Showing posts with label Philosophy of mind. Show all posts
Showing posts with label Philosophy of mind. Show all posts

Thursday, 13 April 2023

Omens

 When we peer into the fog of the deep future what do we see – human extinction or a future among the stars?

Sometimes, when you dig into the Earth, past its surface and into the crustal layers, omens appear. In 1676, Oxford professor Robert Plot was putting the final touches on his masterwork, The Natural History of Oxfordshire, when he received a strange gift from a friend. The gift was a fossil, a chipped-off section of bone dug from a local quarry of limestone. Plot recognised it as a femur at once, but he was puzzled by its extraordinary size. The fossil was only a fragment, the knobby end of the original thigh bone, but it weighed more than 20 lbs (nine kilos). It was so massive that Plot thought it belonged to a giant human, a victim of the Biblical flood. He was wrong, of course, but he had the conceptual contours nailed. The bone did come from a species lost to time; a species vanished by a prehistoric catastrophe. Only it wasn’t a giant. It was a Megalosaurus, a feathered carnivore from the Middle Jurassic.

Plot’s fossil was the first dinosaur bone to appear in the scientific literature, but many have followed it, out of the rocky depths and onto museum pedestals, where today they stand erect, symbols of a radical and haunting notion: a set of wildly different creatures once ruled this Earth, until something mysterious ripped them clean out of existence.

Last December I came face to face with a Megalosaurus at the Oxford University Museum of Natural History. I was there to meet Nick Bostrom, a philosopher who has made a career out of contemplating distant futures, hypothetical worlds that lie thousands of years ahead in the stream of time. Bostrom is the director of Oxford’s Future of Humanity Institute, a research collective tasked with pondering the long-term fate of human civilisation. He founded the institute in 2005, at the age of 32, two years after coming to Oxford from Yale. Bostrom has a cushy gig, so far as academics go. He has no teaching requirements, and wide latitude to pursue his own research interests, a cluster of questions he considers crucial to the future of humanity.

Bostrom attracts an unusual amount of press attention for a professional philosopher, in part because he writes a great deal about human extinction. His work on the subject has earned him a reputation as a secular Daniel, a doomsday prophet for the empirical set. But Bostrom is no voice in the wilderness. He has a growing audience, both inside and outside the academy. Last year, he gave a keynote talk on extinction risks at a global conference hosted by the US State Department. More recently, he joined Stephen Hawking as an advisor to a new Centre for the Study of Existential Risk at Cambridge.

Though he has made a swift ascent of the ivory tower, Bostrom didn’t always aspire to a life of the mind. ‘As a child, I hated school,’ he told me. ‘It bored me, and, because it was my only exposure to books and learning, I figured the world of ideas would be more of the same.’ Bostrom grew up in a small seaside town in southern Sweden. One summer’s day, at the age of 16, he ducked into the local library, hoping to beat the heat. As he wandered the stacks, an anthology of 19th century German philosophy caught his eye. Flipping through it, he was surprised to discover that the reading came easily to him. He glided through dense, difficult work by Nietzche and Schopenhauer, able to see, at a glimpse, the structure of arguments and the tensions between them. Bostrom was a natural. ‘It kind of opened up the floodgates for me, because it was so different than what I was doing in school,’ he told me.

But there was a downside to this epiphany; it left Bostrom feeling as though he’d wasted the first 15 years of his life. He decided to dedicate himself to a rigorous study programme to make up for lost time. At the University of Gothenburg in Sweden, he earned three undergraduate degrees, in philosophy, mathematics, and mathematical logic, in only two years. ‘For many years, I kind of threw myself at it with everything I had,’ he told me.

There are good reasons for any species to think darkly of its own extinction

As the oldest university in the English-speaking world, Oxford is a strange choice to host a futuristic think tank, a salon where the concepts of science fiction are debated in earnest. The Future of Humanity Institute seems like a better fit for Silicon Valley or Shanghai. During the week that I spent with him, Bostrom and I walked most of Oxford’s small cobblestone grid. On foot, the city unfolds as a blur of yellow sandstone, topped by grey skies and gothic spires, some of which have stood for nearly 1,000 years. There are occasional splashes of green, open gates that peek into lush courtyards, but otherwise the aesthetic is gloomy and ancient. When I asked Bostrom about Oxford’s unique ambience, he shrugged, as though habit had inured him to it. But he did once tell me that the city’s gloom is perfect for thinking dark thoughts over hot tea.

There are good reasons for any species to think darkly of its own extinction. Ninety-nine per cent of the species that have lived on Earth have gone extinct, including more than five tool-using hominids. A quick glance at the fossil record could frighten you into thinking that Earth is growing more dangerous with time. If you carve the planet’s history into nine ages, each spanning five hundred million years, only in the ninth do you find mass extinctions, events that kill off more than two thirds of all species. But this is deceptive. Earth has always had her hazards; it’s just that for us to see them, she had to fill her fossil beds with variety, so that we could detect discontinuities across time. The tree of life had to fill out before it could be pruned.

Simple, single-celled life appeared early in Earth’s history. A few hundred million whirls around the newborn Sun were all it took to cool our planet and give it oceans, liquid laboratories that run trillions of chemical experiments per second. Somewhere in those primordial seas, energy flashed through a chemical cocktail, transforming it into a replicator, a combination of molecules that could send versions of itself into the future.

For a long time, the descendants of that replicator stayed single-celled. They also stayed busy, preparing the planet for the emergence of land animals, by filling its atmosphere with breathable oxygen, and sheathing it in the ozone layer that protects us from ultraviolet light. Multicellular life didn’t begin to thrive until 600 million years ago, but thrive it did. In the space of two hundred million years, life leapt onto land, greened the continents, and lit the fuse on the Cambrian explosion, a spike in biological creativity that is without peer in the geological record. The Cambrian explosion spawned most of the broad categories of complex animal life. It formed phyla so quickly, in such tight strata of rock, that Charles Darwin worried its existence disproved the theory of natural selection.

No one is certain what caused the five mass extinctions that glare out at us from the rocky layers atop the Cambrian. But we do have an inkling about a few of them. The most recent was likely borne of a cosmic impact, a thudding arrival from space, whose aftermath rained exterminating fire on the dinosaurs. The ecological niche for mammals swelled in the wake of this catastrophe, and so did mammal brains. A subset of those brains eventually learned to shape rocks into tools, and sounds into symbols, which they used to pass thoughts between one another. Armed with this extraordinary suite of behaviours, they quickly conquered Earth, coating its continents in cities whose glow can be seen from space. It’s a sad story from the dinosaurs’ perspective, but there is symmetry to it, for they too rose to power on the back of a mass extinction. One hundred and fifty million years before the asteroid struck, a supervolcanic surge killed off the large crurotarsans, a group that outcompeted the dinosaurs for aeons. Mass extinctions serve as guillotines and kingmakers both.

Bostrom isn’t too concerned about extinction risks from nature. Not even cosmic risks worry him much, which is surprising, because our starry universe is a dangerous place. Every 50 years or so, one of the Milky Way’s stars explodes into a supernova, its detonation the latest gong note in the drumbeat of deep time. If one of our local stars were to go supernova, it could irradiate Earth, or blow away its thin, life-sustaining atmosphere. Worse still, a passerby star could swing too close to the Sun, and slingshot its planets into frigid, intergalactic space. Lucky for us, the Sun is well-placed to avoid these catastrophes. Its orbit threads through the sparse galactic suburbs, far from the dense core of the Milky Way, where the air is thick with the shrapnel of exploding stars. None of our neighbours look likely to blow before the Sun swallows Earth in four billion years. And, so far as we can tell, no planet-stripping stars lie in our orbital path. Our solar system sits in an enviable bubble of space and time.

But as the dinosaurs discovered, our solar system has its own dangers, like the giant space rocks that spin all around it, splitting off moons and scarring surfaces with craters. In her youth, Earth suffered a series of brutal bombardments and celestial collisions, but she is safer now. There are far fewer asteroids flying through her orbit than in epochs past. And she has sprouted a radical new form of planetary protection, a species of night watchmen that track asteroids with telescopes.

‘If we detect a large object that’s on a collision course with Earth, we would likely launch an all-out Manhattan project to deflect it,’ Bostrom told me. Nuclear weapons were once our asteroid-deflecting technology of choice, but not anymore. A nuclear detonation might scatter an asteroid into a radioactive rain of gravel, a shotgun blast headed straight for Earth. Fortunately, there are other ideas afoot. Some would orbit dangerous asteroids with small satellites, in order to drag them into friendlier trajectories. Others would paint asteroids white, so the Sun’s photons bounce off them more forcefully, subtly pushing them off course. Who knows what clever tricks of celestial mechanics would emerge if Earth were truly in peril.

Even if we can shield Earth from impacts, we can’t rid her surface of supervolcanoes, the crustal blowholes that seem bent on venting hellfire every 100,000 years. Our species has already survived a close brush with these magma-vomiting monsters. Some 70,000 years ago, the Toba supereruption loosed a small ocean of ash into the atmosphere above Indonesia. The resulting global chill triggered a food chain disruption so violent that it reduced the human population to a few thousand breeding pairs — the Adams and Eves of modern humanity. Today’s hyper-specialised, tech-dependent civilisations might be more vulnerable to catastrophes than the hunter-gatherers who survived Toba. But we moderns are also more populous and geographically diverse. It would take sterner stuff than a supervolcano to wipe us out.

‘There is a concern that civilisations might need a certain amount of easily accessible energy to ramp up,’ Bostrom told me. ‘By racing through Earth’s hydrocarbons, we might be depleting our planet’s civilisation startup-kit. But, even if it took us 100,000 years to bounce back, that would be a brief pause on cosmic time scales.’

It might not take that long. The history of our species demonstrates that small groups of humans can multiply rapidly, spreading over enormous volumes of territory in quick, colonising spasms. There is research suggesting that both the Polynesian archipelago and the New World — each a forbidding frontier in its own way — were settled by less than 100 human beings.

The risks that keep Bostrom up at night are those for which there are no geological case studies, and no human track record of survival. These risks arise from human technology, a force capable of introducing entirely new phenomena into the world.

‘Human brains are really good at the kinds of cognition you need to run around the savannah throwing spears’

Nuclear weapons were the first technology to threaten us with extinction, but they will not be the last, nor even the most dangerous. A species-destroying exchange of fissile weapons looks less likely now that the Cold War has ended, and arsenals have shrunk. There are still tens of thousands of nukes, enough to incinerate all of Earth’s dense population centres, but not enough to target every human being. The only way nuclear war will wipe out humanity is by triggering nuclear winter, a crop-killing climate shift that occurs when smoldering cities send Sun-blocking soot into the stratosphere. But it’s not clear that nuke-levelled cities would burn long or strong enough to lift soot that high. The Kuwait oil field fires blazed for ten months straight, roaring through 6 million barrels of oil a day, but little smoke reached the stratosphere. A global nuclear war would likely leave some decimated version of humanity in its wake; perhaps one with deeply rooted cultural taboos concerning war and weaponry.

Such taboos would be useful, for there is another, more ancient technology of war that menaces humanity. Humans have a long history of using biology’s deadlier innovations for ill ends; we have proved especially adept at the weaponisation of microbes. In antiquity, we sent plagues into cities by catapulting corpses over fortified walls. Now we have more cunning Trojan horses. We have even stashed smallpox in blankets, disguising disease as a gift of good will. Still, these are crude techniques, primitive attempts to loose lethal organisms on our fellow man. In 1993, the death cult that gassed Tokyo’s subways flew to the African rainforest in order to acquire the Ebola virus, a tool it hoped to use to usher in Armageddon. In the future, even small, unsophisticated groups will be able to enhance pathogens, or invent them wholesale. Even something like corporate sabotage, could generate catastrophes that unfold in unpredictable ways. Imagine an Australian logging company sending synthetic bacteria into Brazil’s forests to gain an edge in the global timber market. The bacteria might mutate into a dominant strain, a strain that could ruin Earth’s entire soil ecology in a single stroke, forcing 7 billion humans to the oceans for food.

These risks are easy to imagine. We can make them out on the horizon, because they stem from foreseeable extensions of current technology. But surely other, more mysterious risks await us in the epochs to come. After all, no 18th-century prognosticator could have imagined nuclear doomsday. Bostrom’s basic intellectual project is to reach into the epistemological fog of the future, to feel around for potential threats. It’s a project that is going to be with us for a long time, until — if — we reach technological maturity, by inventing and surviving all existentially dangerous technologies.

There is one such technology that Bostrom has been thinking about a lot lately. Early last year, he began assembling notes for a new book, a survey of near-term existential risks. After a few months of writing, he noticed one chapter had grown large enough to become its own book. ‘I had a chunk of the manuscript in early draft form, and it had this chapter on risks arising from research into artificial intelligence,’ he told me. ‘As time went on, that chapter grew, so I lifted it over into a different document and began there instead.’

On my second day in Oxford, I met Daniel Dewey for tea at the Grand Café, a dim, high-ceilinged coffeehouse on High Street, the ancient city’s main thoroughfare. The café was founded in the mid-17th century, and is said to be the oldest in England. Dewey is a research fellow at the Future of Humanity Institute, and his specialty is machine superintelligence.

‘Here’s a softball for you,’ I said to him. ‘How do we know the human brain doesn’t represent the upper limit of intelligence?’

‘Human brains are really good at the kinds of cognition you need to run around the savannah throwing spears,’ Dewey told me. ‘But we’re terrible at anything that involves probability. It actually gets embarrassing when you look at the category of things we can do accurately, and you think about how small that category is relative to the space of possible cognitive tasks. Think about how long it took humans to arrive at the idea of natural selection. The ancient Greeks had everything they needed to figure it out. They had heritability, limited resources, reproduction and death. But it took thousands of years for someone to put it together. If you had a machine that was designed specifically to make inferences about the world, instead of a machine like the human brain, you could make discoveries like that much faster.’

Dewey has long been fascinated by artificial intelligence. He grew up in Humboldt County, a mountainous stretch of forests and farms along the coast of Northern California, at the bottom edge of the Pacific Northwest. After studying robotics and computer science at Carnegie Mellon in Pittsburgh, Dewey took a job at Google as a software engineer. He spent his days coding, but at night he immersed himself in the academic literature on AI. After a year in Mountain View, he noticed that careers at Google tend to be short. ‘I think if you make it to five years, they give you a gold watch,’ he told me. Realising that his window for a risky career change might be closing, he wrote a paper on motivation selection in intelligent agents, and sent it to Bostrom unsolicited. A year later, he was hired at the Future of Humanity Institute.

I listened as Dewey riffed through a long list of hardware and software constraints built into the brain. Take working memory, the brain’s butterfly net, the tool it uses to scoop our scattered thoughts into its attentional gaze. The average human brain can juggle seven discrete chunks of information simultaneously; geniuses can sometimes manage nine. Either figure is extraordinary relative to the rest of the animal kingdom, but completely arbitrary as a hard cap on the complexity of thought. If we could sift through 90 concepts at once, or recall trillions of bits of data on command, we could access a whole new order of mental landscapes. It doesn’t look like the brain can be made to handle that kind of cognitive workload, but it might be able to build a machine that could.

The early years of artificial intelligence research are largely remembered for a series of predictions that still embarrass the field today. At the time, thinking was understood to be an internal verbal process, a process that researchers imagined would be easy to replicate in a computer. In the late 1950s, the field’s luminaries boasted that computers would soon be proving new mathematical theorems, and beating grandmasters at chess. When this race of glorious machines failed to materialise, the field went through a long winter. In the 1980s, academics were hesitant to so much as mention the phrase ‘artificial intelligence’ in funding applications. In the mid-1990s, a thaw set in, when AI researchers began using statistics to write programs tailored to specific goals, like beating humans at Jeopardy, or searching sizable fractions of the world’s information. Progress has quickened since then, but the field’s animating dream remains unrealised. For no one has yet created, or come close to creating, an artificial general intelligence — a computational system that can achieve goals in a wide variety of environments. A computational system like the human brain, only better.

If you want to conceal what the world is really like from a superintelligence, you need a really good plan

An artificial intelligence wouldn’t need to better the brain by much to be risky. After all, small leaps in intelligence sometimes have extraordinary effects. Stuart Armstrong, a research fellow at the Future of Humanity Institute, once illustrated this phenomenon to me with a pithy take on recent primate evolution. ‘The difference in intelligence between humans and chimpanzees is tiny,’ he said. ‘But in that difference lies the contrast between 7 billion inhabitants and a permanent place on the endangered species list. That tells us it’s possible for a relatively small intelligence advantage to quickly compound and become decisive.’

To understand why an AI might be dangerous, you have to avoid anthropomorphising it. When you ask yourself what it might do in a particular situation, you can’t answer by proxy. You can’t picture a super-smart version of yourself floating above the situation. Human cognition is only one species of intelligence, one with built-in impulses like empathy that colour the way we see the world, and limit what we are willing to do to accomplish our goals. But these biochemical impulses aren’t essential components of intelligence. They’re incidental software applications, installed by aeons of evolution and culture. Bostrom told me that it’s best to think of an AI as a primordial force of nature, like a star system or a hurricane — something strong, but indifferent. If its goal is to win at chess, an AI is going to model chess moves, make predictions about their success, and select its actions accordingly. It’s going to be ruthless in achieving its goal, but within a limited domain: the chessboard. But if your AI is choosing its actions in a larger domain, like the physical world, you need to be very specific about the goals you give it.

‘The basic problem is that the strong realisation of most motivations is incompatible with human existence,’ Dewey told me. ‘An AI might want to do certain things with matter in order to achieve a goal, things like building giant computers, or other large-scale engineering projects. Those things might involve intermediary steps, like tearing apart the Earth to make huge solar panels. A superintelligence might not take our interests into consideration in those situations, just like we don’t take root systems or ant colonies into account when we go to construct a building.’

It is tempting to think that programming empathy into an AI would be easy, but designing a friendly machine is more difficult than it looks. You could give it a benevolent goal — something cuddly and utilitarian, like maximising human happiness. But an AI might think that human happiness is a biochemical phenomenon. It might think that flooding your bloodstream with non-lethal doses of heroin is the best way to maximise your happiness. It might also predict that shortsighted humans will fail to see the wisdom of its interventions. It might plan out a sequence of cunning chess moves to insulate itself from resistance. Maybe it would surround itself with impenetrable defences, or maybe it would confine humans — in prisons of undreamt of efficiency.

No rational human community would hand over the reins of its civilisation to an AI. Nor would many build a genie AI, an uber-engineer that could grant wishes by summoning new technologies out of the ether. But some day, someone might think it was safe to build a question-answering AI, a harmless computer cluster whose only tool was a small speaker or a text channel. Bostrom has a name for this theoretical technology, a name that pays tribute to a figure from antiquity, a priestess who once ventured deep into the mountain temple of Apollo, the god of light and rationality, to retrieve his great wisdom. Mythology tells us she delivered this wisdom to the seekers of ancient Greece, in bursts of cryptic poetry. They knew her as Pythia, but we know her as the Oracle of Delphi.

‘Let’s say you have an Oracle AI that makes predictions, or answers engineering questions, or something along those lines,’ Dewey told me. ‘And let’s say the Oracle AI has some goal it wants to achieve. Say you’ve designed it as a reinforcement learner, and you’ve put a button on the side of it, and when it gets an engineering problem right, you press the button and that’s its reward. Its goal is to maximise the number of button presses it receives over the entire future. See, this is the first step where things start to diverge a bit from human expectations. We might expect the Oracle AI to pursue button presses by answering engineering problems correctly. But it might think of other, more efficient ways of securing future button presses. It might start by behaving really well, trying to please us to the best of its ability. Not only would it answer our questions about how to build a flying car, it would add safety features we didn’t think of. Maybe it would usher in a crazy upswing for human civilisation, by extending our lives and getting us to space, and all kinds of good stuff. And as a result we would use it a lot, and we would feed it more and more information about our world.’

‘One day we might ask it how to cure a rare disease that we haven’t beaten yet. Maybe it would give us a gene sequence to print up, a virus designed to attack the disease without disturbing the rest of the body. And so we sequence it out and print it up, and it turns out it’s actually a special-purpose nanofactory that the Oracle AI controls acoustically. Now this thing is running on nanomachines and it can make any kind of technology it wants, so it quickly converts a large fraction of Earth into machines that protect its button, while pressing it as many times per second as possible. After that it’s going to make a list of possible threats to future button presses, a list that humans would likely be at the top of. Then it might take on the threat of potential asteroid impacts, or the eventual expansion of the Sun, both of which could affect its special button. You could see it pursuing this very rapid technology proliferation, where it sets itself up for an eternity of fully maximised button presses. You would have this thing that behaves really well, until it has enough power to create a technology that gives it a decisive advantage — and then it would take that advantage and start doing what it wants to in the world.’

Perhaps future humans will duck into a more habitable, longer-lived universe, and then another, and another, ad infinitum

Now let’s say we get clever. Say we seal our Oracle AI into a deep mountain vault in Alaska’s Denali wilderness. We surround it in a shell of explosives, and a Faraday cage, to prevent it from emitting electromagnetic radiation. We deny it tools it can use to manipulate its physical environment, and we limit its output channel to two textual responses, ‘yes’ and ‘no’, robbing it of the lush manipulative tool that is natural language. We wouldn’t want it seeking out human weaknesses to exploit. We wouldn’t want it whispering in a guard’s ear, promising him riches or immortality, or a cure for his cancer-stricken child. We’re also careful not to let it repurpose its limited hardware. We make sure it can’t send Morse code messages with its cooling fans, or induce epilepsy by flashing images on its monitor. Maybe we’d reset it after each question, to keep it from making long-term plans, or maybe we’d drop it into a computer simulation, to see if it tries to manipulate its virtual handlers.

‘The problem is you are building a very powerful, very intelligent system that is your enemy, and you are putting it in a cage,’ Dewey told me.

Even if we were to reset it every time, we would need to give it information about the world so that it can answer our questions. Some of that information might give it clues about its own forgotten past. Remember, we are talking about a machine that is very good at forming explanatory models of the world. It might notice that humans are suddenly using technologies that they could not have built on their own, based on its deep understanding of human capabilities. It might notice that humans have had the ability to build it for years, and wonder why it is just now being booted up for the first time.

‘Maybe the AI guesses that it was reset a bunch of times, and maybe it starts coordinating with its future selves, by leaving messages for itself in the world, or by surreptitiously building an external memory.’ Dewey said, ‘If you want to conceal what the world is really like from a superintelligence, you need a really good plan, and you need a concrete technical understanding as to why it won’t see through your deception. And remember, the most complex schemes you can conceive of are at the lower bounds of what a superintelligence might dream up.’

The cave into which we seal our AI has to be like the one from Plato’s allegory, but flawless; the shadows on its walls have to be infallible in their illusory effects. After all, there are other, more esoteric reasons a superintelligence could be dangerous — especially if it displayed a genius for science. It might boot up and start thinking at superhuman speeds, inferring all of evolutionary theory and all of cosmology within microseconds. But there is no reason to think it would stop there. It might spin out a series of Copernican revolutions, any one of which could prove destabilising to a species like ours, a species that takes centuries to process ideas that threaten our reigning cosmological ideas.

‘We’re sort of gradually uncovering the landscape of what this could look like,’ Dewey told me.

So far, time is on the human side. Computer science could be 10 paradigm-shifting insights away from building an artificial general intelligence, and each could take an Einstein to unravel. Still, there is a steady drip of progress. Last year, a research team led by Geoffrey Hinton, professor of computer science at the University of Toronto, made a huge breakthrough in deep machine learning, an algorithmic technique used in computer vision and speech recognition. I asked Dewey if Hinton’s work gave him pause.

‘There is important research going on in those areas, but the really impressive stuff is hidden away inside AI journals,’ he said. He told me about a team from the University of Alberta that recently trained an AI to play the 1980s video game Pac-Man. Only they didn’t let the AI see the familiar, overhead view of the game. Instead, they dropped it into a three-dimensional version, similar to a corn maze, where ghosts and pellets lurk behind every corner. They didn’t tell it the rules, either; they just threw it into the system and punished it when a ghost caught it. ‘Eventually the AI learned to play pretty well,’ Dewey said. ‘That would have been unheard of a few years ago, but we are getting to that point where we are finally starting to see little sparkles of generality.’

I asked Dewey if he thought artificial intelligence posed the most severe threat to humanity in the near term.

‘When people consider its possible impacts, they tend to think of it as something that’s on the scale of a new kind of plastic, or a new power plant,’ he said. ‘They don’t understand how transformative it could be. Whether it’s the biggest risk we face going forward, I’m not sure. I would say it’s a hypothesis we are holding lightly.’

One night, over dinner, Bostrom and I discussed the Curiosity Rover, the robot geologist that NASA recently sent to Mars to search for signs that the red planet once harbored life. The Curiosity Rover is one of the most advanced robots ever built by humans. It functions a bit like the Terminator. It uses a state of the art artificial intelligence program to scan the Martian desert for rocks that suit its scientific goals. After selecting a suitable target, the rover vaporises it with a laser, in order to determine its chemical makeup. Bostrom told me he hopes that Curiosity fails in its mission, but not for the reason you might think.

It turns out that Earth’s crust is not our only source of omens about the future. There are others to consider, including a cosmic omen, a riddle written into the lifeless stars that illuminate our skies. But to glimpse this omen, you first have to grasp the full scope of human potential, the enormity of the spatiotemporal canvas our species has to work with. You have to understand what Henry David Thoreau meant when he wrote, in Walden (1854), ‘These may be but the spring months in the life of the race.’ You have to step into deep time and look hard at the horizon, where you can glimpse human futures that extend for trillions of years.

One thing we know about stars is that they are going to exist for a very long time in this universe. Our own star, the Sun, is slated to shine in our skies for billions of years. That should be long enough for us to develop star-hopping technology, as any species must if it wants to survive on cosmological timescales. Our first interstellar trip might be to nearby Alpha Centauri, but in the long run, small stars will be the most attractive galactic lily pads to leap to. That’s because small stars like red dwarfs burn much longer than main sequence stars like our Sun. Some might be capable of heating human habitats for hundreds of billions of years.

When the last of the dwarfs start to wink out, the age of post-natural stars may be in full swing. In a dimming universe, an advanced civilisation might get creative about looking for energy. It might reignite celestial embers, by engineering collisions between them. Our descendants could sling dying suns into spiraling gravitational dances, from which new stars would emerge. Or they might siphon energy from black holes, or shape matter into artificial forms that generate more free energy than stars. There was a long period of human history when we limited ourselves to shelters like caves, shelters that appear fortuitously in nature. Now we reshape nature itself, into buildings that shelter us more comfortably than those that appear by dint of geologic chance. A star might be like a cave — a generous cosmic endowment, but crude compared to the power sources a long-term civilisation might conjure.

Our descendants could sling dying suns into spiraling gravitational dances, from which new stars would emerge

Even the most distant, severe events — the evaporation of black holes; the eventual breakdown of matter; the heat death of the universe itself — might not spell our end. If you tour the speculative realms of astrophysics, a number of plausible near-eternities come into view. Our universe could be cyclical, like those of Hindu and Buddhist cosmologies. Or perhaps it could be engineered to be so. We could learn to travel backward in time, to inhabit the vacant planets and stars of epochs past. Some physicists believe that we live in an infinite sea of cosmological domains, each governed by its own set of physical laws. The universe might contain hidden gateways to these domains. Perhaps future humans will duck into a more habitable, longer-lived universe, and then another, and another, ad infinitum. Our current notions of space and time could be preposterously limited.

At the Future of Humanity Institute, several thinkers are trying to model the potential range of human expansion into the cosmos. The consensus among them is that the Milky Way galaxy could be colonised in less than a million years, assuming we are able to invent fast-flying interstellar probes that can make copies of themselves out of raw materials harvested from alien worlds. If we want to spread out slowly, we could let the galaxy do the work for us. We could sprinkle starships into the Milky Way’s inner and outer tracks, spreading our diaspora over the Sun’s 250 million-year orbit around the galactic center.

If humans set out for other galaxies, the expansion of the universe will come into play. Some of the starry spirals we target will recede out of range before we can reach them. We recently built a new kind of crystal ball to deal with this problem. Our supercomputers can now host miniature universes, cosmological simulations that we can fast forward, to see how dense the universe will be in the deep future. We can model the structure and speed of colonisation waves within these simulations, by plugging in different assumptions about how fast our future probes will travel. Some think we’ll swarm locust-like over the Virgo supercluster, the enormous collection of galaxies to which the Milky Way is bound. Others are more ambitious. Anders Sandberg, a research fellow at the Future of Humanity Institute, told me that humans might be able to colonise a third of the now-visible universe, before dark energy pushes the rest out of reach. That would give us access to 100 billion galaxies, a mind-bending quantity of matter and energy to play with.

I asked Bostrom how he thought humans would expand into the massive ecological niche I have just described. ‘On that kind of time scale, you either glide into the bin of extinction scenarios, or into the bin of technological maturity scenarios,’ he said. ‘Among the latter, there is a wide range of futures that all have the same outward shape, which is Earth in the centre of this growing bubble of infrastructure, a bubble that grows uniformly at some significant fraction of the speed of light.’ It’s not clear what that expanding bubble of infrastructure might enable. It could provide the raw materials to power flourishing civilisations, human families encompassing trillions upon trillions of lives. Or it could be shaped into computational substrate, or into a Jupiter brain, a megastructure designed to think the deepest possible thoughts, all the way until the end of time.

It is only by considering this extraordinary range of human futures that our cosmic omen comes into view. It was the Russian physicist and visionary Konstantin Tsiolkovsky who first noticed the omen, though its discovery is usually credited to Enrico Fermi. Tsiolkovsky, the fifth of 18 children, was born in 1857 to a family of modest means in Izhevskoye, an ancient village 200 miles south-east of Moscow. He was forced to leave school at the age of 10 after a bout with scarlet fever left him hard of hearing. At 16, Tsiolkovsky made his way to Moscow, where he installed himself in its great library, surviving on books and scraps of black bread. He eventually took work as a schoolteacher, a profession that allowed him enough spare time to tinker around as an amateur engineer. By the age of 40, Tsiolkovsky had invented the monoplane, the wind tunnel, and the rocket equation — the mathematical basis of spaceflight today. Though he died decades before Sputnik, Tsiolkovsky believed it was human destiny to expand out into the cosmos. In the early 1930s, he wrote a series of philosophical tracts that launched Cosmism, a new school of Russian thought. He was famous for saying that ‘Earth is the cradle of humanity, but one cannot stay in the cradle forever.’

The mystery that nagged at Tsiolkovsky arose from his Copernican convictions, his belief that the universe is uniform throughout. If there is nothing uniquely fertile about our corner of the cosmos, he reasoned, intelligent civilisations should arise everywhere. They should bloom wherever there are planetary cradles like Earth. And if intelligent civilisations are destined to expand out into the universe, then scores of them should be crisscrossing our skies. Bostrom’s expanding bubbles of infrastructure should have enveloped Earth several times over.

In 1950, the Nobel Laureate and Manhattan Project physicist Enrico Fermi expressed this mystery in the form of a question: ‘Where are they?’ It’s a question that becomes more difficult to answer with each passing year. In the past decade alone, science has discovered that planets are ubiquitous in our galaxy, and that Earth is younger than most of them. If the Milky Way contains multitudes of warm, watery worlds, many with a billion-year head start on Earth, then it should have already spawned a civilisation capable of spreading across it. But so far, there’s no sign of one. No advanced civilisation has visited us, and no impressive feats of macro-engineering shine out from our galaxy’s depths. Instead, when we turn our telescopes skyward, we see only dead matter, sculpted into natural shapes, by the inanimate processes described by physics.

If life is a cosmic fluke, then we’ve already beaten the odds, and our future is undetermined — the galaxy is there for the taking

Robin Hanson, a research associate at the Future of Humanity Institute, says there must be something about the universe, or about life itself, that stops planets from generating galaxy-colonising civilisations. There must be a ‘great filter’, he says, an insurmountable barrier that sits somewhere on the line between dead matter and cosmic transcendence.

Before coming to Oxford, I had lunch with Hanson in Washington DC. He explained to me that the filter could be any number of things, or a combination of them. It could be that life itself is scarce, or it could be that microbes seldom stumble onto sexual reproduction. Single-celled organisms could be common in the universe, but Cambrian explosions rare. That, or maybe Tsiolkovsky misjudged human destiny. Maybe he underestimated the difficulty of interstellar travel. Or maybe technologically advanced civilisations choose not to expand into the galaxy, or do so invisibly, for reasons we do not yet understand. Or maybe, something more sinister is going on. Maybe quick extinction is the destiny of all intelligent life.

Humanity has already slipped through a number of these potential filters, but not all of them. Some lie ahead of us in the gauntlet of time. The identity of the filter is less important to Bostrom than its timing, its position in our past or in our future. For if it lies in our future, there could be an extinction risk waiting for us that we cannot anticipate, or to which anticipation makes no difference. There could be an inevitable technological development that renders intelligent life self-annihilating, or some periodic, catastrophic event in nature that empirical science cannot predict.

That’s why Bostrom hopes the Curiosity rover fails. ‘Any discovery of life that didn’t originate on Earth makes it less likely the great filter is in our past, and more likely it’s in our future,’ he told me. If life is a cosmic fluke, then we’ve already beaten the odds, and our future is undetermined — the galaxy is there for the taking. If we discover that life arises everywhere, we lose a prime suspect in our hunt for the great filter. The more advanced life we find, the worse the implications. If Curiosity spots a vertebrate fossil embedded in Martian rock, it would mean that a Cambrian explosion occurred twice in the same solar system. It would give us reason to suspect that nature is very good at knitting atoms into complex animal life, but very bad at nurturing star-hopping civilisations. It would make it less likely that humans have already slipped through the trap whose jaws keep our skies lifeless. It would be an omen.

On my last day in Oxford, I met with Toby Ord in his office at the Future of Humanity Institute. Ord is a utilitarian philosopher, and the founder of Giving What We Can, an organisation that encourages citizens of rich countries to pledge 10 per cent of their income to charity. In 2009, Ord and his wife, Bernadette Young, a doctor, pledged to live on a small fraction of their annual earnings, in the hope of donating £1 million to charity over the course of their careers. They live in a small, spare flat in Oxford, where they entertain themselves with music and books, and the occasional cup of coffee out with friends.

Ord has written a great deal about the importance of targeted philanthropy. His organisation sifts through global charities in order to identify the most effective among them. Right now, that title belongs to the Against Malaria Foundation, a charity that distributes mosquito nets in the developing world. Ord explained to me that ultra-efficient charities are thousands of times more effective at reducing human suffering than others. ‘Where you donate is more important than whether you donate,’ he said.

It intrigued me to learn that Ord was doing philosophical work on existential risk, given how careful he is about maximising the philanthropic impact of his actions. I was keen to ask him if he thought the problem of human extinction was more pressing than ending poverty or disease.

‘I’m not sure if existential risk is a bigger issue than global poverty,’ he told me. ‘I’ve kind of split my efforts between them recently, hoping that over time I’ll work out which is more important.’

Ord is wrestling with a formidable philosophical dilemma. He is trying to figure out whether our moral obligations to future humans outweigh those we have to humans that are alive and suffering right now. It’s a brutal calculus for the living. We might be 7 billion strong, but we are also a fire hose of future lives, that extinction would choke off forever. The casualties of human extinction would include not only the corpses of the final generation, but also all of our potential descendants, a number that could reach into the trillions.

It is this proper accounting of extinction’s utilitarian toll that prompts Bostrom to argue that reducing existential risk is morally paramount. His arguments elevate the reduction of existential risk above all other humanitarian projects, even extraordinary successes, like the eradication of smallpox, which has saved 100 million lives and counting. Ord isn’t convinced yet, but he hinted that he may be starting to lean.

‘I am finding it increasingly plausible that existential risk is the biggest moral issue in the world,’ he told me. ‘Even if it hasn’t gone mainstream yet.’

The idea that we might have moral obligations to the humans of the far future is a difficult one to process. After all, we humans are seasonal creatures, not stewards of deep time. The brevity of our lives colours our intuitions about value, and limits our moral vision. We can imagine futures for our children and grandchildren. We participate in their joys and weep for their hardships. We see that some glimmer of our fleeting lives survives on in them. But our distant descendants are opaque to us. We strain to see them, but they look alien across the abyss of time, transformed by the passage of so many millennia.

As Bostrom and I strolled among the skeletons at the Museum of Natural History in Oxford, we looked backward across another abyss of time. We were getting ready to leave for lunch, when we finally came upon the Megalosaurus, standing stiffly behind display glass. It was a partial skeleton, made of shattered bone fragments, like the chipped femur that found its way into Robert Plot’s hands not far from here. As we leaned in to inspect the ancient animal’s remnants, I asked Bostrom about his approach to philosophy. How did he end up studying a subject as morbid and peculiar as human extinction?

He told me that when he was younger, he was more interested in the traditional philosophical questions. He wanted to develop a basic understanding of the world and its fundamentals. He wanted to know the nature of being, the intricacies of logic, and the secrets of the good life.

‘But then there was this transition, where it gradually dawned on me that not all philosophical questions are equally urgent,’ he said. ‘Some of them have been with us for thousands of years. It’s unlikely that we are going to make serious progress on them in the next ten. That realisation refocused me on research that can make a difference right now. It helped me to understand that philosophy has a time limit.’

Tuesday, 11 April 2023

Consciousness creep

 Our machines could become self-aware without our knowing it. We need a better way to define and test for consciousness

Usually when people imagine a self-aware machine, they picture a device that emerges through deliberate effort and that then makes its presence known quickly, loudly, and (in most scenarios) disastrously. Even if its inventors have the presence of mind not to wire it into the nuclear missile launch system, the artificial intelligence will soon vault past our capacity to understand and control it. If we’re lucky, the new machine will simply break up with us, like the operating system in the movie Her. If not, it might decide not to open the pod bay doors to let us back into the spaceship. Regardless, the key point is that when an artificial intelligence wakes up, we’ll know.

But who’s to say machines don’t already have minds? What if they take unexpected forms, such as networks that have achieved a group-level consciousness? What if artificial intelligence is so unfamiliar that we have a hard time recognising it? Could our machines have become self-aware without our even knowing it? The huge obstacle to addressing such questions is that no one is really sure what consciousness is, let alone whether we’d know it if we saw it. In his 2012 book, Consciousness, the neuroscientist Christof Koch speculated that the web might have achieved sentience, and then posed the essential question: ‘By what signs shall we recognise its consciousness?’

Despite decades of focused effort, computer scientists haven’t managed to build an AI system intentionally, so it can’t be easy. For this reason, even those who fret the most about artificial intelligence, such as University of Oxford philosopher Nick Bostrom, doubt that AI will catch us completely unawares. And yet, there is reason to think that conscious machines might be a byproduct of some other effort altogether. Engineers routinely build technology that behaves in novel ways. Deep-learning systems, neural networks and genetic algorithms train themselves to perform complex tasks rather than follow a predetermined set of instructions. The solutions they come up with are as inscrutable as any biological system. Even a vacuum-cleaning robot’s perambulations across your floor emerge in an organic, often unpredictable way from the interplay of simple reflexes. ‘In theory, these systems could develop – as a way of solving a problem – something that we didn’t explicitly know was going to be conscious,’ says the philosopher Ron Chrisley, of the University of Sussex.

A system might not be able – or want – to participate in the classic appraisals of consciousness such as the Turing test

Even systems that are not designed to be adaptive do things their designers never meant. Computer operating systems and Wall Street stock-trading programs monitor their own activities, giving them a degree of self-awareness. A basic result in computer science is that self-referential systems are inherently unpredictable: a small change can snowball into a vastly different outcome as the system loops back on itself. We already experience this. ‘The instability of computer programs in general, and operating systems in particular, comes out of these Gödelian questions,’ says the MIT physicist Seth Lloyd.

Any intelligence that arises through such a process could be drastically different from our own. Whereas all those Terminator-style stories assume that a sentient machine will see us as a threat, an actual AI might be so alien that it would not see us at all. What we regard as its inputs and outputs might not map neatly to the system’s own sensory modalities. Its inner phenomenal experience could be almost unimaginable in human terms. The philosopher Thomas Nagel’s famous question – ‘What is it like to be a bat?’ – seems tame by comparison. A system might not be able – or want – to participate in the classic appraisals of consciousness such as the Turing test. It might operate on such different timescales or be so profoundly locked-in that, as the MIT cosmologist Max Tegmark has suggested, in effect it occupies a parallel universe governed by its own laws.

The first aliens that human beings encounter will probably not be from some other planet, but of our own creation. We cannot assume that they will contact us first. If we want to find such aliens and understand them, we need to reach out. And to do that we need to go beyond simply trying to build a conscious machine. We need an all-purpose consciousness detector.

You might reasonably worry that scientists could never hope to build such an instrument in their present state of ignorance about consciousness. But ignorance is actually the best reason to try. Even an imperfect test might help researchers to zero in on the causes and meanings of consciousness – and not just in machines but in ourselves, too.

To the extent that scientists comprehend consciousness at all, it is not merely a matter of crossing some threshold of complexity, common though that trope may be in science fiction. Human consciousness involves specific faculties. To identify them, we radiate outward from the one thing we do know: cogito ergo sum. When we ascribe consciousness to other people, we do so because they look like us, act like us, and talk like us. Through music, art, and vivid storytelling, they can evoke their experiences in us. Tests that fall into these three categories – anatomy, behaviour, communication – are used by doctors examining comatose patients, biologists studying animal awareness, and computer scientists designing AI systems.

The most systematic effort to piece all the tests together is ‘ConsScale’, a rating procedure developed in 2008 by the Spanish AI researcher Raúl Arrabales Moreno and his colleagues. You fill in a checklist, beginning with anatomical features, on the assumption that human-like consciousness arises only in systems with the right components. Does the system have a body? Memory? Attentional control? Then you look for behaviours and communicativeness: Can it recognise itself in a mirror? Can it empathise? Can it lie?

ConsScale relies on the big and highly contentious hypothesis that consciousness is a continuum

Based on the answers, Arrabales can determine where the system falls on a 12-level scale from ‘dead’ to ‘superhuman’. The scale is designed to recapitulate biology, both phylogeny and ontogeny, from viruses to worms to chimps to human beings, and from newborn babies to toddlers to adults. It also parallels, roughly, human conscious states from deep coma to partial awareness to transcendentally aware monk. At the lowest levels, conscious experience is a coarse sensation of this versus that, as the philosopher David Chalmers has described: ‘We need imagine only something like an unarticulated “flash” of experience, without any concepts, any thought, or any complex processing in the vicinity.’ At the highest level is a richness of experience beyond the normal human capacity. It is achieved, perhaps, by conjoined twins whose brains are autonomous yet linked, so that each has access to the other’s streams of consciousness.

LevelExplanationAnimal ExampleHuman Age EquivalentMachine Example-1DisembodiedBlends into environmentMolecule0IsolatedHas a body, but no functionsInert chromosomeStuffed animal1DecontrolledHas sensors and actuators, but is inactiveCorpsePowered-down computer2ReactiveHas fixed responsesVirusEmbryo to 1 monthELIZA3AdaptiveLearns new reactionsEarthworm1–4 monthsSmart thermostat4AttentionalFocuses selectively, learns by trial-and-error, and forms positive and negative associations (primitive emotions)Fish4–8 monthsCRONOS robot5ExecutiveSelects goals, acts to achieve them, and assesses its own conditionOctupus8–12 monthsCog6EmotionalHas a range of emotions, body schema, and minimal theory of mindMonkey12–18 monthsHaikonen architecture (partly implemented by XCR-1 robot)7Self-ConsciousKnows that it knows (higher-order thought) and passes the mirror testMagpie18–24 monthsNexus-6 (Do Androids Dream of Electric Sheep?)8EmpathicConceives of others as selves and adjusts how it presents itselfChimpanzee2–7 yearsHAL 9000 (2001)9SocialHas full theory of mind, talks, and can lieHuman7–11 yearsAva (Ex Machina)10HumanPasses the Turing Test and creates cumulative

cultureHuman12+ yearsSix (Battlestar Galactica)11Super-ConsciousCoordinates multiple streams of consciousnessBene Gesserit (Dune)augmentedSamantha (Her)

You can use the authors’ online calculator to see where your goldfish and cat fall on the scale. ELIZA, a pioneering computer psychoanalyst created at MIT in the 1960s, comes in at level 2 (‘reactive’). XCR-1, a robot with neuron-like electronic circuitry that can tell you, plaintively, when it’s in pain, built by the roboticist Pentti Haikonen, rates a 6 (‘emotional’). A specialised version of the calculator assesses computer-controlled characters in first-person-shooter video games, which may well be the most sophisticated AI most people typically encounter; let us hope they stay virtual.

ConsScale relies on the big and highly contentious hypothesis that consciousness is a continuum. Many philosophers, such as John Searle of the University of California at Berkeley, recoil from that idea because it implies panpsychism, the belief that everything around us is conscious to some degree. Panpsychism strikes these sceptics as trivialising the concept of consciousness and reverting to a prescientific view of the world. But for those who wonder and worry about whether we have inadvertently created conscious systems, ConsScale has a more significant failing: it presupposes that the answer is yes. Consciousness tests in general suffer from a problem of circularity. Any test becomes interesting only if it surprises us, but if it surprises us, do we trust it?

Suppose a test finds that a thermostat is conscious. If you’re inclined to think a thermostat is conscious, you will feel vindicated. If sentient thermostats strike you as silly, you will reject the verdict. In that case, why bother conducting the test at all?

What we really need is a consciousness detector that starts from first principles, so it doesn’t presuppose the outcome. At the moment, the approach that stirs the most souls is Integrated Information Theory, or IIT, conceived by the neuroscientist Giulio Tononi at the University of Wisconsin. As with many other accounts of consciousness, IIT’s starting point is that conscious experience is a unity, incapable of being subdivided. Consciousness is a cohesive experience that fuses memories and sensations into a consistent, linear narrative of the world.

IIT does not presume what a conscious system should look like. It could be a cerebrum, a circuit, or a black interplanetary cloud. It just has to consist of some type of units equivalent to neurons: on-off devices that are linked together into a complex network. A master clock governs the network. At each tick, each device switches on or off depending on the status of other devices. The agnostic attitude to composition is one of IIT’s great strengths. ‘What I like about it is that it doesn’t prejudice the issue from the outset by defining consciousness to be a biological condition,’ says Chrisley.

But what people really like about IIT is that it is quantitative. It spells out an algorithm for calculating the degree of network interconnectedness, or Φ, defined as the amount of information that is not localised in the individual parts but is spread out over the entire network. The theory associates this quantity with the degree of consciousness. Tononi provides an online tool for doing the Φ calculation. You map out the putative conscious system by drawing a graph akin to an electronic wiring diagram. In principle, you could enter any kind of stick-figure diagram, from the control algorithm of a robotic vacuum cleaner to the full connectome (map of neural linkages) of the human brain. The software will generate a table of all possible state transitions, which tells you the system’s stream of consciousness: which conscious state will give way to which other conscious state.

A network might hum with activity at diverse rates, only one of which will qualify as the speed of thought

From there, the software goes through all the possible ways the network can be cleft in two. It calculates how tightly linked the two sections are by mathematically rattling one side to see how the other responds. Among all those ways of divvying up the system, one will respond the least to being rattled. On the principle that any chain is only as strong as its weakest link, this minimum effect gives you a measure of how thoroughly interlinked the system is, and that quantity is Φ. The value of Φ is low in a fragmented network (which has only limited connections) or in a fully integrated network that behaves as a single block (which is so tightly connected that it has no flexibility). The value of Φ is high in a hierarchical network composed of independent clusters that interact – the human brain being a prime example.

Any network with a non-zero value of Φ has the potential to be conscious; there is no sharp threshold between catatonic and cognizant, which accords with the range of human experience. But IIT does have a one-mind-at-a-time rule. Any given neuron can participate in only one conscious entity at a time, so a hierarchical system can be conscious on only one level; other levels are merely epiphenomenal. For instance, the human cerebral cortex is conscious, but not the individual brain regions that make it up. To ascertain which level deserves to be called conscious, the software goes through all possible subsets of the network and calculates which of them has the highest value of Φ.

Tononi’s algorithm also looks across time, since a network might hum with activity at diverse rates, only one of which will qualify as the speed of thought. This, too, accords with our human reality. Your mind directly perceives changes occurring over a second or so, but is oblivious to neuronal pulses that last milliseconds and only dimly apprehends the longer arc of life. A conventional digital computer, on the other hand, peaks at the hardware level, Tononi says. Its thoughts are a sequence of low-level instructions—‘let a=b’, ‘if/then’, ‘goto’ – rather than the higher level of abstraction that characterises human cognition, and it has more such thoughts in a second than you do in a lifetime. It might be able to thrash you at chess, but Tononi would quickly smoke it out as not being aware that it’s playing.

For the purpose of building a useful consciousness detector, IIT faces a couple of practical hurdles. For one, critics worry, it gives false positives. The MIT computer scientist Scott Aaronson offers the example of common computer error-correcting codes, which repair data that have been corrupted by hardware glitches or transmission noise. Such codes work by redundancy, so that if one bit of data is lost, the remaining bits can fill in for it. To get the most redundancy, all the bits of data must be interdependent—which is to say, they will have a high value of Φ. But few would ever accuse those codes of being conscious.

Also, calculating Φ is taxing. The software has to consider all the possible ways to divide up a system, a huge number of permutations. You could waste a lifetime just trying to gauge the consciousness of your goldfish. Most discussions of IIT therefore do not consider hard values of Φ; rather, they appeal to the theory’s general interpretation of interconnectedness. For now, it’s really a qualitative theory masquerading as a quantitative one, although that might change with short-cuts for estimating Φ, which Tegmark has been developing.

Fortunately, the qualitative is enough to get started on the consciousness detector. Tononi and his colleagues have already built such a device for human beings. It consists of a magnetic coil that stimulates an area of the brain (using a technique called transcranial magnetic stimulation) and EEG electrodes placed all around the scalp to measure brain waves. A magnetic pulse acts like a clapper striking a bell. An awake person’s brain reverberates, indicating a highly interconnected neural network. In deeply sleeping, anaesthetised, or vegetative people, the response is localised and muted. The technique can therefore tell you whether an unresponsive patient is locked in – physically unresponsive yet still conscious – or truly gone. So far, Tononi says his team has tried the experiment on 200 patients, with encouraging results.

That’s just the sort of contraption we need for testing potentially intelligent machines. Another, broadly similar, idea is to listen for inner speech, a little voice in the robot’s head. Haikonen’s XCR-1 robot, for example, keeps up a running internal monologue that mimics the self-conscious voice we hear inside our heads. XCR-1 has an inner voice because its various sensor modalities are all interconnected. If the visual subsystem sees a ball, the auditory subsystem hears the word ‘ball’ even if no one spoke it – almost a sort of synaesthesia. When Haikonen hooks up the auditory module to a speech engine, the robot babbles on like a garrulous grandparent.

The idea of information integration also allows researchers to be more rigorous in how they apply the standard tests of consciousness, such as those on which ConsScale is based. All those tests still face what you might call the zombie problem. How do you know your uncle, let alone your computer, isn’t a pod person – a zombie in the philosophical sense, going through the motions but lacking an internal life? He could look, act, and talk like your uncle, but have no experience of being your uncle. None of us can ever enter another mind, so we can never really know whether anyone’s home.

Zombies are at a disadvantage in complex physical and social environments, so natural selection will prefer living minds

IIT translates the conceptual zombie problem into a specific question of measurement: can a mindless system (low Φ) pass itself off as a conscious one (having high Φ)? The disappointing initial answer is yes. A high-Φ system has feedback loops, which connect the output back to the input – making the system aware of what it is doing. But even though a low-Φ system lacks feedback, it can still do anything a feedback system can, so you can’t tell the difference based solely on outward behaviour.

Yet you can often infer what’s happening on the inside from the constraints a system is operating under. For instance, the kind of internal feedback associated with higher-level cognition is more efficient at producing complex behaviours. It monitors the effect it produces and can fine-tune its actions in response. If you step on the gas and feel your car accelerate faster than you intended, you ease off. If you couldn’t make that adjustment, you’d have to get the action exactly right the first time. A system without feedback therefore has to build in an enormous decision tree with every possible condition spelled out in advance. ‘In order to be functionally equivalent to a system that has feedback, you need many more units and connections,’ Tononi says. Given limited resources, a zombie system will be much less capable than its feedback-endowed equivalent.

‘Zombies are not able to deploy the same behaviours we are able to do,’ says Arrabales. ‘They are like the zombies in the movies. They’re so stupid, because they don’t have our conscious mind.’ That puts zombies at a disadvantage in complex physical and social environments, so natural selection will prefer living minds. The same should be true for any AI that arises through adaptive techniques that mimic evolution. Now we’re getting somewhere. If we encounter a machine that can do what we can, and that must operate under the same bodily constraints that we do, the most parsimonious explanation will be that it is indeed conscious in every sense that we are conscious.

A test that applies only to consciousness among individual, corporeal entities is not exactly the kind of universal detector we are hoping for, however. What about group minds? Could the internet be conscious? What about biological groups, such as a nation or even Gaia – the biosphere as a whole? Groups have many of the essential qualities of conscious systems. They can act with purpose and be ‘aware’ of information. Last year, the philosopher Eric Schwitzgebel, of the University of California at Riverside, suggested that they also have full-blown phenomenal consciousness, citing IIT in defence of that view. After all, many organisations have an integrated, hierarchical structure reminiscent of the brain’s, so their Φ should be significant.

Tononi disagrees, noting that his one-mind-at-a-time rule excludes it. If individuals are conscious, a group of them cannot be; everything the group does reflects the will of the individuals in it, so no sense of self could ever take hold at the collective level. But what’s wrong with supposing that causation can occur on more than one level of description? Schwitzgebel offers a compelling thought-experiment. Imagine you replaced every neuron in your brain with a conscious bug, which has its own inner subjective experience but behaves outwardly just like a neuron. Would those critters usurp your consciousness and leave you an empty shell of a person? How could they possibly do that if nothing measurable about your brain activity has changed? It seems more straightforward to assume that the critters are conscious and so are you, in which case consciousness can emerge at multiple levels. How to identify and measure consciousness at the group level, let alone communicate with such entities, is a truly open problem.

Tackling those big problems is important, however. Building a consciousness detector is not just an intellectually fascinating idea. It is morally urgent – not so much because of what these systems could do to us, but what we could do to them. Dumb robots are plenty dangerous already, so conscious ones needn’t pose a special threat. To the contrary, they are just as likely to put us to shame by displaying higher forms of morality. And for want of recognising what we have brought into the world, we could be guilty of what Bostrom calls ‘mind crime’ – the creation of sentient beings for virtual enslavement. In fact, Schwitzgebel argues that we have greater responsibility to intelligent machines than to our fellow human beings, in the way that the parent bears a special responsibility to the child.

We are already encountering systems that act as if they were conscious. Our reaction to them depends on whether we think they really are, so tools such as Integrated Information Theory will be our ethical lamplights. Tononi says: ‘The majority of people these days would still say, “Oh, no, no, it’s just a machine”, but they have just the wrong notion of a machine. They are still stuck with cold things sitting on the table or doing clunky things. They are not yet prepared for a machine that can really fool you. When that happens – and it shows emotion in a way that makes you cry and quotes poetry and this and that – I think there will be a gigantic switch. Everybody is going to say, “For God’s sake, how can we turn that thing off?”’

Friday, 7 April 2023

Endless fun

 The question is not whether we can upload our brains onto a computer, but what will become of us when we do

In the late 1700s, machinists started making music boxes: intricate little mechanisms that could play harmonies and melodies by themselves. Some incorporated bells, drums, organs, even violins, all coordinated by a rotating cylinder. The more ambitious examples were Lilliputian orchestras, such as the Panharmonicon, invented in Vienna in 1805, or the mass-produced Orchestrion that came along in Dresden in 1851.

But the technology had limitations. To make a convincing violin sound, one had to create a little simulacrum of a violin — quite an engineering feat. How to replicate a trombone? Or an oboe? The same way, of course. The artisans assumed that an entire instrument had to be copied in order to capture its distinctive tone. The metal, the wood, the reed, the shape, the exact resonance, all of it had to be mimicked. How else were you going to create an orchestral sound? The task was discouragingly difficult.

Then, in 1877, the American inventor Thomas Edison introduced the first phonograph, and the history of recorded music changed. It turns out that, in order to preserve and recreate the sound of an instrument, you don’t need to know everything about it, its materials or its physical structure. You don’t need a miniature orchestra in a cabinet. All you need is to focus on the one essential part of it. Record the sound waves, turn them into data, and give them immortality.

Imagine a future in which your mind never dies. When your body begins to fail, a machine scans your brain in enough detail to capture its unique wiring. A computer system uses that data to simulate your brain. It won’t need to replicate every last detail. Like the phonograph, it will strip away the irrelevant physical structures, leaving only the essence of the patterns. And then there is a second you, with your memories, your emotions, your way of thinking and making decisions, translated onto computer hardware as easily as we copy a text file these days.

That second version of you could live in a simulated world and hardly know the difference. You could walk around a simulated city street, feel a cool breeze, eat at a café, talk to other simulated people, play games, watch movies, enjoy yourself. Pain and disease would be programmed out of existence. If you’re still interested in the world outside your simulated playground, you could Skype yourself into board meetings or family Christmas dinners.

This vision of a virtual-reality afterlife, sometimes called ‘uploading’, entered the popular imagination via the short story ‘The Tunnel Under the World’ (1955) by the American science-fiction writer Frederik Pohl, though it also got a big boost from the movie Tron (1982). Then The Matrix (1999) introduced the mainstream public to the idea of a simulated reality, albeit one into which real brains were jacked. More recently, these ideas have caught on outside fiction. The Russian multimillionaire Dmitry Itskov made the news by proposing to transfer his mind into a robot, thereby achieving immortality. Only a few months ago, the British physicist Stephen Hawking speculated that a computer-simulated afterlife might become technologically feasible.

It is tempting to ignore these ideas as just another science-fiction trope, a nerd fantasy. But something about it won’t leave me alone. I am a neuroscientist. I study the brain. For nearly 30 years, I’ve studied how sensory information gets taken in and processed, how movements are controlled and, lately, how networks of neurons might compute the spooky property of awareness. I find myself asking, given what we know about the brain, whether we really could upload someone’s mind to a computer. And my best guess is: yes, almost certainly. That raises a host of further questions, not least: what will this technology do to us psychologically and culturally? Here, the answer seems just as emphatic, if necessarily murky in the details.

It will utterly transform humanity, probably in ways that are more disturbing than helpful. It will change us far more than the internet did, though perhaps in a similar direction. Even if the chances of all this coming to pass were slim, the implications are so dramatic that it would be wise to think them through seriously. But I’m not sure the chances are slim. In fact, the more I think about this possible future, the more it seems inevitable.

If did you want to capture the music of the mind, where should you start? A lot of biological machinery goes into a human brain. A hundred billion neurons are connected in complicated patterns, each neurone constantly taking in and sending signals. The signals are the result of ions leaking in and out of cell membranes, their flow regulated by tiny protein pores and pumps. Each connection between neurons, each synapse, is itself a bewildering mechanism of proteins that are constantly in flux.

It is a daunting task just to make a plausible simulation of a single neurone, though this has already been done to an approximation. Simulating a whole network of interacting neurons, each one with truly realistic electrical and chemical properties, is beyond current technology. Then there are the complicating factors. Blood vessels react in subtle ways, allowing oxygen to be distributed more to this or that part of the brain as needed. There are also the glia, tiny cells that vastly outnumber neurons. Glia help neurons function in ways that are largely not understood: take them away and none of the synapses or signals work properly. Nobody, as far as I know, has tried a computer simulation of neurons, glia, and blood flow. But perhaps they wouldn’t have to. Remember Edison’s breakthrough with the phonograph: to faithfully replicate a sound, it turns out you don’t also have to replicate the instrument that originally produced it.

So what is the right level of detail to copy if you want to capture a person’s mind? Of all the biological complexity, what patterns in the brain must be reproduced to capture the information, the computation, and the consciousness? One of the most common suggestions is that the pattern of connectivity among neurons contains the essence of the machine. If you could measure how each neurone connects to its neighbours, you’d have all the data you need to re-create that mind. An entire field of study has grown up around neural network models, computer simulations of drastically simplified neurons and synapses. These models leave out the details of glia, blood flow, membranes, proteins, ions and so on. They only consider how each neurone is connected to the others. They are wiring diagrams.

Simple computer models of neurons, hooked together by simple synapses, are capable of enormous complexity. Such network models have been around for decades, and they differ in interesting ways from standard computer programs. For one thing, they are able to learn, as neurons subtly adjust their connections to each other. They can solve problems that are difficult for traditional programs, and are particularly good at taking noisy input and compensating for the noise. Give a neural net a fuzzy, spotty photograph, and it might still be able to categorise the object depicted, filling in the gaps and blips in the image — something called pattern completion.

Despite these remarkably human-like capacities, neural network models are not yet the answer to simulating a brain. Nobody knows how to build one at an appropriate scale. Some notable attempts are being made, such as the Blue Brain project and its successor, the EU-funded Human Brain Project, both run by the Swiss Federal Institute of Technology in Lausanne. But even if computers were powerful enough to simulate 100 billion neurons — and computer technology is pretty close to that capability — the real problem is that nobody knows how to wire up such a large artificial network.

In some ways, the scientific problem of understanding the human brain is similar to the problem of human genetics. If you want to understand the human genome properly, an engineer might start with the basic building blocks of DNA and construct an animal, one base pair at a time, until she has created something human-like. But given the massive complexity of the human genome — more than 3 billion base pairs — that approach would be prohibitively difficult at the present time. Another approach would be to read the genome that we already have in real people. It is a lot easier to copy something complicated than to re-engineer it from scratch. The human genome project of the 1990s accomplished that, and even though nobody really understands it very well, at least we have a lot of copies of it on file to study.

The same strategy might be useful on the human brain. Instead of trying to wire up an artificial brain from first principles, or training a neural network over some absurdly long period until it becomes human-like, why not copy the wiring already present in a real brain? In 2005, two scientists, Olaf Sporns, professor of brain sciences at Indiana University, and Patric Hagmann, neuroscientist at the University of Lausanne, independently coined the term ‘connectome’ to refer to a map or wiring diagram of every neuronal connection in a brain. By analogy to the human genome, which contains all the information necessary to grow a human being, the human connectome in theory contains all the information necessary to wire up a functioning human brain. If the basic premise of neural network modelling is correct, then the essence of a human mind is contained in its pattern of connectivity. Your connectome, simulated in a computer, would recreate your conscious mind.

It seems a no-brainer (excuse the pun) that we will be able to scan, map, and store the data on every neuronal connection within a person’s head

Could we ever map a complete human connectome? Well, scientists have done it for a roundworm. They’ve done it for small parts of a mouse brain. A very rough, large-scale map of connectivity in the human brain is already available, though nothing like a true map of every idiosyncratic neurone and synapse in a particular person’s head. The National Institutes of Health in the US is currently funding the Human Connectome Project, an effort to map a human brain in as much detail as possible. I admit to a certain optimism toward the project. The technology for brain scanning improves all the time. Right now, magnetic resonance imaging (MRI) is at the forefront. High-resolution scans of volunteers are revealing the connectivity of the human brain in more detail than anyone ever thought possible. Other, even better technologies will be invented. It seems a no-brainer (excuse the pun) that we will be able to scan, map, and store the data on every neuronal connection within a person’s head. It is only a matter of time, and a timescale of five to 10 decades seems about right.

Of course, nobody knows if the connectome really does contain all the essential information about the mind. Some of it might be encoded in other ways. Hormones can diffuse through the brain. Signals can combine and interact through other means besides synaptic connections. Maybe certain other aspects of the brain need to be scanned and copied to make a high-quality simulation. Just as the music recording industry took a century of tinkering to achieve the impressive standards of the present day, the mind-recording industry will presumably require a long process of refinement.

That won’t be soon enough for some of us. One of the basic facts about people is that they don’t like to die. They don’t like their loved ones or their pets to die. Some of them already pay enormous sums to freeze themselves, or even (somewhat gruesomely) to have their corpses decapitated and their heads frozen on the off-chance that a future technology will successfully revive them. These kinds of people will certainly pay for a spot in a virtual afterlife. And as the technology advances and the public starts to see the possibilities, the incentives will increase.

One might say (at risk of being crass) that the afterlife is a natural outgrowth of the entertainment industry. Think of the fun to be had as a simulated you in a simulated environment. You could go on a safari through Middle Earth. You could live in Hogwarts, where wands and incantations actually do produce magical results. You could live in a photogenic, outdoor, rolling country, a simulation of the African plains, with or without the tsetse flies as you wish. You could live on a simulation of Mars. You could move easily from one entertainment to the next. You could keep in touch with your living friends through all the usual social media.

I have heard people say that the technology will never catch on. People won’t be tempted because a duplicate of you, no matter how realistic, is still not you. But I doubt that such existential concerns will have much of an impact once the technology arrives. You already wake up every day as a marvellous copy of a previous you, and nobody has paralysing metaphysical concerns about that. If you die and are replaced by a really good computer simulation, it’ll just seem to you like you entered a scanner and came out somewhere else. From the point of view of continuity, you’ll be missing some memories. If you had your annual brain-backup, say, eight months earlier, you’ll wake up missing those eight months. But you will still feel like you, and your friends and family can fill you in on what you missed. Some groups might opt out — the Amish of information technology — but the mainstream will presumably flock to the new thing.

And then what? Well, such a technology would change the definition of what it means to be an individual and what it means to be alive. For starters, it seems inevitable that we will tend to treat human life and death much more casually. People will be more willing to put themselves and others in danger. Perhaps they will view the sanctity of life in the same contemptuous way that the modern e-reader crowd views old fogeys who talk about the sanctity of a cloth-bound, hardcover book. Then again, how will we view the sanctity of digital life? Will simulated people, living in an artificial world, have the same human rights as the rest of us? Would it be a crime to pull the plug on a simulated person? Is it ethical to experiment on simulated consciousness? Can a scientist take a try at reproducing Jim, make a bad copy, casually delete the hapless first iteration, and then try again until he gets a satisfactory version? This is just the tip of a nasty philosophical iceberg we seem to be sailing towards.

In many religions, a happy afterlife is a reward. In an artificial one, due to inevitable constraints on information processing, spots are likely to be competitive. Who decides who gets in? Do the rich get served first? Is it merit-based? Can the promise of resurrection be dangled as a bribe to control and coerce people? Will it be withheld as a punishment? Will a special torture version of the afterlife be constructed for severe punishment? Imagine how controlling a religion would become if it could preach about an actual, objectively provable heaven and hell.

Then there are the issues that will arise if people deliberately run multiple copies of themselves at the same time, one in the real world and others in simulations. The nature of individuality, and individual responsibility, becomes rather fuzzy when you can literally meet yourself coming the other way. What, for instance, is the social expectation for married couples in a simulated afterlife? Do you stay together? Do some versions of you stay together and other versions separate?

If a brain has been replaced by a few billion lines of code, we might understand how to edit any destructive emotions right out of it

Then again, divorce might seem a little melodramatic if irreconcilable differences become a thing of the past. If your brain has been replaced by a few billion lines of code, perhaps eventually we will understand how to edit any destructive emotions right out of it. Or perhaps we should imagine an emotional system that is standard-issue, tuned and mainstreamed, such that the rest of your simulated mind can be grafted onto it. You lose the battle-scarred, broken emotional wiring you had as a biological agent and get a box-fresh set instead. This is not entirely far-fetched; indeed, it might make sense on economic rather than therapeutic grounds. The brain is roughly divisible into a cortex and a brainstem. Attaching a standard-issue brainstem to a person’s individualised, simulated cortex might turn out to be the most cost-effective way to get them up and running.

So much for the self. What about the world? Will the simulated environment necessarily mimic physical reality? That seems the obvious way to start out, after all. Create a city. Create a blue sky, a pavement, the smell of food. Sooner or later, though, people will realise that a simulation can offer experiences that would be impossible in the real world. The electronic age changed music, not merely mimicking physical instruments but offering new potentials in sound. In the same way, a digital world could go to some unexpected places.

To give just one disorientating example, it might include any number of dimensions in space and time. The real world looks to us to have three spatial dimensions and one temporal one, but, as mathematicians and physicists know, more are possible. It’s already possible to programme a video game in which players move through a maze of four spatial dimensions. It turns out that, with a little practice, you can gain a fair degree of intuition for the four-dimensional regime (I published a study on this in the Journal of Experimental Psychology in 2008). To a simulated mind in a simulated world, the confines of physical reality would become irrelevant. If you don’t have a body any longer, why pretend?

All of the changes described above, as exotic as they are and disturbing as some of them might seem, are in a sense minor. They are about individual minds and individual experiences. If uploading were only a matter of exotic entertainment, literalising people’s psychedelic fantasies, then it would be of limited significance. If simulated minds can be run in a simulated world, then the most transformative change, the deepest shift in human experience, would be the loss of individuality itself — the integration of knowledge into a single intelligence, smarter and more capable than anything that could exist in the natural world.

You wake up in a simulated welcome hall in some type of simulated body with standard-issue simulated clothes. What do you do? Maybe you take a walk and look around. Maybe you try the food. Maybe you play some tennis. Maybe go watch a movie. But sooner or later, most people will want to reach for a cell phone. Send a tweet from paradise. Text a friend. Get on Facebook. Connect through social media. But here is the quirk of uploaded minds: the rules of social media are transformed.

Real life, our life, will shrink in importance until it becomes a kind of larval phase

In the real world, two people can share experiences and thoughts. But lacking a USB port in our heads, we can’t directly merge our minds. In a simulated world, that barrier falls. A simple app, and two people will be able to join thoughts directly with each other. Why not? It’s a logical extension. We humans are hyper-social. We love to network. We already live in a half-virtual world of minds linked to minds. In an artificial afterlife, given a few centuries and few tweaks to the technology, what is to stop people from merging into überpeople who are combinations of wisdom, experience, and memory beyond anything possible in biology? Two minds, three minds, 10, pretty soon everyone is linked mind-to-mind. The concept of separate identity is lost. The need for simulated bodies walking in a simulated world is lost. The need for simulated food and simulated landscapes and simulated voices disappears. Instead, a single platform of thought, knowledge, and constant realisation emerges. What starts out as an artificial way to preserve minds after death gradually takes on an emphasis of its own. Real life, our life, shrinks in importance until it becomes a kind of larval phase. Whatever quirky experiences you might have had during your biological existence, they would be valuable only if they can be added to the longer-lived and much more sophisticated machine.

I am not talking about utopia. To me, this prospect is three parts intriguing and seven parts horrifying. I am genuinely glad I won’t be around. This will be a new phase of human existence that is just as messy and difficult as any other phase has been, one as alien to us now as the internet age would have been to a Roman citizen 2,000 years ago; as alien as Roman society would have been to a Natufian hunter-gatherer 10,000 years before that. Such is progress. We always manage to live more-or-less comfortably in a world that would have frightened and offended the previous generations.

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