Group items tagged

they analyzed the diversity of existing H5N1 strains and found that many wild viruses are already tantalizingly close to becoming potentially contagious among humans.

we know very little about the actual receptors in human airways that are relevant for flu viruses.” Even the so-called “human” receptors can vary significantly, and it’s unclear which ones are found in different parts of the airways, or how common they are.

identified should be tested in ferret experiments to see if they genuinely are more efficient at spreading between mammals. However, given the controversy surrounding the development of potentially contagious flu strains, Fouchier wonders “whether we would be allowed to empirically test this newly acquired knowledge in animal models.”

because the mutation is specific to the tumor — and can serve as a sort of beacon to properly designed immune cells — it might actually be this cancer's Achilles' heel.

treatment involves taking patients' T cells, then inserting a new gene that allows the cells to recognize the mutant protein.

the cells can be reinfused and begin their task of zeroing in on and eliminating cells with the mutation.

"It's taking an antibody, which is typically a kind of molecule that's circulating around in the blood," said Maus. "And it's fusing it to proteins that will cross the membrane and that then will signal to T cells to replicate and kill."

There are plenty of theories, of course, especially regarding why: increasingly complex social networks, a culture built around tool use and collaboration, the challenge of adapting to a mercurial and often harsh climate

Although these possibilities are fascinating, they are extremely difficult to test.

Although it makes up only 2 percent of body weight, the human brain consumes a whopping 20 percent of the body’s total energy at rest. In contrast, the chimpanzee brain needs only half that.

contrary to long-standing assumptions, larger mammalian brains do not always have more neurons, and the ones they do have are not always distributed in the same way.

The human brain has 86 billion neurons in all: 69 billion in the cerebellum, a dense lump at the back of the brain that helps orchestrate basic bodily functions and movement; 16 billion in the cerebral cortex, the brain’s thick corona and the seat of our most sophisticated mental talents, such as self-awareness, language, problem solving and abstract thought; and 1 billion in the brain stem and its extensions into the core of the brain

In contrast, the elephant brain, which is three times the size of our own, has 251 billion neurons in its cerebellum, which helps manage a giant, versatile trunk, and only 5.6 billion in its cortex

primates evolved a way to pack far more neurons into the cerebral cortex than other mammals did

The great apes are tiny compared to elephants and whales, yet their cortices are far denser: Orangutans and gorillas have 9 billion cortical neurons, and chimps have 6 billion. Of all the great apes, we have the largest brains, so we come out on top with our 16 billion neurons in the cortex.

“What kinds of mutations occurred, and what did they do? We’re starting to get answers and a deeper appreciation for just how complicated this process was.”

there was a strong evolutionary pressure to modify the human regulatory regions in a way that sapped energy from muscle and channeled it to the brain.

Accounting for body size and weight, the chimps and macaques were twice as strong as the humans. It’s not entirely clear why, but it is possible that our primate cousins get more power out of their muscles than we get out of ours because they feed their muscles more energy. “Compared to other primates, we lost muscle power in favor of sparing energy for our brains,” Bozek said. “It doesn’t mean that our muscles are inherently weaker. We might just have a different metabolism.

a pioneering experiment. Not only were they going to identify relevant genetic mutations from our brain’s evolutionary past, they were also going to weave those mutations into the genomes of lab mice and observe the consequences.

Silver and Wray introduced the chimpanzee copy of HARE5 into one group of mice and the human edition into a separate group. They then observed how the embryonic mice brains grew.

After nine days of development, mice embryos begin to form a cortex, the outer wrinkly layer of the brain associated with the most sophisticated mental talents. On day 10, the human version of HARE5 was much more active in the budding mice brains than the chimp copy, ultimately producing a brain that was 12 percent larger

“It wasn’t just a couple mutations and—bam!—you get a bigger brain. As we learn more about the changes between human and chimp brains, we realize there will be lots and lots of genes involved, each contributing a piece to that. The door is now open to get in there and really start understanding. The brain is modified in so many subtle and nonobvious ways.”

As recent research on whale and elephant brains makes clear, size is not everything, but it certainly counts for something. The reason we have so many more cortical neurons than our great-ape cousins is not that we have denser brains, but rather that we evolved ways to support brains that are large enough to accommodate all those extra cells.

There’s a danger, though, in becoming too enamored with our own big heads. Yes, a large brain packed with neurons is essential to what we consider high intelligence. But it’s not sufficient

No matter how large the human brain grew, or how much energy we lavished upon it, it would have been useless without the right body. Three particularly crucial adaptations worked in tandem with our burgeoning brain to dramatically increase our overall intelligence: bipedalism, which freed up our hands for tool making, fire building and hunting; manual dexterity surpassing that of any other animal; and a vocal tract that allowed us to speak and sing.

Human intelligence, then, cannot be traced to a single organ, no matter how large; it emerged from a serendipitous confluence of adaptations throughout the body. Despite our ongoing obsession with the size of our noggins, the fact is that our intelligence has always been so much bigger than our brain.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

She and others emphasized the apparent lack of severe health repercussions from reinfection — and the lack of evidence that reinfection is common.

Nearly 4 percent of people who had a previous infection were reinfected, an almost identical rate to those with no history of infection.

“Basically, it’s saying vaccination actually needs to be better than natural immunity. But vaccination is better than natural immunity.”

The study backs up recent laboratory data from South African researchers analyzing blood plasma from recovered patients. Nearly half of the plasma samples had no detectable ability to block the variant from infecting cells in a laboratory dish

The good news is that vaccine trials from Johnson & Johnson and Novavax show that vaccines can work — even against the B.1.351 variant, and particularly in preventing severe illness.

Novavax did not provide a breakdown of mild, moderate and severe cases, but severe cases of covid-19 were rare in the trial, suggesting that reinfection is unlikely to send people to the hospital.

“It is not surprising to see reinfection in individuals who are convalescent. And it would not be surprising to see infection in people who are vaccinated, especially a few months out from vaccine,”

“The key is not whether people get reinfected, it’s whether they get sick enough to be hospitalized.

“If the data holds true, it means we will need to walk the public back on the idea of how close we are to the finish line for ending this pandemic.”

Projections created by data scientist Youyang Gu — whose pandemic models have been cited by the Centers for Disease Control and Prevention — suggest that about 65 percent of America’s population will reach immunity by June 1. But built into that 65 percent is roughly 20 percent having immunity from past infections only.

In a separate study, scientists at Rockefeller University in New York took blood plasma from people who had been vaccinated and found that vaccine-generated antibodies were largely able to block mutations found on the B.1.351 variant.

I think the fact that we … now have data from two vaccines indicating that we can prevent serious disease, even against the new variant, is hopeful,”

A future concern needing close monitoring is whether the reformulation of vaccines to keep up with the evolving virus could drive the virus to continue evolving.

There is also a concern that subpar immunity could allow new resistant variants to emerge. That possibility, Nussenzweig said, is one reason that people should get both doses of a vaccine, on time.

. All of these regions can be disturbed in depressive illnesses.

The second finding is de novo point mutations, which arise spontaneously in the sperm of adult men. Sperm divide every 15 days. This continuous division and copying of DNA leads to errors, and the rate of error increases significantly with age: a 20-year-old will have an average of 25 de novo point mutations in his sperm, whereas a 40-year-old will have 65. These mutations are one reason older fathers are more likely to have children with autism and schizophrenia.

Messenger RNA is a powerful, if fickle, component of life’s building blocks — a workhorse of the cell that is also truly just a messenger, unstable and prone to degrade.

. In 1990,

That same year, a team at the University of Wisconsin startled the scientific world with a paper that showed it was possible to inject a snippet of messenger RNA into mice and turn their muscle cells into factories, creating proteins on demand.

If custom-designed RNA snippets could be used to turn cells into bespoke protein factories, messenger RNA could become a powerful medical tool. It could encode fragments of virus to teach the immune system to defend against pathogens. It could also create whole proteins that are missing or damaged in people with devastating genetic diseases, such as cystic fibrosis.

In 2005, the pair discovered a way to modify RNA, chemically tweaking one of the letters of its code, so it didn’t trigger an inflammatory response. Deborah Fuller, a scientist who works on RNA and DNA vaccines at the University of Washington, said that work deserves a Nobel Prize.

messenger RNA posed a bigger challenge than other targets.“It’s tougher — it’s a much bigger molecule, it’s much more unstable,”

Unlike fields that were sparked by a single powerful insight, Sahin said that the recent success of messenger RNA vaccines is a story of countless improvements that turned an alluring biological idea into a beneficial technology.

“This is a field which benefited from hundreds of inventions,” said Sahin, who noted that when he started BioNTech in 2008, he cautioned investors that the technology would not yield a product for at least a decade. He kept his word: Until the coronavirus sped things along, BioNTech projected the launch of its first commercial project in 2023.

“It’s new to you,” Fuller said. “But for basic researchers, it’s been long enough. . . . Even before covid, everyone was talking: RNA, RNA, RNA.”

All vaccines are based on the same underlying idea: training the immune system to block a virus. Old-fashioned vaccines do this work by injecting dead or weakened viruses

ewer vaccines use distinctive bits of the virus, such as proteins on their surface, to teach the lesson. The latest genetic techniques, like messenger RNA, don’t take as long to develop because those virus bits don’t have to be generated in a lab. Instead, the vaccine delivers a genetic code that instructs cells to build those characteristic proteins themselves.

They wanted the immune system to learn to recognize the thumb tack spike, so McLellan tasked a scientist in his laboratory with identifying genetic mutations that could anchor the protein into the right configuration. It was a painstaking process for Nianshuang Wang, who now works at a biotechnology company, Regeneron Pharmaceuticals. After trying hundreds of genetic mutations, he found two that worked. Five journals rejected the finding, questioning its significance, before it was published in 2017.

That infection opened Graham’s eyes to an opportunity. HKU1 was merely a nuisance, as opposed to a deadly pneumonia; that meant it would be easier to work with in the lab, since researchers wouldn’t have to don layers of protective gear and work in a pressurized laboratory.

Severe acute respiratory syndrome had emerged in 2003. Middle East respiratory syndrome (MERS) broke out in 2012. It seemed clear to Graham and Jason McLellan, a structural biologist now at the University of Texas at Austin, that new coronaviruses were jumping into people on a 10-year-clock and it might be time to brace for the next one.

Last winter, when Graham heard rumblings of a new coronavirus in China, he brought the team back together. Once its genome was shared online by Chinese scientists, the laboratories in Texas and Maryland designed a vaccine, utilizing the stabilizing mutations and the knowledge they had gained from years of basic research — a weekend project thanks to the dividends of all that past work.

Graham needed a technology that could deliver it into the body — and had already been working with Moderna, using its messenger RNA technology to create a vaccine against a different bat virus, Nipah, as a dress rehearsal for a real pandemic. Moderna and NIH set the Nipah project aside and decided to go forward with a coronavirus vaccine.

On Jan. 13, Moderna’s Moore came into work and found her team already busy translating the stabilized spike protein into their platform. The company could start making the vaccine almost right away because of its experience manufacturing experimental cancer vaccines, which involves taking tumor samples and developing personalized vaccines in 45 days.

At BioNTech, Sahin said that even in the early design phases of its vaccine candidates, he incorporated the slight genetic changes designed in Graham’s lab that would make the spike look more like the real thing. At least two other companies would incorporate that same spike.

If all goes well with regulators, the coronavirus vaccines have the makings of a pharmaceutical industry fairy tale. The world faced an unparalleled threat, and companies leaped into the fight. Pfizer plowed $2 billion into the effort. Massive infusions of government cash helped remove the financial risks for Moderna.

But the world will also owe their existence to many scientists outside those companies, in government and academia who pursued ideas they thought were important even when the world doubted them

Some of those scientists will receive remuneration, since their inventions are licensed and integrated into the products that could save the world.

As executives become billionaires, many scientists think it is fair to earn money from their inventions that can help them do more important work. But McLellan’s laboratory at the University of Texas is proud to have licensed an even more potent version of their spike protein, royalty-free, to be incorporated into a vaccine for low and middle income countries.

“They’re using the technology that [Kariko] and I developed,” he said. “We feel like it’s our vaccine, and we are incredibly excited — at how well it’s going, and how it’s going to be used to get rid of this pandemic.”

“People hear about [vaccine progress] and think someone just thought about it that night. The amount of work — it’s really a beautiful story of fundamental basic research,” Fauci said. “It was chancy, in the sense that [the vaccine technology] was new. We were aware there would be pushback. The proof in the pudding is a spectacular success.”

The Vaccine Research Center, where Graham is deputy director, was the brainchild of Anthony S. Fauci, director of the National Institute of Allergy and Infectious Diseases. It was created in 1997 to bring together scientists and physicians from different disciplines to defeat diseases, with a heavy focus on HIV.

the pandemic wasn’t a sudden eureka moment — it was a catalyst that helped ignite lines of research that had been moving forward for years, far outside the spotlight of a global crisis.

"We are truly in a paradigm change,

Now research is asking "when is histology [the microscopic structure of cancers] important, and when isn't it?" he said

the effort is "the largest and most rigorous precision oncology trial that's ever been attempted"

It no longer makes sense to categorise and treat cancer based on the site in the body where it originates when we know it is a disease of DNA mutations that modern technology allows us to understand, he said.

. “Any successful virus has to get beyond that first defense system. The more successful it is at doing that, the better off the virus is.”

A lot of researchers focused their attention on the nine mutations that alter the so-called spike protein that covers the coronavirus and allows it to invade cells

They found that lung cells with Alpha made drastically less interferon, a protein that switches on a host of immune defenses.

Alpha disables the first line of immune defense in our bodies, giving the variant more time to multiply.

They found that Alpha-infected cells make a lot of extra copies — some 80 times more than other versions of the virus — of a gene called Orf9b.

dampening the production of interferon and a full immune response. The virus, protected from attack, has better odds of making copies of itself.

people infected with Alpha have a more robust reaction than they would with other variants, coughing and shedding virus-laden mucus from not only their mouths, but also their noses — making Alpha even better at spreading.

. They may have independently evolved their own tricks for manipulating our immune system.

But studies on people who recover naturally from Covid-19 have shown that their immune systems learn to recognize other viral proteins, including Orf9b.

“It’s quite a tricky enterprise, but becoming more possible as we learn more,”

Biologists call this need “sleep pressure”: Stay up too late, build up sleep pressure. Feeling drowsy in the evenings? Of course you are—by being awake all day, you’ve been generating sleep pressure! But like “dark matter,” this is a name for something whose nature we do not yet understand.

The search for the hypnotoxin was not unsuccessful. There are a handful of substances clearly demonstrated to cause sleep—including a molecule called adenosine, which appears to build up in certain parts of the brains of waking rats, then drain away during slumber. Adenosine is particularly interesting because it is adenosine receptors that caffeine seems to work on.

For instance, if adenosine puts us under at the moment of transition from wakefulness to sleep, where does it come from? “Nobody knows,” remarks Michael Lazarus, a researcher at the institute who studies adenosine. Some people say it’s coming from neurons, some say it’s another class of brain cells. But there isn’t a consensus. At any rate, “this isn’t about storage,” says Yanagisawa. In other words, these substances themselves don’t seem to store information about sleep pressure. They are just a response to it.

A few years ago, the group discovered a mouse that just could not seem to get rid of its sleep pressure. Its EEGs suggested it lived a life of snoozy exhaustion, and mice that had been engineered to carry its mutation showed the same symptoms. “This mutant has more high-amplitude sleep waves than normal. It’s always sleep-deprived,” says Yanagisawa. The mutation was in a gene called SIK3. The longer the mutants stay awake, the more chemical tags the SIK3 protein accumulates. The researchers published their discovery of the SIK3 mutants, as well as another sleep mutant, in Nature in 2016.

Gillespie has seen this same pattern before, among Hawaii’s long-jawed goblin spiders. Each island has its own representatives of the four basic types: green, maroon, small brown, and large brown. At first, Gillespie assumed that all the green species were related to each other. But the spiders’ DNA revealed that the ones that live on the same islands are most closely related, regardless of their colors. They too have hopped from one island to another, radiating into the same four varieties wherever they land.

One of the most common misunderstandings about evolution is that it is a random process. Mutations are random, yes, but those mutations then rise and fall in ways that are anything but random. That’s why stick spiders, when they invade a new island, don’t diversify into red species, or zebra-striped ones. The environment of Hawaii sculpts their bodies in a limited number of ways.

Gillespie adds that there’s an urgency to this work. For millions of years, islands like Hawaii have acted as crucibles of evolution, allowing living things to replay evolution’s tape in the way that Gould envisaged. But in a much shorter time span, humans have threatened the results of those natural experiments. “The Hawaiian islands are in dire trouble from invasive species, and environmental modifications,” says Gillespie. “And you have all these unknown groups of spiders—entire lineages of really beautiful, charismatic animals, most of which are undescribed.”

Dr. Arthur Beaudet, a professor at the Baylor College of Medicine and the chairman of its department of molecular and human genetics, applaud the effort. He believes that the acts committed by men like Mr. Lanza and the gunmen in other rampages in recent years — at Columbine High School and in Aurora, Colo., in Norway, in Tucson and at Virginia Tech — are so far off the charts of normal behavior that there must be genetic changes driving them.

Everything known about mental illness, these skeptics say, argues that there are likely to be hundreds of genes involved in extreme violent behavior, not to mention a variety of environmental influences, and that all of these factors can interact in complex and unpredictable ways.

The National Institutes of Health was embroiled in controversy about 20 years ago simply for proposing to study the biological underpinnings of violence. Critics accused researchers of racism and singling out minorities, especially black men.

Studies of people at the far end of a bell curve can be especially informative, because the genetic roots of their conditions can be stark and easy to spot, noted J. H. Pate Skene, a Duke University neurobiologist. “I think doing research on outliers, people at an end of a spectrum on something of concern like violent behavior, is certainly a good idea,” he said, but he advised tempering expectations.

“If we know someone has a 2 percent chance or a 10 percent chance or a 20 percent chance of violent behavior, what would you do with that person?” Dr. Skene said. “They have not been convicted of anything — have not done anything wrong.”

Ultimately, understanding the genetics of violence might enable researchers to find ways to intervene before a person commits a horrific crime. But that goal would be difficult to achieve, and the pursuit of it risks jeopardizing personal liberties.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Just as the loss of a species decreases the richness of an ecosystem, the addition of new animals could achieve the opposite effect.

National Geographic Society hosted a larger conference to debate the scientific and ethical questions raised by the prospect of “de-extinction.

“De-extinction went from concept to potential reality right before our eyes,

less scientific, if more persuasive, argument was advanced by the ethicist Hank Greely and the law professor Jacob Sherkow, both of Stanford. De-extinction should be pursued, they argued in a paper published in Science, because it would be really

“This may be the biggest attraction and possibly the biggest benefit of de-extinction. It would surely be very cool to see a living woolly mammoth.”

They will replace chunks of band-tailed-pigeon DNA with synthesized chunks of passenger-pigeon DNA, until the cell’s genome matches their working passenger-pigeon genome.

Scientists predict that changes made by human beings to the composition of the atmosphere could kill off a quarter of the planet’s mammal species, a fifth of its reptiles and a sixth of its birds by 2050

This cloning method, called somatic cell nuclear transfer, can be used only on species for which we have cellular material.

There is a shortcut. The genome of a closely related species will have a high proportion of identical DNA, so it can serve as a blueprint, or “scaffold.”

By comparing the fragments of passenger-pigeon DNA with the genomes of similar species, researchers can assemble an approximation of an actual passenger-pigeon genome.

As with any translation, there may be errors of grammar, clumsy phrases and perhaps a few missing passages, but the book will be legible. It should, at least, tell a good story.

the genome will have to be inscribed into a living cell.

“We’ve framed it in terms of conservation,”

MAGE (Multiplex Automated Genome Engineering). MAGE is nicknamed the “evolution machine” because it can introduce the equivalent of millions of years of genetic mutations within minutes

Developmental and behavioral biologists would take over, just in time to answer some difficult questions. Chicks imitate their parents’ behavior. How do you raise a passenger pigeon without parents of its own species? And how do you train band-tailed pigeons to nurture the strange spawn that emerge from their eggs; chicks that, to them, might seem monstrous: an avian Rosemary’s Baby?

For endangered species with tiny populations, scientists would introduce genetic diversity to offset inbreeding.

They will try to alter the birds’ diets, migration habits and environment. The behavior of each subsequent generation will more closely resemble that of their genetic cousins.

For species threatened by contagion, an effort would be made to fortify their DNA with genes that make them disease-resistant

The scientific term for this type of genetic intervention is “facilitated adaptation.”

This optimistic, soft-focus fantasy of de-extinction, while thrilling to Ben Novak, is disturbing to many conservation biologists, who consider it a threat to their entire discipline and even to the environmental movement.

The first question posed by conservationists addresses the logic of bringing back an animal whose native habitat has disappeared. Why go through all the trouble just to have the animal go extinct all over again?

There is also anxiety about disease

“If you recreate a species genetically and release it, and that genotype is based on a bird from a 100-year-old environment, you probably will increase risk.”

“There’s always this fear that somehow, if we do it, we’re going to accidentally make something horrible, because only nature can really do it right. But nature is totally random. Nature makes monsters. Nature makes threats. Many of the things that are most threatening to us are a product of nature. Revive & Restore is not going to tip the balance in any way.”

De-extinction also poses a rhetorical threat to conservation biologists. The specter of extinction has been the conservation movement’s most powerful argument. What if extinction begins to be seen as a temporary inconvenience?

De-extinction suggests that we can technofix our way out of environmental issues generally, and that’s very, very bad.

How will we decide which species to resurrect?

Philip Seddon recently published a 10-point checklist to determine the suitability of any species for revival, taking into account causes of its extinction, possible threats it might face upon resurrection and man’s ability to destroy the species “in the event of unacceptable ecological or socioeconomic impacts.”

But the most visceral argument against de-extinction is animal cruelty.

“Is it fair to do this to these animals?” Shapiro asked. “Is ‘because we feel guilty’ a good-enough reason?” Stewart Brand made a utilitarian counterargument: “We’re going to go through some suffering, because you try a lot of times, and you get ones that don’t take. On the other hand, if you can bring bucardos back, then how many would get to live that would not have gotten to live?”

In “How to Permit Your Mammoth,” published in The Stanford Environmental Law Journal, Norman F. Carlin asks whether revived species should be protected by the Endangered Species Act or regulated as a genetically modified organism.

He concludes that revived species, “as products of human ingenuity,” should be eligible for patenting.

The term “de-extinction” is misleading. Passenger pigeons will not rise from the grave

Our understanding of the passenger pigeon’s behavior derives entirely from historical accounts.

There is no authoritative definition of “species.” The most widely accepted definition describes a group of organisms that can procreate with one another and produce fertile offspring, but there are many exceptions.

Theseus’ ship, therefore, “became a standing example among the philosophers . . . one side holding that the ship remained the same, and the other contending that it was not the same.”

What is coming will go well beyond the resurrection of extinct species. For millenniums, we have customized our environment, our vegetables and our animals, through breeding, fertilization and pollination. Synthetic biology offers far more sophisticated tools. The creation of novel organisms, like new animals, plants and bacteria, will transform human medicine, agriculture, energy production and much else.