New Cloaking System Makes Connected Cars Untrackable

New Machine Learning Framework Uncovers Twitter's Vast Bot Population

Up to 15 percent of active Twitter accounts are really bots: autonomous agents driven by algorithms rather than actual human personalities. That's about 48 million fakes. The 15 percent figure comes courtesy of a new analysis by computer scientists at Indiana University and the University of Southern California using a machine learning framework designed to detect bots based on nearly a thousand distinct Twitter user characteristics. The group's work is described in a paper posted this week to the arXiv preprint server.

On its face, this is a classic machine learning classification problem. Take some properties of an entity—screen name length, account age, and number of retweets per hour, for example—and look at those same properties across many different versions or instances of that entity along with some other property that we want to predict (whether an account is a bot or a human, in our case). Crunch some linear algebra and you'll wind up with an abstracted model of that entity with respect to whatever it is we're making predictions about. 

This model is basically a formula that you can then feed in some so-far unseen observations and get a prediction in return. Like, if we take the above properties for many thousands or millions of Twitter accounts we should be able to return a model that will make predictions based on new observations of those properties. Machine learning is really just clever statistics.

Part of what makes the new research interesting is the sheer number of features used in the classification model. Just think of a Twitter account and then try to come up with as many different ways of describing it as you can: number of followers, age, username length, number of retweets, number of tweets, verified or not, average tweet length. Um. I could give you maybe 20 or so. Meanwhile, the researchers here are considering things like "positive emoticons entropy of single tweets," "time between two consecutive tweets," and "fraction of users [friends] with default profile and default picture."

To train their model, the researchers used publicly available datasets consisting of some 15,000 manually verified Twitter bots and 16,000 verified human accounts. (So, these are all accounts that at some point a real person sorted through and decided were human or bot.) They considered the most recent 200 tweets from each account as well as the most recent 100 tweets mentioning each account. That's about 2.6 million bot-generated tweets and 3 million human-generated tweets. Using the resulting model, the researchers went on to classify 14 million Twitter accounts, finding that between 9 and 15 percent of all accounts are likely bots.

There are a couple of interesting caveats here. "First, we do not exclude the possibility that very sophisticated bots can systematically escape a human annotator's judgement," the authors write. "These complex bots may be active on Twitter, and therefore present in our datasets, and may have been incorrectly labeled as humans, making even the 15 percent figure a conservative estimate. Second, increasing evidence suggests the presence on social media of hybrid human-bot accounts (sometimes referred to as cyborgs) that perform automated actions with some human supervision. Some have been allegedly used for terrorist propaganda and recruitment purposes. It remains unclear how these accounts should be labeled, and how pervasive they are."

Twitter itself has estimated that up to 8.5 percent of users, "used third party applications that may have automatically contacted our servers for regular updates without any discernible additional user-initiated action," according to a recent FEC filing. The company had no additional comment on its own classification methods or the current study. 

It's worth pointing out that not all bots are inherently evil. There are many bots out there that don't even really pretend to be human and provide automated emergency notifications or serve customer service roles. Though the study observed some clustering of different bot types—PR bots, link pushers, spam accounts with no followers, the aforementioned cyborgs—it's not totally clear where benevolent service-bots fit into the ecosystem. 

from New Machine Learning Framework Uncovers Twitter's Vast Bot Population

To Solve Opioid Addiction, We Still Need to Solve Pain

Airline Unveils Beer Calibrated To Taste Good at 30,000 Feet

When I'm cruising at 36,000 feet getting tossed around by some Midwestern turbulence, my primary concern when it comes to beer is that it does in fact contain alcohol. As such, my $7 warmish can offers a temporary respite from the anxieties of being stuck in a tube lurching around the lower stratosphere. For my purposes, the contents of that can might as well taste like the underside of a tray table, but apparently other passengers have different priorities. They actually want it to taste good, like they're not even gripped by terror. Weird.

For those people, the Hong Kong-based airline Cathay Pacific has developed its very own hand-crafted beer scientifically designed to taste better at 30,000 feet. Betsy Beer, named after the airline's very first aircraft, will be served to passengers in first and business classes on flights between Hong Kong and destinations in the UK, according to an airline press release.

It sounds pretty tasty: "The inclusion of 'Dragon Eye' fruit is a unique characteristic of the beverage. Known for its aromatic properties, the fruit adds to the round, rich, textural properties that make the beer distinctive. This flavour is enhanced further by the inclusion of a small component of New Territories'-sourced honey in the  brewing process, giving the beer agreeable floral notes, while the use of Fuggle, a revered hop and a mainstay of traditional British craft ales, lends it a pleasingly earthy and full-bodied flavour." Would consume.

It's well-known that high altitudes and the aircraft cabin environment screw with your sense of taste. A 2010 study commissioned by Lufthansa found that airline passengers lose about 30 percent of their sensitivity to sweet and salty foods in-flight. "In the air, food and drink tastes as it does when we have a cold," said aroma chemist Dr. Andrea Burdack-Freitag at the time.

The reasons are intuitive enough. For one thing, whether a passenger is afraid or not, an airline cabin is an extreme environment. It's loud and crowded and gross—you're just trapped there smelling and hearing like 300 people all at the same time. Details and nuances are going to get lost in those conditions. Noise alone can trigger "ancestral fear," experimental psychologist and airline industry consultant Charles Spence told the New York Times. The one taste that becomes enhanced at altitude, he says, is umami. Our fearful ancestors may have turned to umami-rich foods for energy in fight-or-flight situations. Thus, we drink V8 on airplanes.

We also lose some of our sense of smell and taste because air in aircraft cabins is superdry and superthin. There's just less actual stuff in that kind of atmosphere to smell. As a consequence, we experience bitterness more strongly, likely just because it's a dominant flavor. 

So, Betsy Beer features, "a wheat base to combat bitterness [and] increased carbonation for added mouthfeel," the Cathay Pacific pitch continues. The beer is also unfiltered "to retain vitamin B—well-known for restorative properties."

Of course, as a typical American schlub flying mostly domestic routes in coach, I'm not exactly the target market for this. At least it's some verification that that overpriced can of Heineken really does taste like approximately nothing and it's not just me.

from Airline Unveils Beer Calibrated To Taste Good at 30,000 Feet

Turing Completeness Is Where You Least Expect It

The condition of Turing completeness is almost always explained in terms of the Turing machine. Naturally. A Turing machine is simply a hypothetical black box that performs a small set of mathematical/logical operations on a strip of paper tape that is of potentially unlimited length. The box zips back and forth along the tape reading and writing data into discrete cells, eventually at some point in the future returning the answer to some question. This is what it is to be computable at all—an assurance that the machine won't run forever. Any true computer is just a simulation of this primitive abstraction.

I've always had a hard time with the idea of the Turing machine. Part of the problem is that the Turing machine as described above is often described in terms of what it is—a box that does mathematical operations on cellular divisions of a strip of paper tape—and not what it means. And what the Turing machine means is that there is a condition of being able solve any computational problem given enough time and memory. 

For something to be Turing complete, it must be able to solve every problem solvable by any other computer that exists or that could be imagined to exist (such as a Turing machine). It may be easier to see this from the other side: a Turing incomplete computer is unable to solve some known class of problems solvable by another computer. Usually, when we talk about Turing completeness, we're talking about computers-as-programming languages, which are systems that describe how to solve different problems. 

Most programming languages you've heard of are Turing complete. There are no computational problems that can be solved by C and not by JavaScript or by Java and not Python or by Python and not Java. And so on. An interesting exception is SQL—the language implementing relational databases—which in its most basic form is generally understood to be Turing incomplete. This makes sense given that SQL doesn't actually exist for general-purpose computation and doesn't need features like loops and if-then conditional branching (which allows sections of code to be skipped under certain circumstances). 

Likewise, HTML isn't Turing complete, nor should we expect it to be. Its usage is descriptive and not computational. 

An interesting thing about Turing completeness is that it's not all that hard to achieve. In fact, it exists in the world all over the place just through accident. Bitcoin and darknet researcher Gwern Branwen has published a small catalog of "surprisingly turing-complete" constructs that lead to some curious and worrisome security implications.

"One might think that such universality as a system being smart enough to be able to run any program might be difficult or hard to achieve, but it turns out to be the opposite and it is difficult to write a useful system which does not immediately tip over into TC," Branwen writes. "It turns out that given even a little control over input into something which transforms input to output, one can typically leverage that control into full-blown TC. This can be amusing, useful (although usually not), harmful, or extremely insecure [and] a cracker's delight."

Some examples include Magic the Gathering, CSS, and common musical notation. In the case of Magic, this is only true assuming an endless sequence of cards that force player moves, thus eliminating player choice. The mechanics get pretty deep. CSS—cascading style sheets, which add information about the appearance of webpages—meanwhile, becomes Turing complete when we include user clicks into the system and, thus, the ability to change the system's state. Otherwise, CSS, as a descriptive language, mostly just sits there, like HTML. With some slight tweaks, common musical notation can be converted into the esoteric, Turing-complete programming language Choon. 

A 2013 paper published by University of Cambridge computer scientist Stephen Dolan, and cited by Branwen, offers a contender for shortest computer science paper title ever: "mov Is Turing Complete." He's referring to an assembly language instruction—a hardware-level command, basically—that moves a unit of data from one memory address to another. It's one of a very long list of such assembly instructions, but what Dolan showed is that every other instruction can be reduced to this one action. It's wild.

The security implication is maybe not obvious. Some large part of computer security, generally, is evading and defending against malicious code that might gain entry into your system and do unwanted things. Doing so requires being able to identify such code as code, and not, say, Pokemon and or human heart cells. Computation could be anywhere.

from Turing Completeness Is Where You Least Expect It

Could AI Replace Student Testing?

Driving Is Social. Autonomous Cars Aren't, Argues Computer Scientist

Autonomous cars are premised on the simple notion that driving is a fundamentally algorithmic activity. It can be understood as a formal system in which all agents act according to well-defined rules. If red: stop. If green: go. If obstacle and traveling fast: swerve. Else: stop. If gap: merge. Put a whole lot of these rules together and we wind up with an autonomous car capable of some incredibly complex behaviors that will suffice in most real-world driving scenarios.

But that's not quite it. Driving is more than a system of formal rules. It's a social activity. Its mechanics involve subtle interactions between humans that reflect those of the not-driving world. We've had hundreds of thousands of years to learn how to predict each other's behaviors at levels that are not just formal, but are emotional and instinctual. This stuff is hard to articulate, let alone program.

This is the general point made by Barry Brown, a human-computer interaction researcher at Stockholm University, in the current issue of Computer: "Driving isn't just a mechanical operation but also a complex social activity. Until the day comes when all vehicles are fully autonomous, self-driving cars must be more than safe and efficient-they must also understand and interact naturally with human drivers. So long as most vehicles on the roadway continue to be operated by people, self-driving car designers must consider how their choices impact other drivers as well as their own vehicles' passengers."

Brown laments the lack of public data made available by self-driving car developers. We just don't have much of an inside perspective. So, Brown responded by aggressively building a wide-reaching outside perspective via public YouTube footage of driver-assisted and fully self-driving cars in the wild. In all, he collected 93 videos taken of mostly Tesla's driver-assistance system, Autopilot, as well as several from similar systems in Volvo and Honda vehicles. Nine were of Google self-driving cars. The formal method used by Brown and colleagues at Stockholm University is described in detail in a separate paper presented recently at the University of Nottingham's Mixed Reality Laboratory. 

"We're in the midst of a global field test of autonomous driving technology, yet results from these tests are proprietary, with little publicly available data," Brown writes. "Occasionally, flaws in the technology are exposed by videos taken by in-car dashcams and passengers' mobile phones and uploaded to social media sites like YouTube, prompting media discussion and sometimes controversy."

What Brown found mostly consists of well-behaving autonomous cars, but in several cases they got it very wrong. What all of these situations had in common was an inability on the part of the car to understand or display social cues. "While people can often discern other drivers' intentions as well as mood or character—aggressive, hesitant, selfish, unpredictable, and so on-based on changes (or the absence of changes) in their car's speed, direction, and so on, [Tesla] Autopilot lacks this ability," Brown says.

Below is one common example, in which the Tesla fails to recognize a polite gesture by another motorist and then proceeds to cut that motorist off. Another autonomous Tesla would be like "whatever," but here the action is perceived as rude.

Here's another (long) one. In this case, a human driver misinterprets a gap left open between two self-driving cars as a polite invitation to merge into that space. In reality, this is just the autonomous system maintaining a safe distance in a situation where human drivers generally don't leave spaces (that is, stopped at a red light). 

In this third example, the self-driving car attempts to "creep" into an intersection guarded by a four-way stop. In situations where two or more drivers hit four-way stops at more or less the same time, this is a precautionary move meant to signal to other cars that they need to wait. But in this case, the self-driving car doesn't creep enough and the other driver doesn't register the Google car's intention.

"As self-driving cars become better at the mechanics of driving, yet still not "behave" exactly like human drivers, they could arouse feelings of anger or frustration," Brown writes. "For example, always slowing down to avoid a collision—a statistically safe but not always the best response—could encourage drivers to cut off or zoom past an autonomous car, ironically increasing the risk of a collision."

The general conclusion here is that we can't program autonomous cars to behave as though every other car is self-driving as well. Until they are, we're stuck with humans and need to build self-driving systems that can better account for that fact.

from Driving Is Social. Autonomous Cars Aren't, Argues Computer Scientist

Build Your Own Terrifying Cat-Blob with Machine Learning

Engineers Turn Human Body Into Bioacoustic Communications Channel

Bill Gates Warns of Bioterror Doomsday

That any of us manage to sleep at night given the proximity of Donald Trump, the United States' philosopher king, to said country's so-called nuclear football and the launch codes to use it is a statement on either the psyche's ability to adapt to ever-mounting anxiety or its ability to hide under rocks. 

Maybe substituting in another fear will help out with that, in which case Bill Gates has us covered. At last week's Munich Security Conference he warned that a genetically engineered virus could kill more people than nuclear weapons while being far easier and cheaper to make. Gates' conference co-panelists, including the executive director of the WHO, generally agreed.

"The next epidemic has a good chance of originating on a computer screen of a terrorist intent on using genetic engineering to create a synthetic version of the smallpox virus or contagious and highly deadly strain of flu," Gates said. He noted that, according to epidemiological models, a respiratory-spread pathogen could kill 30 million people in less than a year.

Beyond global health, generally, this is one of Gates' pet issues. Following the ebola outbreak of 2015, he wrote in the New England Journal of Medicine that world governments must take said outbreak as a call to action. We simply got lucky that time around, he argued, and a more transmissible pathogen could have easily overwhelmed existing resources for responding to it. Gates lamented that the world does not currently fund a central organization responsible for coordinating responses to a global epidemic. The US, meanwhile, hasn't conducted a large-scale epidemic simulation since 2001's Dark Winter exercise. 

In remarks made to the Telegraph ahead of the Munich conference, Gates offered: "Natural epidemics can be extremely large. Intentionally caused epidemics, bioterrorism, would be the largest of all."

"With nuclear weapons, you'd think you would probably stop after killing 100 million," he said. "Smallpox won't stop. Because the population is naïve, and there are no real preparations. That, if it got out and spread, would be a larger number."

Gates imagines "germ games" epidemic response simulations, better outbreak monitoring, and quick-response vaccine development networks. For now, we remain wide open to attack, whether it comes via some terrorist biotech lab or just good old nature. 

from Bill Gates Warns of Bioterror Doomsday

This DARPA Video Targeting AI Hype Is Necessary Viewing

Artificial intelligence is grossly misunderstood, but you can't really blame the public. However well-intentioned, we're up against multiple coordinated efforts to distort the field, whether that's technologist doomsaying or Singularity marketing. And, as is often the case in overhyped and-or distorted science, there aren't really people on the inside doing the work of bullshit-calling. 

DARPAtv published the video below a few days ago and it's worth your 15 minutes. It's a rare clear-eyed look into the guts of AI that's also simple enough for most non-technical folks to follow. It's dry, but IRL computer science is pretty dry. The key point is that that this stuff is still really hard, and many of the things that we imagine AI to be capable of or imminently capable of are in fact looming challenges in the field—problems just now being formulated.

I don't have much else to add. AI, whether it's tasked with facial recognition or piloting DARPA monster-dogs, is really just a lot of statistics and computations that would blitz your laptop. The AI third-wave, where computers will only begin to "understand" the world in abstract terms, is a long ways off.   

from This DARPA Video Targeting AI Hype Is Necessary Viewing

Scientists Offer Four Guidelines for Maintaining Scientific Integrity in the Age of Trump

Science only works when it's apolitical. It requires a unique sort of freedom, which is the freedom to reach unpopular conclusions. From vaccines to GMOs to climate change, this is a tension that exists across the political spectrum. It's a constant battle, even in an era when so much of our lives are defined by not just science and technology, but the assurance of continued scientific and technological progress. It's weird to consider that progress as provisional.  

But that's where we are. It's not just climate change and the environment, it's the whole scientific enterprise. In its demands for evidence and insistence on questioning "alternative facts," science is a target and scientists are well aware of this. The Union of Concerned Scientists (UCS), a 200,000-member organization that generally advocates for using science to solve environmental and social problems, is, well, concerned. 

In a piece published Thursday in Science, Gretchen Goldman and colleagues at the UCS lay out four general guidelines for maintaining scientific integrity in an era where science will be attacked simply for being science—that is, how to tell the truth amidst an open revolt against truth.

"Early indications that the Administration plans to distort or disregard science and evidence, coupled with the chaos and confusion occurring within federal agencies, now imperils the effectiveness of our government," Goldman writes. "The scientific community will need to connect science-informed policy to positive outcomes and staunchly defend scientific freedom. It must also spotlight political interference in science-based policy development and be prepared to protect scientists—both within and outside the government—against executive or legislative overreach."

Read More: Scientists' New Role in Trump's America: The Resistance

The four guidelines summarized in the piece are pragmatic and, indeed, defensive. To start, the scientific community needs to demonstrate public support for the work of federal scientists. 

Given the recently resurrected Holman Rule—which gives Congress the authority to reduce the pay of individual federal workers to $1—the feds now have tremendous power when it comes to punishing and silencing individual scientists. If scientists are going to remain willing to share research that may not be politically popular, we should be able to offer at least the cover of public opinion. Votes still matter, at least for now.

Second, scientists need to be prepared to defend the scientific basis for regulations that protect the public, whether it's from sketchy drug companies, polluters, or whatever else. Regulation is like the height of evil in Trumpland, and a whole lot of regulations have a basis in science. Ultimately, this may mean scientists are the ones left to defend these regulations.

The third point is closely related to that and basically reduces to fighting back. Congress is going to pass laws excluding scientists from public policy decisions and we can expect those laws to find a receptive White House. This is where scientists can lose in a really wholesale way, as they're institutionally shut out of the process. We need to jam a foot in the door.

"Finally," Goldman and co. write, "scientists can individually engage by providing scientific advice to decision-makers and communicating the importance of their work to those outside the scientific community. They can be conduits of information when scientific integrity is compromised in government. They can fiercely protect university independence. And they can defend peers who become political targets for speaking up."

As a manifesto for scientific relevance in post-truth politics, the UCS piece ends on a hopeful-ish note: "The scientific community is well positioned for what may lie ahead. Already, scientific societies have asked the Trump Administration to appoint a science adviser and more than 5500 scientists have signed a letter asking the Administration to uphold scientific integrity. Alarms must sound when science is silenced, manipulated, or otherwise compromised."

To a certain extent, as an advocate you just have to be optimistic. A much larger share of Americans claims to be interested in science than voted for Donald Trump, at least. That hardly assures its place in public policy circa 2017, but we—scientists, the media, the scientifically-minded public—won't let it go quietly. 

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from Scientists Offer Four Guidelines for Maintaining Scientific Integrity in the Age of Trump

Report on Secret Baltimore Aerial Surveillance Concludes 'We Should Do This Again Sometime'

When it comes to classic dystopian surveillance societies, there's nowhere quite like Baltimore. There are—or were—the JLENS cruise missile-spotting blimps hanging out over the northeastern reaches of the city, there are the ubiquitous blue light crime cameras mounted above street corners in "bad" parts of town, and, for several weeks in 2016, there were high-flying aircraft circling above Baltimore gathering bulk footage of, well, absolutely everything.

This last thing, the slow circling of all-seeing Cessna aircraft, gets dystopian bonus points for having been conducted in secret. It was a pilot program initiated by the Baltimore Police Department in concert with Ohio-based contractor Persistent Surveillance Systems and kept secret not only from the Baltimore citizenry but from the mayor and city council as well. Baltimore's top cops basically told no one about the program, and then, when pressed, explained that it hadn't really been a secret at all and was merely an extension of the aforementioned CitiWatch blue light camera network. (Which is a pretty silly thing to say given that part of the whole point of the blue light cameras is visibility and, thus, deterrence, but I digress.)

The DC-based Police Foundation—"advancing policing through innovation and science"—has a new report out about the Baltimore surveillance program, which was known officially as the "Baltimore Community Support Program" (BCSP). While noting that there's really no hard data on the BCSP, the Foundation gives the program a weak thumbs up and offers a kind of wan defense of the program's secretiveness. (The secretiveness, the report argues, was merely a "bureaucratic misunderstanding" and kind of just an illusion given the relative clarity of the CityWatch surveillance program.)

Here's the gist of Foundation's findings: "From its limited review, the Police Foundation has concluded that persistent surveillance has the potential for increasing the clearance of crimes and reducing the cost of criminal investigations. Anecdotal information from BPD officers who have used BCSP data to investigate crimes reported that the BCSP was a helpful crime-fighting tool that saved them considerable investigative time. Furthermore, this is suggestive that trust and confidence in the police could also be elevated through this type of program—as along as adequate public understanding and support is present before the technology."

Ultimately, the Police Foundation concludes that we should more rigorously evaluate programs like BCSP before deploying them on a mass scale. Which points back to the apparent lack of rigor employed by Baltimore's cops in running a surveillance system that's pretty clearly not normal and is representative of a gross escalation in surveillance. It's also another way of saying let's do this again.  

While Baltimore cops and Persistent Surveillance argue that the images collected via the BCSP don't offer sufficient resolution to identify individual citizens—offering instead broad movements of groups or vehicles that might be correlated to ground-level camera footage—the ACLU isn't buying it. When the program first became public last August, ACLU policy analyst Jay Stanley noted that what might look like a pixelated dot from above can still be identified by pulling that aforementioned ground-level footage, and, besides, you can learn a whole lot about a pixelated dot by watching its comings and goings.

"'Pixelated dots' can be followed forward and backward in time as they enter and exit homes and other buildings, and even without any other technology, that simple fact can be used to identify people," Stanley wrote. "Indeed, the entire purpose of the system is to identify particular individuals."

In comments to the Baltimore Sun last week, David Rocah, senior staff attorney at the ACLU of Maryland, offered, "What's glaringly omitted is any recognition about the privacy impact and societal impact of giving the government the power of knowing where everyone goes every time they leave their house, because that's what this means." 

"If the only lens you look at is, 'Can this be useful?' then I think you are entirely missing the point," Rocah said.

What makes BCSP even more ominous is how the Baltimore cops paid for it. Part of the reason city leaders could be cut out of the loop on the program is that city money didn't pay for it. Instead, $360,000 in funding came from the Houston-based Laura and John Arnold Foundation, the endeavour of Enron trader and hedge fund manager-turned-philanthropist John Arnold. 

Arnold's less evil than his resume might indicate, but the sketchiness is not so much in the money's source than it is in the fact that collecting outside money allowed Baltimore's cops to do an end run around public scrutiny. One has to wonder how different the discussion would be about the program's future if said cops—now under a federal consent decree to "increase transparency, public oversight, [and] accountability"—had just been upfront about a scheme that is so clearly outside the norm.

from Report on Secret Baltimore Aerial Surveillance Concludes 'We Should Do This Again Sometime'

For $21.6 Billion You Just Could Pay a Line of Dudes To Stand on the Border

According to a leaked Department of Homeland Security report, completing Donald Trump's wall along the US-Mexico border will cost $21.6 billion. Give or take. That's about twice that of Trump's own estimate.

Because money is kind of hard to imagine at the scale of governmental budgets, I did some quick math that might put things into perspective. For $21.6 billion dollars, you could hire about 65,000 border patrol agents at $40,000 per year for 10 years. Add in the 20,000 agents already patrolling the border and staffing checkpoints, and we have 85,000 agents in all to span a roughly 2,000 mile border. Subtract the 700 or so miles that are currently already fenced, and the open range needing to be patrolled is down to 1,300 miles, not accounting for natural barriers.

So, if we then divide that 85,000 three ways (optimistically, sure) to account for shifts and days off and whatnot, we wind up with 28,000 agents guarding 1,300 miles. Lob off a couple hundred miles from that length for pre-existing barriers like the Rio Grande River, you wind up with three or four agents allocated for each individual mile across the entire border. Throw in some lifeguard chairs and we wind up with a border that's under constant view of a live, present human at any given time.    

I'm obviously taking some liberties here with a lot of unknowns. But given that Trump wants to spend billions in taxpayer money to solve an imaginary problem, it's kind of relative. 

from For $21.6 Billion You Just Could Pay a Line of Dudes To Stand on the Border

A Cheap New Metamaterial Offers Air Conditioning Without Air Conditioners

The past decade has seen a recasting of air conditioning as an ironic villain. This technology that we use to live in defiance of heat is itself, as a nearly unparalleled energy hog, contributing to the very heat it exists to dispel. We turn up the AC and the climate responds. According to the United States Department of Energy, air conditioning in the United States accounts for 117 million metric tons of carbon dioxide released into the atmosphere every year.

As described in the current issue of Science, researchers at the University of Colorado and the University of Wyoming have developed a new metamaterial (a material engineered to have extraordinary properties) that offers at least a partial potential solution in the form of daytime radiative cooling. That's the process by which incoming thermal energy from the Sun is exchanged for outgoing energy in the form of infrared radiation. 

While efficient nighttime radiative cooling systems are pretty reasonable, achieving the same thing during daylight hours has been hampered by a fundamental problem: Absorbing even just a few percent of the incoming radiation from the Sun easily washes away any potential cooling benefits. What's needed is a material that strongly emits infrared radiation, but just barely absorbs energy from the Sun.  

Materials scientists have accomplished this previously by using very complicated and difficult-to-produce nanomaterials. As the current paper explains, these prior attempts are all hampered by the fact that they require exotic and impractical fabrication techniques. The challenge here was to make something that could actually be scaled to real-world use. 

The resulting material is composed of a layer of visibly transparent polymers randomly embedded with tiny spheres of glass and then covered over by a thin layer of silver. Basically, incoming light of many different wavelengths gets caught up in and then reemitted by the spheres. The randomization of these spheres is part of what accounts for the wide range (96 percent) of reflectivity across the spectrum of sunlight. 

The researchers behind the current paper tested out their new material in a couple of different ways. In one, it was stretched out over part of a styrofoam cooler that was kept at a constant temperature by an attached heater. Here, cooling capacity could be measured by the amount of heating power was required to maintain this temperature across a long timespan. In a second experiment, the material was used to cool water, which functioned as a cooling storage medium—like a battery for coldness.

It's still unclear as to how the material will fare in terms of reliability and lifetime for practical outdoor applications, but a world where we don't have to cart around energy-guzzling appliances to guarantee ourselves fake microclimates doesn't seem that far away.

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from A Cheap New Metamaterial Offers Air Conditioning Without Air Conditioners

The Most Popular Weekend Programming Languages

This 'Bullet' Black Hole Is Ripping Through a Supernova Gas Cloud

We usually think of black holes as anchors—gravitational sources so extreme that they wind up functioning as focal points for vast galaxies. Isolated, liberated black holes hurtling through the gaseous wreckage of a supernova at nearly 100 times the speed of sound? Not so much. But, according to astrophysicists writing in the Astrophysical Journal Letters, this is a likely explanation behind an astrophysical anomaly known just as the "bullet."

The Keio University-based astrophysicists behind the discovery observed the bullet in 2014 and 2015 using the Atacama Submillimeter Telescope, a 10 meter radio telescope located in northern Chile. The bullet lives at the edge of a massive expanding gas cloud serving as a shell surrounding the supernova remnant (SNR) W44. It's the energy-transfer processes of this SNR that the researchers were studying when they happened upon a feature with an unusually high density and temperature beyond what could be explained by the SNR alone.

Looking closer, the Keio researchers found that this mysterious something was both hauling ass and trailing a long, thin tendril of gas picked up from W44's shell. The bullet's extreme kinetic energy is a mystery. One possible explanation is just that a second supernova explanation occurred elsewhere within the W44 gas shell, which is indeed large enough to accommodate an additional such event. 

Massive stars tend to form in groups, so it's not impossible. They calculated that the chance probability of a second supernova occurring is around 10^-3.2, or .00063. The Keio team apparently wasn't satisfied with those weak odds and sought other explanations. They came up with black hole two scenarios that could explain the phenomenon. Both involve anomalous cosmic violence. 

In the first scenario, the "explosion model," the gas cloud surrounding W44 passes a static black hole (a "normal" but isolated black hole), which has the effect of abruptly pulling a bunch of gas into a relatively tiny space (owing to the black hole's extreme gravity). The violently condensed gas then explodes as W44 moves away from the then-activated black hole, spewing out the observed tendril of gas in the process. Here, the bullet is gas alone. 

In the second scenario, the "shooting model" illustrated above, a high-velocity black hole that just happened to have been bouncing around the general vicinity of W44 just happened to have collided with the gas shell, resulting in the same trail of gas, only this time following in the wake of the interloping black hole. 

It's weird to think about, but it's thought that stray black holes may be common in the Milky Way—a group of astronomers estimated in 2002 that the Milky Way may house nearly billion strays. Still, the Keio group thinks the explosion model is more plausible because we're actually observed phenomena best explained by rapidly contracting and then exploding gas: the Tornado nebula, and a more vague, recently discovered feature located near the center of our galaxy.

from This 'Bullet' Black Hole Is Ripping Through a Supernova Gas Cloud

Hydrogel Robot Hand Catches and Releases Live Fish

In fishing terms, "catch and release" generally means first impaling with a hook and then nearly suffocating a fish. For engineers at MIT, it means delicately trapping a fish with water itself—albeit in gel form—and then releasing the "quite happy" victim, in the words of Xuanhe Zhao, the mechanical engineering professor that leads a group at MIT studying hydrogel "soft" robots. For the past five years, Zhao and colleagues have been perfecting hydrogel recipes ideal for tough and forceful but also soft and biocompatible robot manipulators. 

The results of Zhao's latest recipe, which are described in the current issue of Nature Communications, can be seen in the video below. 

A hydrogel is more than just a soft robot. The soft robots we typically see—and there are a lot of them lately—are fashioned from materials like silicon. But because a hydrogel consists mostly of regular old water, it has the advantage of biocompatibility. A hydrogel might then be safer to use in biomedical settings. 

Since hydrogel is basically water jelly, engineers have to prove that it can be useful for tasks like, well, gripping fish, which we might imagine to instead be an internal organ being gripped during surgery. A swimmy fish liver, say. Previous attempts at hydrogel actuators have been limited in this respect, according to Zhao, while the hydrogel gripper seen above is able to achieve up to 1 Newton in force with a response time of less than a second. Add to it the acoustic and optical camouflage that comes with being made out of mostly water—one of the material's big selling points—and that goldfish didn't have a chance. 

Zhao and co. took their inspiration from tiny eel larvae known as glass eels. In the natural world, glass eels start their lives with a perilous trek from their ocean hatching grounds to future river homes. These tiny defenseless blobs manage the journey thanks to both speed and complete transparency, which is thought to be an evolutionary adaptation. This is what they were hoping to mimic, only swapping out eel muscles for hydraulic actuators. 

"The hydrogel actuators and robots can maintain their robustness and functionality over multiple cycles (that is, over 1,000) of actuations, due to the anti-fatigue property of the hydrogel under moderate stresses," Zhao writes. "It is also expected that pneumatic actuation can be used to drive the hydrogel actuators and robots to operate in air."

from Hydrogel Robot Hand Catches and Releases Live Fish

The Real Threat Is Machine Incompetence, Not Intelligence

The past couple of years have been a real cringe-y time to be an AI researcher. Just imagine a whole bunch of famous technologists and top-serious science authorities all suddenly taking aim at your field of research as a clear and present threat to the very survival of the species. All you want to do is predict appropriate emoji use based on textual analyses and here's Elon Musk saying this thing he doesn't really seem to know much about is the actual apocalypse.  

It's not that computer scientists haven't argued against AI hype, but an academic you've never heard of (all of them?) pitching the headline "AI is hard" is at a disadvantage to the famous person whose job description largely centers around making big public pronouncements. This month that academic is Alan Bundy, a professor of automated reasoning at the University of Edinburgh in Scotland, who argues in the Communications of the ACM that there is a real AI threat, but it's not human-like machine intelligence gone amok. Quite the opposite: the danger is instead shitty AI. Incompetent, bumbling machines.

Bundy notes that most all of our big-deal AI successes in recent years are extremely narrow in scope. We have machines that can play Jeopardy and Go—at tremendous cost in both cases—but that's nothing like general intelligence. 

"The singularity is predicated on a linear model of intelligence, rather like IQ, on which each animal species has its place, and along which AI is gradually advancing," Bundy writes. "Intelligence is not like this. As Aaron Sloman, for instance, has successfully argued, intelligence must be modeled using a multidimensional space, with many different kinds of intelligence and with AI progressing in many different directions."

The problem is that the public doesn't really get this because no one bothers to explain it. We have general intelligence and so we see a simulacrum of intelligence on TV and assume that it too involves something like general intelligence, even though a Go-playing computer is more or less doomed to an existence as a Go-playing computer. In Bundy's words: "Many humans tend to ascribe too much intelligence to narrowly focused AI systems."

So, yes: machine incompetence. Bundy has unusual standing on this point because he was one of a number of UK computer scientists who argued in the early-1980s against Ronald Reagan and Edward Teller's Strategic Defense Initiative, initially proposed as a closed-loop AI system (read: necessarily human-free) that would theoretically detect outbound Soviet ICBMs and blast them to pieces with lasers before they could re-enter Earth's atmosphere and end the world. 

SDI seemed like a great idea to policy-makers, but what the policy-makers didn't understand was that the AI just wasn't there. False positives in prior early-warning missile detection systems were common and had been triggered by, among other things, flocks of birds and the moonrise. A false positive could mean nuclear war. 

"Fortunately, in these systems a human was in the loop to abort any unwarranted retaliation to the falsely suspected attack," Bundy writes. "A group of us from Edinburgh met U.K. Ministry of Defence scientists, engaged with SDI, who admitted they shared our analysis. The SDI was subsequently quietly dropped by morphing it into a saner program. This is an excellent example of non-computer scientists overestimating the abilities of dumb machines."

Research scientists "need to publicize these lessons to ensure they are widely understood," Bundy adds. 

He goes on to argue that AI will continue to develop in siloed form, where new and impressive machines continue to scare doomsayers for their abilities within relatively narrow task domains while remaining "incredibly dumb" when it comes to everything else. 

The risk remains the same as it was in the 1980s, where the public and policy-makers see machines being amazing within these narrow domains, while never seeing how badly they fail when tasks become general and start to approach the edges of human cognition. 

from The Real Threat Is Machine Incompetence, Not Intelligence