they aren’t readily predictable. But they are not random. They do obey laws, and there are certain kinds of patterns — chaotic attractors — that they tend to produce. But again, unpredictable is not random. An important and subtle distinction which changed my whole view of the world.

William Poundstone— Author of “The Recursive Universe: Cosmic Complexity and the Limits of Scientific Knowledge,” Los Angeles, Calif.

The Game of Life’s pulsing, pyrotechnic constellations are classic examples of emergent phenomena, introduced decades before that adjective became a buzzword.

Fifty years later, the misfortunes of 2020 are the stuff of memes. The biggest challenges facing us today are emergent: viruses leaping from species to species; the abrupt onset of wildfires and tropical storms as a consequence of a small rise in temperature; economies in which billions of free transactions lead to staggering concentrations of wealth; an internet that becomes more fraught with hazard each year

Looming behind it all is our collective vision of an artificial intelligence-fueled future that is certain to come with surprises, not all of them pleasant.

The name Conway chose — the Game of Life — frames his invention as a metaphor. But I’m not sure that even he anticipated how relevant Life would become, and that in 50 years we’d all be playing an emergent game of life and death.

“We think this process of counterfactual simulation is really pervasive,” Gerstenberg says. “In many cases it may not be supported by eye movements, because there are many kinds of abstract counterfactual thinking that we just do in our mind. But the billiard-ball collisions lead to a particular kind of counterfactual simulation where we can see it.”

Even before the evolution of a central brain, nervous systems took advantage of a simple computing trick: competition.

It coordinates something called overt attention – aiming the satellite dishes of the eyes, ears, and nose toward anything important.

Selective enhancement therefore probably evolved sometime between hydras and arthropods—between about 700 and 600 million years ago, close to the beginning of complex, multicellular life

The next evolutionary advance was a centralized controller for attention that could coordinate among all senses. In many animals, that central controller is a brain area called the tectum

At any moment only a few neurons win that intense competition, their signals rising up above the noise and impacting the animal’s behavior. This process is called selective signal enhancement, and without it, a nervous system can do almost nothing.

All vertebrates—fish, reptiles, birds, and mammals—have a tectum. Even lampreys have one, and they appeared so early in evolution that they don’t even have a lower jaw. But as far as anyone knows, the tectum is absent from all invertebrates

According to fossil and genetic evidence, vertebrates evolved around 520 million years ago. The tectum and the central control of attention probably evolved around then, during the so-called Cambrian Explosion when vertebrates were tiny wriggling creatures competing with a vast range of invertebrates in the sea.

The tectum is a beautiful piece of engineering. To control the head and the eyes efficiently, it constructs something called an internal model, a feature well known to engineers. An internal model is a simulation that keeps track of whatever is being controlled and allows for predictions and planning.

The tectum’s internal model is a set of information encoded in the complex pattern of activity of the neurons. That information simulates the current state of the eyes, head, and other major body parts, making predictions about how these body parts will move next and about the consequences of their movement

In fish and amphibians, the tectum is the pinnacle of sophistication and the largest part of the brain. A frog has a pretty good simulation of itself.

With the evolution of reptiles around 350 to 300 million years ago, a new brain structure began to emerge – the wulst. Birds inherited a wulst from their reptile ancestors. Mammals did too, but our version is usually called the cerebral cortex and has expanded enormously

The cortex also takes in sensory signals and coordinates movement, but it has a more flexible repertoire. Depending on context, you might look toward, look away, make a sound, do a dance, or simply store the sensory event in memory in case the information is useful for the future.

The most important difference between the cortex and the tectum may be the kind of attention they control. The tectum is the master of overt attention—pointing the sensory apparatus toward anything important. The cortex ups the ante with something called covert attention. You don’t need to look directly at something to covertly attend to it. Even if you’ve turned your back on an object, your cortex can still focus its processing resources on it

The cortex needs to control that virtual movement, and therefore like any efficient controller it needs an internal model. Unlike the tectum, which models concrete objects like the eyes and the head, the cortex must model something much more abstract. According to the AST, it does so by constructing an attention schema—a constantly updated set of information that describes what covert attention is doing moment-by-moment and what its consequences are

Covert attention isn’t intangible. It has a physical basis, but that physical basis lies in the microscopic details of neurons, synapses, and signals. The brain has no need to know those details. The attention schema is therefore strategically vague. It depicts covert attention in a physically incoherent way, as a non-physical essence

this, according to the theory, is the origin of consciousness. We say we have consciousness because deep in the brain, something quite primitive is computing that semi-magical self-description.

I’m reminded of Teddy Roosevelt’s famous quote, “Do what you can with what you have where you are.” Evolution is the master of that kind of opportunism. Fins become feet. Gill arches become jaws. And self-models become models of others. In the AST, the attention schema first evolved as a model of one’s own covert attention. But once the basic mechanism was in place, according to the theory, it was further adapted to model the attentional states of others, to allow for social prediction. Not only could the brain attribute consciousness to itself, it began to attribute consciousness to others.

In the AST’s evolutionary story, social cognition begins to ramp up shortly after the reptilian wulst evolved. Crocodiles may not be the most socially complex creatures on earth, but they live in large communities, care for their young, and can make loyal if somewhat dangerous pets.

If AST is correct, 300 million years of reptilian, avian, and mammalian evolution have allowed the self-model and the social model to evolve in tandem, each influencing the other. We understand other people by projecting ourselves onto them. But we also understand ourselves by considering the way other people might see us.

t the cortical networks in the human brain that allow us to attribute consciousness to others overlap extensively with the networks that construct our own sense of consciousness.

Language is perhaps the most recent big leap in the evolution of consciousness. Nobody knows when human language first evolved. Certainly we had it by 70 thousand years ago when people began to disperse around the world, since all dispersed groups have a sophisticated language. The relationship between language and consciousness is often debated, but we can be sure of at least this much: once we developed language, we could talk about consciousness and compare notes

Maybe partly because of language and culture, humans have a hair-trigger tendency to attribute consciousness to everything around us. We attribute consciousness to characters in a story, puppets and dolls, storms, rivers, empty spaces, ghosts and gods. Justin Barrett called it the Hyperactive Agency Detection Device, or HADD

the HADD goes way beyond detecting predators. It’s a consequence of our hyper-social nature. Evolution turned up the amplitude on our tendency to model others and now we’re supremely attuned to each other’s mind states. It gives us our adaptive edge. The inevitable side effect is the detection of false positives, or ghosts.

In practical terms, the results meant that nonpreferred investigators had to submit higher quality grants to get money, whereas preferred investigators could get relatively lower quality grants funded. For instance, in a simulation that assumed that the top 10% of grants were funded, and that bias reduced the scores of nonpreferred applicants by an average of 3.7%, the preferred applicants got 118 funded grants, compared with just 82 from nonpreferred applicants.

We were led to wonder whether a broken robot can break a child.

Kids are central to the sociable-robot project, because its agenda is to make people more comfortable with robots in roles normally reserved for humans, and robotics companies know that children are vulnerable consumers who can bring the whole family along.

In October, Mattel scrapped plans for Aristotle — a kind of Alexa for the nursery, designed to accompany children as they progress from lullabies and bedtime stories through high school homework — after lawmakers and child advocacy groups argued that the data the device collected about children could be misused by Mattel, marketers, hackers and other third parties. I was part of that campaign: There is something deeply unsettling about encouraging children to confide in machines that are in turn sharing their conversations with countless others.

Recently, I opened my MIT mail and found a “call for subjects” for a study involving sociable robots that will engage children in conversation to “elicit empathy.” What will these children be empathizing with, exactly? Empathy is a capacity that allows us to put ourselves in the place of others, to know what they are feeling. Robots, however, have no emotions to share

What they can do is push our buttons. When they make eye contact and gesture toward us, they predispose us to view them as thinking and caring. They are designed to be cute, to provoke a nurturing response. And when it comes to sociable AI, nurturance is the killer app: We nurture what we love, and we love what we nurture. If a computational object or robot asks for our help, asks us to teach it or tend to it, we attach. That is our human vulnerability.

digital companions don’t understand our emotional lives. They present themselves as empathy machines, but they are missing the essential equipment: They have not known the arc of a life. They have not been born; they don’t know pain, or mortality, or fear. Simulated thinking may be thinking, but simulated feeling is never feeling, and simulated love is never love.

Breazeal’s position is this: People have relationships with many classes of things. They have relationships with children and with adults, with animals and with machines. People, even very little people, are good at this. Now, we are going to add robots to the list of things with which we can have relationships. More powerful than with pets. Less powerful than with people. We’ll figure it out.

The nature of the attachments to dolls and sociable machines is different. When children play with dolls, they project thoughts and emotions onto them. A girl who has broken her mother’s crystal will put her Barbies into detention and use them to work on her feelings of guilt. The dolls take the role she needs them to take.

Sociable machines, by contrast, have their own agenda. Playing with robots is not about the psychology of projection but the psychology of engagement. Children try to meet the robot’s needs, to understand the robot’s unique nature and wants. There is an attempt to build a mutual relationship.

Some people might consider that a good thing: encouraging children to think beyond their own needs and goals. Except the whole commercial program is an exercise in emotional deception.

when we offer these robots as pretend friends to our children, it’s not so clear they can wink with us. We embark on an experiment in which our children are the human subjects.

it is hard to imagine what those “right types” of ties might be. These robots can’t be in a two-way relationship with a child. They are machines whose art is to put children in a position of pretend empathy. And if we put our children in that position, we shouldn’t expect them to understand what empathy is. If we give them pretend relationships, we shouldn’t expect them to learn how real relationships — messy relationships — work. On the contrary. They will learn something superficial and inauthentic, but mistake it for real connection.

In the process, we can forget what is most central to our humanity: truly understanding each other.

For so long, we dreamed of artificial intelligence offering us not only instrumental help but the simple salvations of conversation and care. But now that our fantasy is becoming reality, it is time to confront the emotional downside of living with the robots of our dreams.

SEIR models start to fall apart when you think about the underlying causes of the data. You need models that can allow for all possible states, and assess which ones matter for shaping the pandemic’s trajectory over time.

These techniques have enjoyed enormous success ever since they moved out of physics. They’ve been running your iPhone and nuclear power stations for a long time. In my field, neurobiology, we call the approach dynamic causal modelling (DCM). We can’t see brain states directly, but we can infer them given brain imaging data

Epidemiologists currently tackle the inference problem by number-crunching on a huge scale, making use of high-performance computers. Imagine you want to simulate an outbreak in Scotland. Using conventional approaches, this would take you a day or longer with today’s computing resources. And that’s just to simulate one model or hypothesis – one set of parameters and one set of starting conditions.

Using DCM, you can do the same thing in a minute. That allows you to score different hypotheses quickly and easily, and so to home in sooner on the best one.

This is like dark matter in the universe: we can’t see it, but we know it must be there to account for what we can see. Knowing it exists is useful for our preparations for any second wave, because it suggests that targeted testing of those at high risk of exposure to Covid-19 might be a better approach than non-selective testing of the whole population.

Our response as individuals – and as a society – becomes part of the epidemiological process, part of one big self-organising, self-monitoring system. That means it is possible to predict not only numbers of cases and deaths in the future, but also societal and institutional responses – and to attach precise dates to those predictions.

How well have your predictions been borne out in this first wave of infections?For London, we predicted that hospital admissions would peak on 5 April, deaths would peak five days later, and critical care unit occupancy would not exceed capacity – meaning the Nightingale hospitals would not be required. We also predicted that improvements would be seen in the capital by 8 May that might allow social distancing measures to be relaxed – which they were in the prime minister’s announcement on 10 May. To date our predictions have been accurate to within a day or two, so there is a predictive validity to our models that the conventional ones lack.

What do your models say about the risk of a second wave?The models support the idea that what happens in the next few weeks is not going to have a great impact in terms of triggering a rebound – because the population is protected to some extent by immunity acquired during the first wave. The real worry is that a second wave could erupt some months down the line when that immunity wears off.

the important message is that we have a window of opportunity now, to get test-and-trace protocols in place ahead of that putative second wave. If these are implemented coherently, we could potentially defer that wave beyond a time horizon where treatments or a vaccine become available, in a way that we weren’t able to before the first one.

We’ve been comparing the UK and Germany to try to explain the comparatively low fatality rates in Germany. The answers are sometimes counterintuitive. For example, it looks as if the low German fatality rate is not due to their superior testing capacity, but rather to the fact that the average German is less likely to get infected and die than the average Brit. Why? There are various possible explanations, but one that looks increasingly likely is that Germany has more immunological “dark matter” – people who are impervious to infection, perhaps because they are geographically isolated or have some kind of natural resistance

Any other advantages?Yes. With conventional SEIR models, interventions and surveillance are something you add to the model – tweaks or perturbations – so that you can see their effect on morbidity and mortality. But with a generative model these things are built into the model itself, along with everything else that matters.

Are generative models the future of disease modelling?That’s a question for the epidemiologists – they’re the experts. But I would be very surprised if at least some part of the epidemiological community didn’t become more committed to this approach in future, given the impact that Feynman’s ideas have had in so many other disciplines.

during this same period, students' self-reported narcissism levels have shot through the roof. "Many people see the current group of college students, sometimes called 'Generation Me,' " Konrath continues, "as one of the most self-centered, narcissistic, competitive, confident, and individualistic in recent history."

Reading a book carves brand-new neural pathways into the ancient cortical bedrock of our brains. It transforms the way we see the world—makes us, as Nicholas Carr puts it in his recent essay, "The Dreams of Readers," "more alert to the inner lives of others." We become vampires without being bitten—in other words, more empathic. Books make us see in a way that casual immersion in the Internet, and the quicksilver virtual world it offers, doesn't.

if society really is becoming more psychopathic, it's not all doom and gloom. In the right context, certain psychopathic characteristics can actually be very constructive. A neurosurgeon I spoke with (who rated high on the psychopathic spectrum) described the mind-set he enters before taking on a difficult operation as "an intoxication that sharpens rather than dulls the senses." In fact, in any kind of crisis, the most effective individuals are often those who stay calm—who are able to respond to the exigencies of the moment while at the same time maintaining the requisite degree of detachment.

mental toughness isn't the only characteristic that Special Forces soldiers have in common with psychopaths. There's also fearlessness.

I ask Andy whether he ever felt any regret over anything he'd done. Over the lives he'd taken on his numerous secret missions around the world. "No," he replies matter-of-factly, his arctic-blue eyes showing not the slightest trace of emotion. "You seriously don't think twice about it. When you're in a hostile situation, the primary objective is to pull the trigger before the other guy pulls the trigger. And when you pull it, you move on. Simple as that. Why stand there, dwelling on what you've done? Go down that route and chances are the last thing that goes through your head will be a bullet from an M16. "The regiment's motto is 'Who Dares Wins.' But sometimes it can be shortened to 'F--- It.' "

one of the things that we know about psychopaths is that the light switches of their brains aren't wired up in quite the same way as the rest of ours are—and that one area particularly affected is the amygdala, a peanut-size structure located right at the center of the circuit board. The amygdala is the brain's emotion-control tower. It polices our emotional airspace and is responsible for the way we feel about things. But in psychopaths, a section of this airspace, the part that corresponds to fear, is empty.

Turn down the signals to the amygdala, of course, and you're well on the way to giving someone a psychopath makeover. Indeed, Liane Young and her team in Boston have since kicked things up a notch and demonstrated that applying TMS to the right temporoparietal junction—a neural ZIP code within that neighborhood—has significant effects not just on lying ability but also on moral-reasoning ability: in particular, ascribing intentionality to others' actions.

at an undisclosed moment sometime within the next 60 seconds, the image you see at the present time will change, and images of a different nature will appear on the screen. These images will be violent. And nauseating. And of a graphic and disturbing nature. "As you view these images, changes in your heart rate, skin conductance, and EEG activity will be monitored and compared with the resting levels that are currently being recorded

"OK," says Nick. "Let's get the show on the road." He disappears behind us, leaving Andy and me merrily soaking up the incontinence ad. Results reveal later that, at this point, as we wait for something to happen, our physiological output readings are actually pretty similar. Our pulse rates are significantly higher than our normal resting levels, in anticipation of what's to come. But with the change of scene, an override switch flips somewhere in Andy's brain. And the ice-cold Special Forces soldier suddenly swings into action. As vivid, florid images of dismemberment, mutilation, torture, and execution flash up on the screen in front of us (so vivid, in fact, that Andy later confesses to actually being able to "smell" the blood: a "kind of sickly-sweet smell that you never, ever forget"), accompanied not by the ambient spa music of before but by blaring sirens and hissing white noise, his physiological readings start slipping into reverse. His pulse rate begins to slow. His GSR begins to drop, his EEG to quickly and dramatically attenuate. In fact, by the time the show is over, all three of Andy's physiological output measures are pooling below his baseline.

Nick has seen nothing like it. "It's almost as if he was gearing himself up for the challenge," he says. "And then, when the challenge eventually presented itself, his brain suddenly responded by injecting liquid nitrogen into his veins. Suddenly implemented a blanket neural cull of all surplus feral emotion. Suddenly locked down into a hypnotically deep code red of extreme and ruthless focus." He shakes his head, nonplused. "If I hadn't recorded those readings myself, I'm not sure I would have believed them," he continues. "OK, I've never tested Special Forces before. And maybe you'd expect a slight attenuation in response. But this guy was in total and utter control of the situation. So tuned in, it looked like he'd completely tuned out."

My physiological output readings, in contrast, went through the roof. Exactly like Andy's, they were well above baseline as I'd waited for the carnage to commence. But that's where the similarity ended. Rather than go down in the heat of battle, in the midst of the blood and guts, mine had appreciated exponentially. "At least it shows that the equipment is working properly," comments Nick. "And that you're a normal human being."

TMS can't penetrate far enough into the brain to reach the emotion and moral-reasoning precincts directly. But by damping down or turning up the regions of the cerebral cortex that have links with such areas, it can simulate the effects of deeper, more incursive influence.

Before the experiment, I'd been curious about the time scale: how long it would take me to begin to feel the rush. Now I had the answer: about 10 to 15 minutes. The same amount of time, I guess, that it would take most people to get a buzz out of a beer or a glass of wine.

The effects aren't entirely dissimilar. An easy, airy confidence. A transcendental loosening of inhibition. The inchoate stirrings of a subjective moral swagger: the encroaching, and somehow strangely spiritual, realization that hell, who gives a s---, anyway? There is, however, one notable exception. One glaring, unmistakable difference between this and the effects of alcohol. That's the lack of attendant sluggishness. The enhancement of attentional acuity and sharpness. An insuperable feeling of heightened, polished awareness. Sure, my conscience certainly feels like it's on ice, and my anxieties drowned with a half-dozen shots of transcranial magnetic Jack Daniel's. But, at the same time, my whole way of being feels as if it's been sumptuously spring-cleaned with light. My soul, or whatever you want to call it, immersed in a spiritual dishwasher.

So this, I think to myself, is how it feels to be a psychopath. To cruise through life knowing that no matter what you say or do, guilt, remorse, shame, pity, fear—all those familiar, everyday warning signals that might normally light up on your psychological dashboard—no longer trouble you.

I suddenly get a flash of insight. We talk about gender. We talk about class. We talk about color. And intelligence. And creed. But the most fundamental difference between one individual and another must surely be that of the presence, or absence, of conscience. Conscience is what hurts when everything else feels good. But what if it's as tough as old boots? What if one's conscience has an infinite, unlimited pain threshold and doesn't bat an eye when others are screaming in agony?

It's a new paradigm for a physical reality.

that memory is not a video recorder [but that] it’s a constructive activity that is in many ways accurate but prone to interesting errors and distortions. It’s the constructive side of memory that is most relevant to historians.

Is it the case then that our memories constantly change every time we access them?

That certainly can happen depending on how you recount a memory. What you emphasize. What you exaggerate. What you don’t talk about. All of those things will shape and sculpt the memory for future use. Certainly the potential is there.

Research on memory shows that the more distant in time the event, the more prone to inaccuracy the memory. There are several experiments when subjects recorded impressions of an event soon afterward, then a year later and then a few years later, and the memory changed.Yes. It’s not that the information is lost but, as the memory weakens, you become more prone to incorporating other kinds of information or mixing up elements of other events. This has been seen, for example, in the study of flashbulb memories. Where were you when Kennedy was shot? Where were you when you heard about 9/11?

Isn’t there a tendency to add details or information that may make the story more convincing or interesting later?Yes. That’s more a social function of memory. It may be that you draw on your general knowledge and probable information from your memory in a social context where there may be social demands that lead you distort the memory.

What are the different memory systems?

What is the difference between working memory and permanent memory?Working memory is really a temporary memory buffer where you hold onto information, manipulate information, use it, and it’s partly a gateway to long-term memory and also a buffer that you use when you’re retrieving information from long-term memory and that information temporarily resides in working memory, so to speak.

Your discussion of the testimony of White House Counsel John Dean about Watergate is illuminating. There was a perception that Dean had a photographic memory and he testified in rich detail about events. Yet later studies of White House tape recordings revealed that he was often inaccurate.

He was perceived because of all the detail with which he reported events and the great confidence to be something analogous to a human tape recorder. Yet there was interesting work done by psychologist Ulric Neisser who went back and analyzed what Dean said at the hearings as compared to available information on the White House taping system and basically found many and significant discrepancies between what Dean remembered and what was actually said. He usually had the gist and the meaning and overall significance right, but the exact details were often quite different in his memory than what actually was said.

That seems to get into the area of false memories and how they present problems in the legal system.We know from DNA exonerations of people wrongfully convicted of crimes that a large majority of those cases -- one of the more recent estimates is that in the first 250 cases of 2011 DNA exonerations, roughly 70 to 75 percent of those individuals were convicted on the basis of faulty eyewitness memory.

One of the interesting recent lines of research that my lab has been involved in over the past few years has been looking at similarities between what goes on between the brain and mind when we remember past events on the one hand and imagine events that might occur in the future or might have occurred in the past. What we have found, particularly with brain scanning studies, is that you get very similar brain networks coming online when you remember past events and imagine future events, for example. Many of the same brain regions or network of structures come online, and this has helped us understand more why, for example, imagining events that might have occurred can be so harmful to memory accuracy because when you imagine, you’re recruiting many of the same brain regions as accessed when you actually remember. So it’s not surprising that some of these imagined events can actually turn into false memories under the right circumstances.

One reasonably well accepted distinction involves episodic memory, the memory for personal experience; semantic memory, the memory for general knowledge; and procedural memory, the memory for skills and unconscious forms of memory.Those are three of the major kinds of memory and they all have different neural substrates.

One of the points from that Ross Perot study is that his supporters often misremembered what they felt like at the time he reported he had dropped out of the race. The nature of that misremembering depended on their state at the time they were remembering and what decisions they had made about Perot in the interim affected how they reconstructed their earlier memories.Again, that makes nicely the point that our current emotions and current appraisals of a situation can feed back into our reconstruction of the past and sometimes lead us to distort our memories so that they better support our current emotions and our current selves. We’re often using memories to justify what we currently know, believe and feel.

memory doesn’t work like a video camera or tape recorder.That is the main point. Our latest thinking on this is the idea that one of the major functions of memory is to support our ability to plan for the future, to imagine the future, and to use our past experiences in a flexible way to simulate different outcomes of events.

flexibility of memory is something that makes it useful to support this very important ability to run simulations of future events. But that very flexibility might be something that contributes to some of the memory distortion we talked about. That has been prominent in the last few years in my thinking about the constructive nature of memory.

The historian Daniel Aaron told his students “we remember what’s important.” What do you think of that comment?I think that generally holds true. Certainly, again, more important memories tend to be more significant with more emotional arousal and may elicit “deeper processing”, as we call it in cognitive psychology

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.

“I couldn’t tolerate knowing something was fake and not doing something about it,” he told me. “Everything loses meaning. What’s the point of writing a paper, fighting very hard to get it published, going to conferences?”

Simonsohn stressed that there’s a world of difference between data techniques that generate false positives, and fraud, but he said some academic psychologists have, until recently, been dangerously indifferent to both. Outright fraud is probably rare. Data manipulation is undoubtedly more common—and surely extends to other subjects dependent on statistical study, including biomedicine. Worse, sloppy statistics are “like steroids in baseball”: Throughout the affected fields, researchers who are too intellectually honest to use these tricks will publish less, and may perish. Meanwhile, the less fastidious flourish.

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.

Both blacks and whites subtly try to get a white partner when asked to team up to do an intellectually difficult task. In computer shooting simulations, both black and white participants were more likely to think black figures were armed. In emergency rooms, whites are pervasively given stronger painkillers than blacks or Hispanics.

Clearly, we should spend more effort rigging situations to reduce universal, unconscious racism.

Over the course of many generations, the gene pool thickens with helpful alleles until—voila!—the overwhelming number of these alleles are helpful and learners guesses are so uncannily accurate as to seem instinctual. Makes sense, no? But now consider that languages change. (And in the real world they do—quickly.) If the language’s principles switch often, many of those helpfully biased alleles are suddenly not so helpful at all. For fast-changing languages, the model finds, neutral alleles win out:

when the language is programmed to hardly mutate at all, the genes have a chance to adapt to the new language. The two populations become genetically distinct, their alleles heavily biased toward the idiosyncrasies of their local language—precisely what we don’t see in the real world

when the language is programmed to change quickly, neutral alleles are again favored.

maybe our brains couldn’t have evolved to handle language’s more arbitrary properties, because languages never stay the same and, as far as we know, they never have. What goes unspoken here is that the simulations seem to suggest that truly universal properties—such as language’s hierarchical nature—could have been encoded in our brains.

But revealingly, when it came to seeing the superior three-star players having the needle injection, the participants' AI and aMCC were eerily quiet. In other words, their brain's automatic empathic response was apparently dampened.

Moreover, when viewing inferior players' suffering, but not the suffering of superior players, the participants' brains showed increased communication between the AI and other regions involved in empathy and perspective-taking

putting themselves mentally and emotionally in the position of the inferior players, but not the superior.

The researchers didn't find any neural evidence that their participants enjoyed watching three-star players' suffering. However, the results do suggest that the automatic simulation of others' pain that normally goes on in our brains was dampened when participants saw a superior player suffering.

It just goes to show how competitive we are by nature and how quick we are to measure ourselves in relation to others

The researchers think the reduced neural empathy we show toward superior people is somehow linked to the way they make us feel bad about ourselves

Of course, it's worth bearing in mind that, like most social neuroscience research, this study involves making a lot of assumptions about the meaning of people's brain-activity patterns. It certainly seems as if the participants were overstating the empathy they felt for the superior players, and that their brains gave away their true feelings. But this is just one interpretation of the results.

It's also a shame, from a methodological point of view, that there wasn't a condition in which the participants looked at equal-status players in pain.

These issues aside, the new results are consistent with, and add to, past research that's shown people's neural empathic responses are diminished when witnessing pain endured by someone they dislike, or someone from a different social group.

We can strive to be good people, but sadly it seems our brains often reveal the darker side of human nature.