Tag Archives: evolution

#432036 The Power to Upgrade Our Own Biology Is ...

Upgrading our biology may sound like science fiction, but attempts to improve humanity actually date back thousands of years. Every day, we enhance ourselves through seemingly mundane activities such as exercising, meditating, or consuming performance-enhancing drugs, such as caffeine or adderall. However, the tools with which we upgrade our biology are improving at an accelerating rate and becoming increasingly invasive.

In recent decades, we have developed a wide array of powerful methods, such as genetic engineering and brain-machine interfaces, that are redefining our humanity. In the short run, such enhancement technologies have medical applications and may be used to treat many diseases and disabilities. Additionally, in the coming decades, they could allow us to boost our physical abilities or even digitize human consciousness.

What’s New?
Many futurists argue that our devices, such as our smartphones, are already an extension of our cortex and in many ways an abstract form of enhancement. According to philosophers Andy Clark and David Chalmers’ theory of extended mind, we use technology to expand the boundaries of the human mind beyond our skulls.

One can argue that having access to a smartphone enhances one’s cognitive capacities and abilities and is an indirect form of enhancement of its own. It can be considered an abstract form of brain-machine interface. Beyond that, wearable devices and computers are already accessible in the market, and people like athletes use them to boost their progress.

However, these interfaces are becoming less abstract.

Not long ago, Elon Musk announced a new company, Neuralink, with the goal of merging the human mind with AI. The past few years have seen remarkable developments in both the hardware and software of brain-machine interfaces. Experts are designing more intricate electrodes while programming better algorithms to interpret neural signals. Scientists have already succeeded in enabling paralyzed patients to type with their minds, and are even allowing brains to communicate with one another purely through brainwaves.

Ethical Challenges of Enhancement
There are many social and ethical implications of such advancements.

One of the most fundamental issues with cognitive and physical enhancement techniques is that they contradict the very definition of merit and success that society has relied on for millennia. Many forms of performance-enhancing drugs have been considered “cheating” for the longest time.

But perhaps we ought to revisit some of our fundamental assumptions as a society.

For example, we like to credit hard work and talent in a fair manner, where “fair” generally implies that an individual has acted in a way that has served him to merit his rewards. If you are talented and successful, it is considered to be because you chose to work hard and take advantage of the opportunities available to you. But by these standards, how much of our accomplishments can we truly be credited for?

For instance, the genetic lottery can have an enormous impact on an individual’s predisposition and personality, which can in turn affect factors such as motivation, reasoning skills, and other mental abilities. Many people are born with a natural ability or a physique that gives them an advantage in a particular area or predisposes them to learn faster. But is it justified to reward someone for excellence if their genes had a pivotal role in their path to success?

Beyond that, there are already many ways in which we take “shortcuts” to better mental performance. Seemingly mundane activities like drinking coffee, meditating, exercising, or sleeping well can boost one’s performance in any given area and are tolerated by society. Even the use of language can have positive physical and psychological effects on the human brain, which can be liberating to the individual and immensely beneficial to society at large. And let’s not forget the fact that some of us are born into more access to developing literacy than others.

Given all these reasons, one could argue that cognitive abilities and talents are currently derived more from uncontrollable factors and luck than we like to admit. If anything, technologies like brain-machine interfaces can enhance individual autonomy and allow one a choice of how capable they become.

As Karim Jebari points out (pdf), if a certain characteristic or trait is required to perform a particular role and an individual lacks this trait, would it be wrong to implement the trait through brain-machine interfaces or genetic engineering? How is this different from any conventional form of learning or acquiring a skill? If anything, this would be removing limitations on individuals that result from factors outside their control, such as biological predisposition (or even traits induced from traumatic experiences) to act or perform in a certain way.

Another major ethical concern is equality. As with any other emerging technology, there are valid concerns that cognitive enhancement tech will benefit only the wealthy, thus exacerbating current inequalities. This is where public policy and regulations can play a pivotal role in the impact of technology on society.

Enhancement technologies can either contribute to inequality or allow us to solve it. Educating and empowering the under-privileged can happen at a much more rapid rate, helping the overall rate of human progress accelerate. The “normal range” for human capacity and intelligence, however it is defined, could shift dramatically towards more positive trends.

Many have also raised concerns over the negative applications of government-led biological enhancement, including eugenics-like movements and super-soldiers. Naturally, there are also issues of safety, security, and well-being, especially within the early stages of experimentation with enhancement techniques.

Brain-machine interfaces, for instance, could have implications on autonomy. The interface involves using information extracted from the brain to stimulate or modify systems in order to accomplish a goal. This part of the process can be enhanced by implementing an artificial intelligence system onto the interface—one that exposes the possibility of a third party potentially manipulating individual’s personalities, emotions, and desires by manipulating the interface.

A Tool For Transcendence
It’s important to discuss these risks, not so that we begin to fear and avoid such technologies, but so that we continue to advance in a way that minimizes harm and allows us to optimize the benefits.

Stephen Hawking notes that “with genetic engineering, we will be able to increase the complexity of our DNA, and improve the human race.” Indeed, the potential advantages of modifying biology are revolutionary. Doctors would gain access to a powerful tool to tackle disease, allowing us to live longer and healthier lives. We might be able to extend our lifespan and tackle aging, perhaps a critical step to becoming a space-faring species. We may begin to modify the brain’s building blocks to become more intelligent and capable of solving grand challenges.

In their book Evolving Ourselves, Juan Enriquez and Steve Gullans describe a world where evolution is no longer driven by natural processes. Instead, it is driven by human choices, through what they call unnatural selection and non-random mutation. Human enhancement is bringing us closer to such a world—it could allow us to take control of our evolution and truly shape the future of our species.

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#431869 When Will We Finally Achieve True ...

The field of artificial intelligence goes back a long way, but many consider it was officially born when a group of scientists at Dartmouth College got together for a summer, back in 1956. Computers had, over the last few decades, come on in incredible leaps and bounds; they could now perform calculations far faster than humans. Optimism, given the incredible progress that had been made, was rational. Genius computer scientist Alan Turing had already mooted the idea of thinking machines just a few years before. The scientists had a fairly simple idea: intelligence is, after all, just a mathematical process. The human brain was a type of machine. Pick apart that process, and you can make a machine simulate it.
The problem didn’t seem too hard: the Dartmouth scientists wrote, “We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.” This research proposal, by the way, contains one of the earliest uses of the term artificial intelligence. They had a number of ideas—maybe simulating the human brain’s pattern of neurons could work and teaching machines the abstract rules of human language would be important.
The scientists were optimistic, and their efforts were rewarded. Before too long, they had computer programs that seemed to understand human language and could solve algebra problems. People were confidently predicting there would be a human-level intelligent machine built within, oh, let’s say, the next twenty years.
It’s fitting that the industry of predicting when we’d have human-level intelligent AI was born at around the same time as the AI industry itself. In fact, it goes all the way back to Turing’s first paper on “thinking machines,” where he predicted that the Turing Test—machines that could convince humans they were human—would be passed in 50 years, by 2000. Nowadays, of course, people are still predicting it will happen within the next 20 years, perhaps most famously Ray Kurzweil. There are so many different surveys of experts and analyses that you almost wonder if AI researchers aren’t tempted to come up with an auto reply: “I’ve already predicted what your question will be, and no, I can’t really predict that.”
The issue with trying to predict the exact date of human-level AI is that we don’t know how far is left to go. This is unlike Moore’s Law. Moore’s Law, the doubling of processing power roughly every couple of years, makes a very concrete prediction about a very specific phenomenon. We understand roughly how to get there—improved engineering of silicon wafers—and we know we’re not at the fundamental limits of our current approach (at least, not until you’re trying to work on chips at the atomic scale). You cannot say the same about artificial intelligence.
Common Mistakes
Stuart Armstrong’s survey looked for trends in these predictions. Specifically, there were two major cognitive biases he was looking for. The first was the idea that AI experts predict true AI will arrive (and make them immortal) conveniently just before they’d be due to die. This is the “Rapture of the Nerds” criticism people have leveled at Kurzweil—his predictions are motivated by fear of death, desire for immortality, and are fundamentally irrational. The ability to create a superintelligence is taken as an article of faith. There are also criticisms by people working in the AI field who know first-hand the frustrations and limitations of today’s AI.
The second was the idea that people always pick a time span of 15 to 20 years. That’s enough to convince people they’re working on something that could prove revolutionary very soon (people are less impressed by efforts that will lead to tangible results centuries down the line), but not enough for you to be embarrassingly proved wrong. Of the two, Armstrong found more evidence for the second one—people were perfectly happy to predict AI after they died, although most didn’t, but there was a clear bias towards “15–20 years from now” in predictions throughout history.
Measuring Progress
Armstrong points out that, if you want to assess the validity of a specific prediction, there are plenty of parameters you can look at. For example, the idea that human-level intelligence will be developed by simulating the human brain does at least give you a clear pathway that allows you to assess progress. Every time we get a more detailed map of the brain, or successfully simulate another part of it, we can tell that we are progressing towards this eventual goal, which will presumably end in human-level AI. We may not be 20 years away on that path, but at least you can scientifically evaluate the progress.
Compare this to those that say AI, or else consciousness, will “emerge” if a network is sufficiently complex, given enough processing power. This might be how we imagine human intelligence and consciousness emerged during evolution—although evolution had billions of years, not just decades. The issue with this is that we have no empirical evidence: we have never seen consciousness manifest itself out of a complex network. Not only do we not know if this is possible, we cannot know how far away we are from reaching this, as we can’t even measure progress along the way.
There is an immense difficulty in understanding which tasks are hard, which has continued from the birth of AI to the present day. Just look at that original research proposal, where understanding human language, randomness and creativity, and self-improvement are all mentioned in the same breath. We have great natural language processing, but do our computers understand what they’re processing? We have AI that can randomly vary to be “creative,” but is it creative? Exponential self-improvement of the kind the singularity often relies on seems far away.
We also struggle to understand what’s meant by intelligence. For example, AI experts consistently underestimated the ability of AI to play Go. Many thought, in 2015, it would take until 2027. In the end, it took two years, not twelve. But does that mean AI is any closer to being able to write the Great American Novel, say? Does it mean it’s any closer to conceptually understanding the world around it? Does it mean that it’s any closer to human-level intelligence? That’s not necessarily clear.
Not Human, But Smarter Than Humans
But perhaps we’ve been looking at the wrong problem. For example, the Turing test has not yet been passed in the sense that AI cannot convince people it’s human in conversation; but of course the calculating ability, and perhaps soon the ability to perform other tasks like pattern recognition and driving cars, far exceed human levels. As “weak” AI algorithms make more decisions, and Internet of Things evangelists and tech optimists seek to find more ways to feed more data into more algorithms, the impact on society from this “artificial intelligence” can only grow.
It may be that we don’t yet have the mechanism for human-level intelligence, but it’s also true that we don’t know how far we can go with the current generation of algorithms. Those scary surveys that state automation will disrupt society and change it in fundamental ways don’t rely on nearly as many assumptions about some nebulous superintelligence.
Then there are those that point out we should be worried about AI for other reasons. Just because we can’t say for sure if human-level AI will arrive this century, or never, it doesn’t mean we shouldn’t prepare for the possibility that the optimistic predictors could be correct. We need to ensure that human values are programmed into these algorithms, so that they understand the value of human life and can act in “moral, responsible” ways.
Phil Torres, at the Project for Future Human Flourishing, expressed it well in an interview with me. He points out that if we suddenly decided, as a society, that we had to solve the problem of morality—determine what was right and wrong and feed it into a machine—in the next twenty years…would we even be able to do it?
So, we should take predictions with a grain of salt. Remember, it turned out the problems the AI pioneers foresaw were far more complicated than they anticipated. The same could be true today. At the same time, we cannot be unprepared. We should understand the risks and take our precautions. When those scientists met in Dartmouth in 1956, they had no idea of the vast, foggy terrain before them. Sixty years later, we still don’t know how much further there is to go, or how far we can go. But we’re going somewhere.
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#431836 Do Our Brains Use Deep Learning to Make ...

The first time Dr. Blake Richards heard about deep learning, he was convinced that he wasn’t just looking at a technique that would revolutionize artificial intelligence. He also knew he was looking at something fundamental about the human brain.
That was the early 2000s, and Richards was taking a course with Dr. Geoff Hinton at the University of Toronto. Hinton, a pioneer architect of the algorithm that would later take the world by storm, was offering an introductory course on his learning method inspired by the human brain.
The key words here are “inspired by.” Despite Richards’ conviction, the odds were stacked against him. The human brain, as it happens, seems to lack a critical function that’s programmed into deep learning algorithms. On the surface, the algorithms were violating basic biological facts already proven by neuroscientists.
But what if, superficial differences aside, deep learning and the brain are actually compatible?
Now, in a new study published in eLife, Richards, working with DeepMind, proposed a new algorithm based on the biological structure of neurons in the neocortex. Also known as the cortex, this outermost region of the brain is home to higher cognitive functions such as reasoning, prediction, and flexible thought.
The team networked their artificial neurons together into a multi-layered network and challenged it with a classic computer vision task—identifying hand-written numbers.
The new algorithm performed well. But the kicker is that it analyzed the learning examples in a way that’s characteristic of deep learning algorithms, even though it was completely based on the brain’s fundamental biology.
“Deep learning is possible in a biological framework,” concludes the team.
Because the model is only a computer simulation at this point, Richards hopes to pass the baton to experimental neuroscientists, who could actively test whether the algorithm operates in an actual brain.
If so, the data could then be passed back to computer scientists to work out the next generation of massively parallel and low-energy algorithms to power our machines.
It’s a first step towards merging the two fields back into a “virtuous circle” of discovery and innovation.
The blame game
While you’ve probably heard of deep learning’s recent wins against humans in the game of Go, you might not know the nitty-gritty behind the algorithm’s operations.
In a nutshell, deep learning relies on an artificial neural network with virtual “neurons.” Like a towering skyscraper, the network is structured into hierarchies: lower-level neurons process aspects of an input—for example, a horizontal or vertical stroke that eventually forms the number four—whereas higher-level neurons extract more abstract aspects of the number four.
To teach the network, you give it examples of what you’re looking for. The signal propagates forward in the network (like climbing up a building), where each neuron works to fish out something fundamental about the number four.
Like children trying to learn a skill the first time, initially the network doesn’t do so well. It spits out what it thinks a universal number four should look like—think a Picasso-esque rendition.
But here’s where the learning occurs: the algorithm compares the output with the ideal output, and computes the difference between the two (dubbed “error”). This error is then “backpropagated” throughout the entire network, telling each neuron: hey, this is how far off you were, so try adjusting your computation closer to the ideal.
Millions of examples and tweakings later, the network inches closer to the desired output and becomes highly proficient at the trained task.
This error signal is crucial for learning. Without efficient “backprop,” the network doesn’t know which of its neurons are off kilter. By assigning blame, the AI can better itself.
The brain does this too. How? We have no clue.
Biological No-Go
What’s clear, though, is that the deep learning solution doesn’t work.
Backprop is a pretty needy function. It requires a very specific infrastructure for it to work as expected.
For one, each neuron in the network has to receive the error feedback. But in the brain, neurons are only connected to a few downstream partners (if that). For backprop to work in the brain, early-level neurons need to be able to receive information from billions of connections in their downstream circuits—a biological impossibility.
And while certain deep learning algorithms adapt a more local form of backprop— essentially between neurons—it requires their connection forwards and backwards to be symmetric. This hardly ever occurs in the brain’s synapses.
More recent algorithms adapt a slightly different strategy, in that they implement a separate feedback pathway that helps the neurons to figure out errors locally. While it’s more biologically plausible, the brain doesn’t have a separate computational network dedicated to the blame game.
What it does have are neurons with intricate structures, unlike the uniform “balls” that are currently applied in deep learning.
Branching Networks
The team took inspiration from pyramidal cells that populate the human cortex.
“Most of these neurons are shaped like trees, with ‘roots’ deep in the brain and ‘branches’ close to the surface,” says Richards. “What’s interesting is that these roots receive a different set of inputs than the branches that are way up at the top of the tree.”
This is an illustration of a multi-compartment neural network model for deep learning. Left: Reconstruction of pyramidal neurons from mouse primary visual cortex. Right: Illustration of simplified pyramidal neuron models. Image Credit: CIFAR
Curiously, the structure of neurons often turn out be “just right” for efficiently cracking a computational problem. Take the processing of sensations: the bottoms of pyramidal neurons are right smack where they need to be to receive sensory input, whereas the tops are conveniently placed to transmit feedback errors.
Could this intricate structure be evolution’s solution to channeling the error signal?
The team set up a multi-layered neural network based on previous algorithms. But rather than having uniform neurons, they gave those in middle layers—sandwiched between the input and output—compartments, just like real neurons.
When trained with hand-written digits, the algorithm performed much better than a single-layered network, despite lacking a way to perform classical backprop. The cell-like structure itself was sufficient to assign error: the error signals at one end of the neuron are naturally kept separate from input at the other end.
Then, at the right moment, the neuron brings both sources of information together to find the best solution.
There’s some biological evidence for this: neuroscientists have long known that the neuron’s input branches perform local computations, which can be integrated with signals that propagate backwards from the so-called output branch.
However, we don’t yet know if this is the brain’s way of dealing blame—a question that Richards urges neuroscientists to test out.
What’s more, the network parsed the problem in a way eerily similar to traditional deep learning algorithms: it took advantage of its multi-layered structure to extract progressively more abstract “ideas” about each number.
“[This is] the hallmark of deep learning,” the authors explain.
The Deep Learning Brain
Without doubt, there will be more twists and turns to the story as computer scientists incorporate more biological details into AI algorithms.
One aspect that Richards and team are already eyeing is a top-down predictive function, in which signals from higher levels directly influence how lower levels respond to input.
Feedback from upper levels doesn’t just provide error signals; it could also be nudging lower processing neurons towards a “better” activity pattern in real-time, says Richards.
The network doesn’t yet outperform other non-biologically derived (but “brain-inspired”) deep networks. But that’s not the point.
“Deep learning has had a huge impact on AI, but, to date, its impact on neuroscience has been limited,” the authors say.
Now neuroscientists have a lead they could experimentally test: that the structure of neurons underlie nature’s own deep learning algorithm.
“What we might see in the next decade or so is a real virtuous cycle of research between neuroscience and AI, where neuroscience discoveries help us to develop new AI and AI can help us interpret and understand our experimental data in neuroscience,” says Richards.
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#431690 Oxford Study Says Alien Life Would ...

The alternative universe known as science fiction has given our culture a menagerie of alien species. From overstuffed teddy bears like Ewoks and Wookies to terrifying nightmares such as Alien and Predator, our collective imagination of what form alien life from another world may take has been irrevocably imprinted by Hollywood.
It might all be possible, or all these bug-eyed critters might turn out to be just B-movie versions of how real extraterrestrials will appear if and when they finally make the evening news.
One thing for certain is that aliens from another world will be shaped by the same evolutionary forces as here on Earth—natural selection. That’s the conclusion of a team of scientists from the University of Oxford in a study published this month in the International Journal of Astrobiology.
A complex alien that comprises a hierarchy of entities, where each lower level collection of entities has aligned evolutionary interests.Image Credit: Helen S. Cooper/University of Oxford.
The researchers suggest that evolutionary theory—famously put forth by Charles Darwin in his seminal book On the Origin of Species 158 years ago this month—can be used to make some predictions about alien species. In particular, the team argues that extraterrestrials will undergo natural selection, because that is the only process by which organisms can adapt to their environment.
“Adaptation is what defines life,” lead author Samuel Levin tells Singularity Hub.
While it’s likely that NASA or some SpaceX-like private venture will eventually kick over a few space rocks and discover microbial life in the not-too-distant future, the sorts of aliens Levin and his colleagues are interested in describing are more complex. That’s because natural selection is at work.
A quick evolutionary theory 101 refresher: Natural selection is the process by which certain traits are favored over others in a given population. For example, take a group of brown and green beetles. It just so happens that birds prefer foraging on green beetles, allowing more brown beetles to survive and reproduce than the more delectable green ones. Eventually, if these population pressures persist, brown beetles will become the dominant type. Brown wins, green loses.
And just as human beings are the result of millions of years of adaptations—eyes and thumbs, for example—aliens will similarly be constructed from parts that were once free living but through time came together to work as one organism.
“Life has so many intricate parts, so much complexity, for that to happen (randomly),” Levin explains. “It’s too complex and too many things working together in a purposeful way for that to happen by chance, as how certain molecules come about. Instead you need a process for making it, and natural selection is that process.”
Just don’t expect ET to show up as a bipedal humanoid with a large head and almond-shaped eyes, Levin says.
“They can be built from entirely different chemicals and so visually, superficially, unfamiliar,” he explains. “They will have passed through the same evolutionary history as us. To me, that’s way cooler and more exciting than them having two legs.”
Need for Data
Seth Shostak, a lead astronomer at the SETI Institute and host of the organization’s Big Picture Science radio show, wrote that while the argument is interesting, it doesn’t answer the question of ET’s appearance.
Shostak argues that a more productive approach would invoke convergent evolution, where similar environments lead to similar adaptations, at least assuming a range of Earth-like conditions such as liquid oceans and thick atmospheres. For example, an alien species that evolved in a liquid environment would evolve a streamlined body to move through water.
“Happenstance and the specifics of the environment will produce variations on an alien species’ planet as it has on ours, and there’s really no way to predict these,” Shostak concludes. “Alas, an accurate cosmic bestiary cannot be written by the invocation of biological mechanisms alone. We need data. That requires more than simply thinking about alien life. We need to actually discover it.”
Search Is On
The search is on. On one hand, the task seems easy enough: There are at least 100 billion planets in the Milky Way alone, and at least 20 percent of those are likely to be capable of producing a biosphere. Even if the evolution of life is exceedingly rare—take a conservative estimate of .001 percent or 200,000 planets, as proposed by the Oxford paper—you have to like the odds.
Of course, it’s not that easy by a billion light years.
Planet hunters can’t even agree on what signatures of life they should focus on. The idea is that where there’s smoke there’s fire. In the case of an alien world home to biological life, astrobiologists are searching for the presence of “biosignature gases,” vapors that could only be produced by alien life.
As Quanta Magazine reported, scientists do this by measuring a planet’s atmosphere against starlight. Gases in the atmosphere absorb certain frequencies of starlight, offering a clue as to what is brewing around a particular planet.
The presence of oxygen would seem to be a biological no-brainer, but there are instances where a planet can produce a false positive, meaning non-biological processes are responsible for the exoplanet’s oxygen. Scientists like Sara Seager, an astrophysicist at MIT, have argued there are plenty of examples of other types of gases produced by organisms right here on Earth that could also produce the smoking gun, er, planet.

Life as We Know It
Indeed, the existence of Earth-bound extremophiles—organisms that defy conventional wisdom about where life can exist, such as in the vacuum of space—offer another clue as to what kind of aliens we might eventually meet.
Lynn Rothschild, an astrobiologist and synthetic biologist in the Earth Science Division at NASA’s Ames Research Center in Silicon Valley, takes extremophiles as a baseline and then supersizes them through synthetic biology.
For example, say a bacteria is capable of surviving at 120 degrees Celsius. Rothschild’s lab might tweak an organism’s DNA to see if it could metabolize at 150 degrees. The idea, as she explains, is to expand the envelope for life without ever getting into a rocket ship.

While researchers may not always agree on the “where” and “how” and “what” of the search for extraterrestrial life, most do share one belief: Alien life must be out there.
“It would shock me if there weren’t [extraterrestrials],” Levin says. “There are few things that would shock me more than to find out there aren’t any aliens…If I had to bet on it, I would bet on the side of there being lots and lots of aliens out there.”
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#431682 Oxford Study Says Alien Life Would ...

The alternative universe known as science fiction has given our culture a menagerie of alien species. From overstuffed teddy bears like Ewoks and Wookies to terrifying nightmares such as Alien and Predator, our collective imagination of what form alien life from another world may take has been irrevocably imprinted by Hollywood.
It might all be possible, or all these bug-eyed critters might turn out to be just B-movie versions of how real extraterrestrials will appear if and when they finally make the evening news.
One thing for certain is that aliens from another world will be shaped by the same evolutionary forces as here on Earth—natural selection. That’s the conclusion of a team of scientists from the University of Oxford in a study published this month in the International Journal of Astrobiology.
A complex alien that comprises a hierarchy of entities, where each lower level collection of entities has aligned evolutionary interests.Image Credit: Helen S. Cooper/University of Oxford.
The researchers suggest that evolutionary theory—famously put forth by Charles Darwin in his seminal book On the Origin of Species 158 years ago this month—can be used to make some predictions about alien species. In particular, the team argues that extraterrestrials will undergo natural selection, because that is the only process by which organisms can adapt to their environment.
“Adaptation is what defines life,” lead author Samuel Levin tells Singularity Hub.
While it’s likely that NASA or some SpaceX-like private venture will eventually kick over a few space rocks and discover microbial life in the not-too-distant future, the sorts of aliens Levin and his colleagues are interested in describing are more complex. That’s because natural selection is at work.
A quick evolutionary theory 101 refresher: Natural selection is the process by which certain traits are favored over others in a given population. For example, take a group of brown and green beetles. It just so happens that birds prefer foraging on green beetles, allowing more brown beetles to survive and reproduce than the more delectable green ones. Eventually, if these population pressures persist, brown beetles will become the dominant type. Brown wins, green loses.
And just as human beings are the result of millions of years of adaptations—eyes and thumbs, for example—aliens will similarly be constructed from parts that were once free living but through time came together to work as one organism.
“Life has so many intricate parts, so much complexity, for that to happen (randomly),” Levin explains. “It’s too complex and too many things working together in a purposeful way for that to happen by chance, as how certain molecules come about. Instead you need a process for making it, and natural selection is that process.”
Just don’t expect ET to show up as a bipedal humanoid with a large head and almond-shaped eyes, Levin says.
“They can be built from entirely different chemicals and so visually, superficially, unfamiliar,” he explains. “They will have passed through the same evolutionary history as us. To me, that’s way cooler and more exciting than them having two legs.”
Need for Data
Seth Shostak, a lead astronomer at the SETI Institute and host of the organization’s Big Picture Science radio show, wrote that while the argument is interesting, it doesn’t answer the question of ET’s appearance.
Shostak argues that a more productive approach would invoke convergent evolution, where similar environments lead to similar adaptations, at least assuming a range of Earth-like conditions such as liquid oceans and thick atmospheres. For example, an alien species that evolved in a liquid environment would evolve a streamlined body to move through water.
“Happenstance and the specifics of the environment will produce variations on an alien species’ planet as it has on ours, and there’s really no way to predict these,” Shostak concludes. “Alas, an accurate cosmic bestiary cannot be written by the invocation of biological mechanisms alone. We need data. That requires more than simply thinking about alien life. We need to actually discover it.”
Search is On
The search is on. On one hand, the task seems easy enough: There are at least 100 billion planets in the Milky Way alone, and at least 20 percent of those are likely to be capable of producing a biosphere. Even if the evolution of life is exceedingly rare—take a conservative estimate of .001 percent or 200,000 planets, as proposed by the Oxford paper—you have to like the odds.
Of course, it’s not that easy by a billion light years.
Planet hunters can’t even agree on what signatures of life they should focus on. The idea is that where there’s smoke there’s fire. In the case of an alien world home to biological life, astrobiologists are searching for the presence of “biosignature gases,” vapors that could only be produced by alien life.
As Quanta Magazine reported, scientists do this by measuring a planet’s atmosphere against starlight. Gases in the atmosphere absorb certain frequencies of starlight, offering a clue as to what is brewing around a particular planet.
The presence of oxygen would seem to be a biological no-brainer, but there are instances where a planet can produce a false positive, meaning non-biological processes are responsible for the exoplanet’s oxygen. Scientists like Sara Seager, an astrophysicist at MIT, have argued there are plenty of examples of other types of gases produced by organisms right here on Earth that could also produce the smoking gun, er, planet.

Life as We Know It
Indeed, the existence of Earth-bound extremophiles—organisms that defy conventional wisdom about where life can exist, such as in the vacuum of space—offer another clue as to what kind of aliens we might eventually meet.
Lynn Rothschild, an astrobiologist and synthetic biologist in the Earth Science Division at NASA’s Ames Research Center in Silicon Valley, takes extremophiles as a baseline and then supersizes them through synthetic biology.
For example, say a bacteria is capable of surviving at 120 degrees Celsius. Rothschild’s lab might tweak an organism’s DNA to see if it could metabolize at 150 degrees. The idea, as she explains, is to expand the envelope for life without ever getting into a rocket ship.

While researchers may not always agree on the “where” and “how” and “what” of the search for extraterrestrial life, most do share one belief: Alien life must be out there.
“It would shock me if there weren’t [extraterrestrials],” Levin says. “There are few things that would shock me more than to find out there aren’t any aliens…If I had to bet on it, I would bet on the side of there being lots and lots of aliens out there.”
Image Credit: NASA Continue reading

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