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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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Artificial intelligence has received its fair share of hype recently. However, it’s hype that’s well-founded: IDC predicts worldwide spend on AI and cognitive computing will culminate to a whopping $46 billion (with a “b”) by 2020, and all the tech giants are jumping on board faster than you can say “ROI.” But what is AI, exactly?
According to Hilary Mason, AI today is being misused as a sort of catch-all term to basically describe “any system that uses data to do anything.” But it’s so much more than that. A truly artificially intelligent system is one that learns on its own, one that’s capable of crunching copious amounts of data in order to create associations and intelligently mimic actual human behavior.
It’s what powers the technology anticipating our next online purchase (Amazon), or the virtual assistant that deciphers our voice commands with incredible accuracy (Siri), or even the hipster-friendly recommendation engine that helps you discover new music before your friends do (Pandora). But AI is moving past these consumer-pleasing “nice-to-haves” and getting down to serious business: saving our butts.
Much in the same way robotics entered manufacturing, AI is making its mark in healthcare by automating mundane, repetitive tasks. This is especially true in the case of detecting cancer. By leveraging the power of deep learning, algorithms can now be trained to distinguish between sets of pixels in an image that represents cancer versus sets that don’t—not unlike how Facebook’s image recognition software tags pictures of our friends without us having to type in their names first. This software can then go ahead and scour millions of medical images (MRIs, CT scans, etc.) in a single day to detect anomalies on a scope that humans just aren’t capable of. That’s huge.
As if that wasn’t enough, these algorithms are constantly learning and evolving, getting better at making these associations with each new data set that gets fed to them. Radiology, dermatology, and pathology will experience a giant upheaval as tech giants and startups alike jump in to bring these deep learning algorithms to a hospital near you.
In fact, some already are: the FDA recently gave their seal of approval for an AI-powered medical imaging platform that helps doctors analyze and diagnose heart anomalies. This is the first time the FDA has approved a machine learning application for use in a clinical setting.
But how efficient is AI compared to humans, really? Well, aside from the obvious fact that software programs don’t get bored or distracted or have to check Facebook every twenty minutes, AI is exponentially better than us at analyzing data.
Take, for example, IBM’s Watson. Watson analyzed genomic data from both tumor cells and healthy cells and was ultimately able to glean actionable insights in a mere 10 minutes. Compare that to the 160 hours it would have taken a human to analyze that same data. Diagnoses aside, AI is also being leveraged in pharmaceuticals to aid in the very time-consuming grunt work of discovering new drugs, and all the big players are getting involved.
But AI is far from being just a behind-the-scenes player. Gartner recently predicted that by 2025, 50 percent of the population will rely on AI-powered “virtual personal health assistants” for their routine primary care needs. What this means is that consumer-facing voice and chat-operated “assistants” (think Siri or Cortana) would, in effect, serve as a central hub of interaction for all our connected health devices and the algorithms crunching all our real-time biometric data. These assistants would keep us apprised of our current state of well-being, acting as a sort of digital facilitator for our personal health objectives and an always-on health alert system that would notify us when we actually need to see a physician.
Slowly, and thanks to the tsunami of data and advancements in self-learning algorithms, healthcare is transitioning from a reactive model to more of a preventative model—and it’s completely upending the way care is delivered. Whether Elon Musk’s dystopian outlook on AI holds any weight or not is yet to be determined. But one thing’s certain: for the time being, artificial intelligence is saving our lives.
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For every new piece of technology that gets developed, you can usually find people saying it will never be useful. The president of the Michigan Savings Bank in 1903, for example, said, “The horse is here to stay but the automobile is only a novelty—a fad.” It’s equally easy to find people raving about whichever new technology is at the peak of the Gartner Hype Cycle, which tracks the buzz around these newest developments and attempts to temper predictions. When technologies emerge, there are all kinds of uncertainties, from the actual capacity of the technology to its use cases in real life to the price tag.
Eventually the dust settles, and some technologies get widely adopted, to the extent that they can become “invisible”; people take them for granted. Others fall by the wayside as gimmicky fads or impractical ideas. Picking which horses to back is the difference between Silicon Valley millions and Betamax pub-quiz-question obscurity. For a while, it seemed that Google had—for once—backed the wrong horse.
Google Glass emerged from Google X, the ubiquitous tech giant’s much-hyped moonshot factory, where highly secretive researchers work on the sci-fi technologies of the future. Self-driving cars and artificial intelligence are the more mundane end for an organization that apparently once looked into jetpacks and teleportation.
The original smart glasses, Google began selling Google Glass in 2013 for $1,500 as prototypes for their acolytes, around 8,000 early adopters. Users could control the glasses with a touchpad, or, activated by tilting the head back, with voice commands. Audio relay—as with several wearable products—is via bone conduction, which transmits sound by vibrating the skull bones of the user. This was going to usher in the age of augmented reality, the next best thing to having a chip implanted directly into your brain.
On the surface, it seemed to be a reasonable proposition. People had dreamed about augmented reality for a long time—an onboard, JARVIS-style computer giving you extra information and instant access to communications without even having to touch a button. After smartphone ubiquity, it looked like a natural step forward.
Instead, there was a backlash. People may be willing to give their data up to corporations, but they’re less pleased with the idea that someone might be filming them in public. The worst aspect of smartphones is trying to talk to people who are distractedly scrolling through their phones. There’s a famous analogy in Revolutionary Road about an old couple’s loveless marriage: the husband tunes out his wife’s conversation by turning his hearing aid down to zero. To many, Google Glass seemed to provide us with a whole new way to ignore each other in favor of our Twitter feeds.
Then there’s the fact that, regardless of whether it’s because we’re not used to them, or if it’s a more permanent feature, people wearing AR tech often look very silly. Put all this together with a lack of early functionality, the high price (do you really feel comfortable wearing a $1,500 computer?), and a killer pun for the users—Glassholes—and the final recipe wasn’t great for Google.
Google Glass was quietly dropped from sale in 2015 with the ominous slogan posted on Google’s website “Thanks for exploring with us.” Reminding the Glass users that they had always been referred to as “explorers”—beta-testing a product, in many ways—it perhaps signaled less enthusiasm for wearables than the original, Google Glass skydive might have suggested.
In reality, Google went back to the drawing board. Not with the technology per se, although it has improved in the intervening years, but with the uses behind the technology.
Under what circumstances would you actually need a Google Glass? When would it genuinely be preferable to a smartphone that can do many of the same things and more? Beyond simply being a fashion item, which Google Glass decidedly was not, even the most tech-evangelical of us need a convincing reason to splash $1,500 on a wearable computer that’s less socially acceptable and less easy to use than the machine you’re probably reading this on right now.
Enter the Google Glass Enterprise Edition.
Piloted in factories during the years that Google Glass was dormant, and now roaring back to life and commercially available, the Google Glass relaunch got under way in earnest in July of 2017. The difference here was the specific audience: workers in factories who need hands-free computing because they need to use their hands at the same time.
In this niche application, wearable computers can become invaluable. A new employee can be trained with pre-programmed material that explains how to perform actions in real time, while instructions can be relayed straight into a worker’s eyeline without them needing to check a phone or switch to email.
Medical devices have long been a dream application for Google Glass. You can imagine a situation where people receive real-time information during surgery, or are augmented by artificial intelligence that provides additional diagnostic information or questions in response to a patient’s symptoms. The quest to develop a healthcare AI, which can provide recommendations in response to natural language queries, is on. The famously untidy doctor’s handwriting—and the associated death toll—could be avoided if the glasses could take dictation straight into a patient’s medical records. All of this is far more useful than allowing people to check Facebook hands-free while they’re riding the subway.
Google’s “Lens” application indicates another use for Google Glass that hadn’t quite matured when the original was launched: the Lens processes images and provides information about them. You can look at text and have it translated in real time, or look at a building or sign and receive additional information. Image processing, either through neural networks hooked up to a cloud database or some other means, is the frontier that enables driverless cars and similar technology to exist. Hook this up to a voice-activated assistant relaying information to the user, and you have your killer application: real-time annotation of the world around you. It’s this functionality that just wasn’t ready yet when Google launched Glass.
Amazon’s recent announcement that they want to integrate Alexa into a range of smart glasses indicates that the tech giants aren’t ready to give up on wearables yet. Perhaps, in time, people will become used to voice activation and interaction with their machines, at which point smart glasses with bone conduction will genuinely be more convenient than a smartphone.
But in many ways, the real lesson from the initial failure—and promising second life—of Google Glass is a simple question that developers of any smart technology, from the Internet of Things through to wearable computers, must answer. “What can this do that my smartphone can’t?” Find your answer, as the Enterprise Edition did, as Lens might, and you find your product.
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On a dark night, away from city lights, the stars of the Milky Way can seem uncountable. Yet from any given location no more than 4,500 are visible to the naked eye. Meanwhile, our galaxy has 100–400 billion stars, and there are even more galaxies in the universe.
The numbers of the night sky are humbling. And they give us a deep perspective…on drugs.
Yes, this includes wow-the-stars-are-freaking-amazing-tonight drugs, but also the kinds of drugs that make us well again when we’re sick. The number of possible organic compounds with “drug-like” properties dwarfs the number of stars in the universe by over 30 orders of magnitude.
Next to this multiverse of possibility, the chemical configurations scientists have made into actual medicines are like the smattering of stars you’d glimpse downtown.
But for good reason.
Exploring all that potential drug-space is as humanly impossible as exploring all of physical space, and even if we could, most of what we’d find wouldn’t fit our purposes. Still, the idea that wonder drugs must surely lurk amid the multitudes is too tantalizing to ignore.
Which is why, Alex Zhavoronkov said at Singularity University’s Exponential Medicine in San Diego last week, we should use artificial intelligence to do more of the legwork and speed discovery. This, he said, could be one of the next big medical applications for AI.
Dogs, Diagnosis, and Drugs
Zhavoronkov is CEO of Insilico Medicine and CSO of the Biogerontology Research Foundation. Insilico is one of a number of AI startups aiming to accelerate drug discovery with AI.
In recent years, Zhavoronkov said, the now-famous machine learning technique, deep learning, has made progress on a number of fronts. Algorithms that can teach themselves to play games—like DeepMind’s AlphaGo Zero or Carnegie Mellon’s poker playing AI—are perhaps the most headline-grabbing of the bunch. But pattern recognition was the thing that kicked deep learning into overdrive early on, when machine learning algorithms went from struggling to tell dogs and cats apart to outperforming their peers and then their makers in quick succession.
[Watch this video for an AI update from Neil Jacobstein, chair of Artificial Intelligence and Robotics at Singularity University.]
In medicine, deep learning algorithms trained on databases of medical images can spot life-threatening disease with equal or greater accuracy than human professionals. There’s even speculation that AI, if we learn to trust it, could be invaluable in diagnosing disease. And, as Zhavoronkov noted, with more applications and a longer track record that trust is coming.
“Tesla is already putting cars on the street,” Zhavoronkov said. “Three-year, four-year-old technology is already carrying passengers from point A to point B, at 100 miles an hour, and one mistake and you’re dead. But people are trusting their lives to this technology.”
“So, why don’t we do it in pharma?”
Trial and Error and Try Again
AI wouldn’t drive the car in pharmaceutical research. It’d be an assistant that, when paired with a chemist or two, could fast-track discovery by screening more possibilities for better candidates.
There’s plenty of room to make things more efficient, according to Zhavoronkov.
Drug discovery is arduous and expensive. Chemists sift tens of thousands of candidate compounds for the most promising to synthesize. Of these, a handful will go on to further research, fewer will make it to human clinical trials, and a fraction of those will be approved.
The whole process can take many years and cost hundreds of millions of dollars.
This is a big data problem if ever there was one, and deep learning thrives on big data. Early applications have shown their worth unearthing subtle patterns in huge training databases. Although drug-makers already use software to sift compounds, such software requires explicit rules written by chemists. AI’s allure is its ability to learn and improve on its own.
“There are two strategies for AI-driven innovation in pharma to ensure you get better molecules and much faster approvals,” Zhavoronkov said. “One is looking for the needle in the haystack, and another one is creating a new needle.”
To find the needle in the haystack, algorithms are trained on large databases of molecules. Then they go looking for molecules with attractive properties. But creating a new needle? That’s a possibility enabled by the generative adversarial networks Zhavoronkov specializes in.
Such algorithms pit two neural networks against each other. One generates meaningful output while the other judges whether this output is true or false, Zhavoronkov said. Together, the networks generate new objects like text, images, or in this case, molecular structures.
“We started employing this particular technology to make deep neural networks imagine new molecules, to make it perfect right from the start. So, to come up with really perfect needles,” Zhavoronkov said. “[You] can essentially go to this [generative adversarial network] and ask it to create molecules that inhibit protein X at concentration Y, with the highest viability, specific characteristics, and minimal side effects.”
Zhavoronkov believes AI can find or fabricate more needles from the array of molecular possibilities, freeing human chemists to focus on synthesizing only the most promising. If it works, he hopes we can increase hits, minimize misses, and generally speed the process up.
Proof’s in the Pudding
Insilico isn’t alone on its drug-discovery quest, nor is it a brand new area of interest.
Last year, a Harvard group published a paper on an AI that similarly suggests drug candidates. The software trained on 250,000 drug-like molecules and used its experience to generate new molecules that blended existing drugs and made suggestions based on desired properties.
An MIT Technology Review article on the subject highlighted a few of the challenges such systems may still face. The results returned aren’t always meaningful or easy to synthesize in the lab, and the quality of these results, as always, is only as good as the data dined upon.
Stanford chemistry professor and Andreesen Horowitz partner, Vijay Pande, said that images, speech, and text—three of the areas deep learning’s made quick strides in—have better, cleaner data. Chemical data, on the other hand, is still being optimized for deep learning. Also, while there are public databases, much data still lives behind closed doors at private companies.
To overcome the challenges and prove their worth, Zhavoronkov said, his company is very focused on validating the tech. But this year, skepticism in the pharmaceutical industry seems to be easing into interest and investment.
AI drug discovery startup Exscientia inked a deal with Sanofi for $280 million and GlaxoSmithKline for $42 million. Insilico is also partnering with GlaxoSmithKline, and Numerate is working with Takeda Pharmaceutical. Even Google may jump in. According to an article in Nature outlining the field, the firm’s deep learning project, Google Brain, is growing its biosciences team, and industry watchers wouldn’t be surprised to see them target drug discovery.
With AI and the hardware running it advancing rapidly, the greatest potential may yet be ahead. Perhaps, one day, all 1060 molecules in drug-space will be at our disposal. “You should take all the data you have, build n new models, and search as much of that 1060 as possible” before every decision you make, Brandon Allgood, CTO at Numerate, told Nature.
Today’s projects need to live up to their promises, of course, but Zhavoronkov believes AI will have a big impact in the coming years, and now’s the time to integrate it. “If you are working for a pharma company, and you’re still thinking, ‘Okay, where is the proof?’ Once there is a proof, and once you can see it to believe it—it’s going to be too late,” he said.
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