Showing posts with label bw. Show all posts
Showing posts with label bw. Show all posts
Wednesday, April 26, 2017
This Device Could Provide a Third of Americas Power BW
This Device Could Provide a Third of Americas Power BW
The Triton is designed to harvest wave energy without using moving parts that can break down in the brutal ocean.
Tapping into the Oceans Energy
There are 332,519,000 cubic miles of water on the planet. Thats 352,670,000,000,000,000,000 gallons just sloshing around out there.
Anyone whos ridden or been tossed by a wave has a sense of the kinetic energy contained in our perpetually moving oceans. If we could harness it, it could provide a clean, renewable source of energy. But efforts to turn our oceans into power generators?often in the form of "aqua-mills," windmill technology adapted to water?have foundered on the complexity of their many moving parts in the corrosive and remote environs of the sea.
A new approach, developed by a company called Oscilla Power, applies all that kinetic energy to a solid piece of metal instead of using it to turn the blades of an impeller. That creates an alternating magnetic polarity in the metal that can be converted into electrical current.
Oscillas technology, which is nearly solid-state, may prove far more durable than any other ocean-power project, increasing the chance to draw power from our oceans cleanly, meaningfully, and endlessly.
Available link for download
Sunday, March 5, 2017
Technology Made Travel Agents Obsolete Now Its Saving Them BW
Technology Made Travel Agents Obsolete Now Its Saving Them BW
Booking travel feels old-school, no matter how you spin it: Such sites as Expedia and Orbitz have barely updated their layouts since 1999, and dialing a travel agent hardly feels appropriate in the age of Uber. That?s about to change, thanks to a series of disruptive travel services that are blending human intelligence with mobile technology. The goal: striking a middle ground in an industry where the personal touch still means something, but the bottom-line savings of D.I.Y. tech is hard to beat.
Whom it?s for: The time-crunched and independent-minded
What it costs: $400 for an annual membership, billed only when you make your first booking
?People have been writing our epitaphs forever,? said Jack Ezon, president of leading agency Ovation Travel and co-founder of Skylark, a members-only website and app that caters to the luxury leisure market. ?Until recently,? he said, ?our agency?s high-end customers weren?t going online?the internet was really a mass market space.? That has changed in the last few years, as every five-star company from Four Seasons to Aman began moving their business to the web.
Enter Skylark, which is catering to a new psychographic that Ezon described as the ?do-it-yourself until you don?t want to? economy: sophisticated types who know what they want, know how to find it, but simply can?t add to their to-do lists. For a $400 annual fee, they can browse a vetted list of seven to eight hotel-and-airfare packages, all offered at discounted agency rates, and then book within a matter of minutes. Members get the perks of booking with an agent?24/7 customer service, automatic rebookings in the event of flight cancelations, free upgrades, and so on?minus the back and forth. (Skylark is invite-only while in beta mode, and will open to the public by yearend.)
?We merge online and offline,? said Ezon of Skylark?s model. ?Every digital transaction is paired with a live travel specialist who you can contact by phone, IM, e-mail ? however you want.? The service will help you coordinate a scavenger hunt for the kids in Rio or get VIP access tickets to the British Museum in London, for instance.
In a recent test, we found discounts that ranged from insignificant ($33 off a four-night stay at the Hotel Vernet in Paris) to impressive (29 percent off flights to London and a stay at the Corinthia). The best results come from package deals, which protect both hotel and airline from revealing specific discounts and damaging their price integrity.
Available link for download
Thursday, February 2, 2017
This Guy Trains Computers to Find Future Criminals BW
This Guy Trains Computers to Find Future Criminals BW
Richard Berk says his algorithms take the bias out of criminal justice. But could they make it worse?
by Joshua Brustein July 18, 2016
When historians look back at the turmoil over prejudice and policing in the U.S. over the past few years, they?re unlikely to dwell on the case of Eric Loomis. Police in La Crosse, Wis., arrested Loomis in February 2013 for driving a car that was used in a drive-by shooting. He had been arrested a dozen times before. Loomis took a plea, and was sentenced to six years in prison plus five years of probation.
The episode was unremarkable compared with the deaths of Philando Castile and Alton Sterling at the hands of police, which were captured on camera and distributed widely online. But Loomis?s story marks an important point in a quieter debate over the role of fairness and technology in policing. Before his sentence, the judge in the case received an automatically generated risk score that determined Loomis was likely to commit violent crimes in the future.
Risk scores, generated by algorithms, are an increasingly common factor in sentencing. Computers crunch data?arrests, type of crime committed, and demographic information?and a risk rating is generated. The idea is to create a guide that?s less likely to be subject to unconscious biases, the mood of a judge, or other human shortcomings. Similar tools are used to decide which blocks police officers should patrol, where to put inmates in prison, and who to let out on parole. Supporters of these tools claim they?ll help solve historical inequities, but their critics say they have the potential to aggravate them, by hiding old prejudices under the veneer of computerized precision. Some people see them as a sterilized version of what brought protesters into the streets at Black Lives Matter rallies.
Loomis is a surprising fulcrum in this controversy: He?s a white man. But when Loomis challenged the state?s use of a risk score in his sentence, he cited many of the fundamental criticisms of the tools: that they?re too mysterious to be used in court, that they punish people for the crimes of others, and that they hold your demographics against you. Last week the Wisconsin Supreme Court ruled against Loomis, but the decision validated some of his core claims. The case, say legal experts, could serve as a jumping-off point for legal challenges questioning the constitutionality of these kinds of techniques.
To understand the algorithms being used all over the country, it?s good to talk to Richard Berk. He?s been writing them for decades (though he didn?t write the tool that created Loomis?s risk score). Berk, a professor at the University of Pennsylvania, is a shortish, bald guy, whose solid stature and I-dare-you-to-disagree-with-me demeanor might lead people to mistake him for an ex-cop. In fact, he?s a career statistician.
His tools have been used by prisons to determine which inmates to place in restrictive settings; parole departments to choose how closely to supervise people being released from prison; and police officers to predict whether people arrested for domestic violence will re-offend. He once created an algorithm that would tell the Occupational Safety and Health Administration which workplaces were likely to commit safety violations, but says the agency never used it for anything. Starting this fall, the state of Pennsylvania plans to run a pilot program using Berk?s system in sentencing decisions.
As his work has been put into use across the country, Berk?s academic pursuits have become progressively fantastical. He?s currently working on an algorithm that he says will be able to predict at the time of someone?s birth how likely she is to commit a crime by the time she turns 18. The only limit to applications like this, in Berk?s mind, is the data he can find to feed into them.
?The policy position that is taken is that it?s much more dangerous to release Darth Vader than it is to incarcerate Luke Skywalker?
This kind of talk makes people uncomfortable, something Berk was clearly aware of on a sunny Thursday morning in May as he headed into a conference in the basement of a campus building at Penn to play the role of least popular man in the room. He was scheduled to participate in the first panel of the day, which was essentially a referendum on his work. Berk settled into his chair and prepared for a spirited debate about whether what he does all day is good for society.
The moderator, a researcher named Sandra Mayson, took the podium. ?This panel is the Minority Report panel,? she said, referring to the Tom Cruise movie where the government employs a trio of psychic mutants to identify future murderers, then arrests these ?pre-criminals? before their offenses occur. The comparison is so common it?s become a kind of joke. ?I use it too, occasionally, because there?s no way to avoid it," Berk said later.
For the next hour, the other members of the panel took turns questioning the scientific integrity, utility, and basic fairness of predictive techniques such as Berk?s. As it went on, he began to fidget in frustration. Berk leaned all the way back in his chair and crossed his hands over his stomach. He leaned all the way forward and flexed his fingers. He scribbled a few notes. He rested his chin in one hand like a bored teenager and stared off into space.
Eventually, the debate was too much for him: ?Here?s what I, maybe hyperbolically, get out of this,? Berk said. ?No data are any good, the criminal justice system sucks, and all the actors in the criminal justice system are biased by race and gender. If that?s the takeaway message, we might as well all go home. There?s nothing more to do.? The room tittered with awkward laughter.
Berk?s work on crime started in the late 1960s, when he was splitting his time between grad school and a social work job in Baltimore. The city exploded in violence following the assassination of Martin Luther King Jr. Berk?s graduate school thesis examined the looting patterns during the riots. ?You couldn?t really be alive and sentient at that moment in time and not be concerned about what was going on in crime and justice,? he said. ?Very much like today with the Ferguson stuff.?
In the mid-1990s, Berk began focusing on machine learning, where computers look for patterns in data sets too large for humans to sift through manually. To make a model, Berk inputs tens of thousands of profiles into a computer. Each one includes the data of someone who has been arrested, including how old they were when first arrested, what neighborhood they?re from, how long they?ve spent in jail, and so on. The data also contain information about who was re-arrested. The computer finds patterns, and those serve as the basis for predictions about which arrestees will re-offend.
To Berk, a big advantage of machine learning is that it eliminates the need to understand what causes someone to be violent. ?For these problems, we don?t have good theory,? he said. Feed the computer enough data and it can figure it out on its own, without deciding on a philosophy of the origins of criminal proclivity. This is a seductive idea. But it?s also one that comes under criticism each time a supposedly neutral algorithm in any field produces worryingly non-neutral results. In one widely cited study, researchers showed that Google?s automated ad-serving software was more likely to show ads for high-paying jobs to men than to women. Another found that ads for arrest records show up more often when searching the web for distinctly black names than for white ones.
Computer scientists have a maxim, ?Garbage in, garbage out.? In this case, the garbage would be decades of racial and socioeconomic disparities in the criminal justice system. Predictions about future crimes based on data about historical crime statistics have the potential to equate past patterns of policing with the predisposition of people in certain groups?mostly poor and nonwhite?to commit crimes.
Berk readily acknowledges this as a concern, then quickly dismisses it. Race isn?t an input in any of his systems, and he says his own research has shown his algorithms produce similar risk scores regardless of race. He also argues that the tools he creates aren?t used for punishment?more often they?re used, he said, to reverse long-running patterns of overly harsh sentencing, by identifying people whom judges and probation officers shouldn?t worry about.
Berk began working with Philadelphia?s Adult Probation and Parole Department in 2006. At the time, the city had a big murder problem and a small budget. There were a lot of people in the city?s probation and parole programs. City Hall wanted to know which people it truly needed to watch. Berk and a small team of researchers from the University of Pennsylvania wrote a model to identify which people were most likely to commit murder or attempted murder while on probation or parole. Berk generally works for free, and was never on Philadelphia?s payroll.
A common question, of course, is how accurate risk scores are. Berk says that in his own work, between 29 percent and 38 percent of predictions about whether someone is low-risk end up being wrong. But focusing on accuracy misses the point, he says. When it comes to crime, sometimes the best answers aren?t the most statistically precise ones. Just like weathermen err on the side of predicting rain because no one wants to get caught without an umbrella, court systems want technology that intentionally overpredicts the risk that any individual is a crime risk. The same person could end up being described as either high-risk or not depending on where the government decides to set that line. ?The policy position that is taken is that it?s much more dangerous to release Darth Vader than it is to incarcerate Luke Skywalker,? Berk said.
?Every mark of poverty serves as a risk factor?
Philadelphia?s plan was to offer cognitive behavioral therapy to the highest-risk people, and offset the costs by spending less money supervising everyone else. When Berk posed the Darth Vader question, the parole department initially determined it?d be 10 times worse, according to Geoffrey Barnes, who worked on the project. Berk figured that at that threshold the algorithm would name 8,000 to 9,000 people as potential pre-murderers. Officials realized they couldn?t afford to pay for that much therapy, and asked for a model that was less harsh. Berk?s team twisted the dials accordingly. ?We?re intentionally making the model less accurate, but trying to make sure it produces the right kind of error when it does,? Barnes said.
The program later expanded to group everyone into high-, medium-, and low-risk populations, and the city significantly reduced how closely it watched parolees Berk?s system identified as low-risk. In a 2010 study, Berk and city officials reported that people who were given more lenient treatment were less likely to be arrested for violent crimes than people with similar risk scores who stayed with traditional parole or probation. People classified as high-risk were almost four times more likely to be charged with violent crimes.
Since then, Berk has created similar programs in Maryland?s and Pennsylvania?s statewide parole systems. In Pennsylvania, an internal analysis showed that between 2011 and 2014 about 15 percent of people who came up for parole received different decisions because of their risk scores. Those who were released during that period were significantly less likely to be re-arrested than those who had been released in years past. The conclusion: Berk?s software was helping the state make smarter decisions.
Laura Treaster, a spokeswoman for the state?s Board of Probation and Parole, says Pennsylvania isn?t sure how its risk scores are impacted by race. ?This has not been analyzed yet,? she said. ?However, it needs to be noted that parole is very different than sentencing. The board is not determining guilt or innocence. We are looking at risk.?
Sentencing, though, is the next frontier for Berk?s risk scores. And using algorithms to decide how long someone goes to jail is proving more controversial than using them to decide when to let people out early.
Wisconsin courts use Compas, a popular commercial tool made by a Michigan-based company called Northpointe. By the company?s account, the people it deems high-risk are re-arrested within two years in about 70 percent of cases. Part of Loomis?s challenge was specific to Northpointe?s practice of declining to share specific information about how its tool generates scores, citing competitive reasons. Not allowing a defendant to assess the evidence against him violated due process, he argued. (Berk shares the code for his systems, and criticizes commercial products such as Northpointe?s for not doing the same.)
As the court was considering Loomis?s appeal, the journalism website ProPublica published an investigation looking at 7,000 Compas risk scores in a single county in Florida over the course of 2013 and 2014. It found that black people were almost twice as likely as white people to be labeled high-risk, then not commit a crime, while it was much more common for white people who were labeled low-risk to re-offend than black people who received a low-risk score. Northpointe challenged the findings, saying ProPublica had miscategorized many risk scores and ignored results that didn?t support its thesis. Its analysis of the same data found no racial disparities.
Even as it upheld Loomis?s sentence, the Wisconsin Supreme Court cited the research on race to raise concerns about the use of tools like Compas. Going forward, it requires risk scores to be accompanied by disclaimers about their nontransparent nature and various caveats about their conclusions. It also says they can?t be used as the determining factor in a sentencing decision. The decision was the first time that such a high court had signaled ambivalence about the use of risk scores in sentencing.
Sonja Starr, a professor at the University of Michigan?s law school and a prominent critic of risk assessment, thinks that Loomis?s case foreshadows stronger legal arguments to come. Loomis made a demographic argument, saying that Compas rated him as riskier because of his gender, reflecting the historical patterns of men being arrested at higher rates than women. But he didn?t frame it as an argument that Compas violated the Equal Protection Clause of the 14th Amendment, which allowed the court to sidestep the core issue.
Loomis also didn?t argue that the risk scores serve to discriminate against poor people. ?That?s the part that seems to concern judges, that every mark of poverty serves as a risk factor,? Starr said. ?We should very easily see more successful challenges in other cases.?
Officials in Pennsylvania, which has been slowly preparing to use risk assessment in sentencing for the past six years, are sensitive to these potential pitfalls. The state?s experience shows how tricky it is to create an algorithm through the public policy process. To come up with a politically palatable risk tool, Pennsylvania established a sentencing commission. It quickly rejected commercial products like Compas, saying they were too expensive and too mysterious, so the commission began creating its own system.
?If you want me to do a totally race-neutral forecast, you?ve got to tell me what variables you?re going to allow me to use, and nobody can, because everything is confounded with race and gender?
Race was discarded immediately as an input. But every other factor became a matter of debate. When the state initially wanted to include location, which it determined to be statistically useful in predicting who would re-offend, the Pennsylvania Association of Criminal Defense Lawyers argued that it was a proxy for race, given patterns of housing segregation. The commission eventually dropped the use of location. Also in question: the system?s use of arrests, instead of convictions, since it seems to punish people who live in communities that are policed more aggressively.
Berk argues that eliminating sensitive factors weakens the predictive power of the algorithms. ?If you want me to do a totally race-neutral forecast, you?ve got to tell me what variables you?re going to allow me to use, and nobody can, because everything is confounded with race and gender,? he said.
Starr says this argument confuses the differing standards in academic research and the legal system. In social science, it can be useful to calculate the relative likelihood that members of certain groups will do certain things. But that doesn?t mean a specific person?s future should be calculated based on an analysis of populationwide crime stats, especially when the data set being used reflects decades of racial and socioeconomic disparities. It amounts to a computerized version of racial profiling, Starr argued. ?If the variables aren?t appropriate, you shouldn?t be relying on them," she said.
Late this spring, Berk traveled to Norway to meet with a group of researchers from the University of Oslo. The Norwegian government gathers an immense amount of information about the country?s citizens and connects each of them to a single identification file, presenting a tantalizing set of potential inputs.
Torbj›rn Skardhamar, a professor at the university, was interested in exploring how he could use machine learning to make long-term predictions. He helped set up Berk?s visit. Norway has lagged behind the U.S. in using predictive analytics in criminal justice, and the men threw around a few ideas.
Berk wants to predict at the moment of birth whether people will commit a crime by their 18th birthday, based on factors such as environment and the history of a new child?s parents. This would be almost impossible in the U.S., given that much of a person?s biographical information is spread out across many agencies and subject to many restrictions. He?s not sure if it?s possible in Norway, either, and he acknowledges he also hasn?t completely thought through how best to use such information.
Caveats aside, this has the potential to be a capstone project of Berk?s career. It also takes all of the ethical and political questions and extends them to their logical conclusion. Even in the movie Minority Report, the government peered only hours into the future?not years. Skardhamar, who is new to these techniques, said he?s not afraid of making mistakes: They?re talking about them now, he said, so they can avoid future errors. ?These are tricky questions,? he said, mulling all the ways the project could go wrong. ?Making them explicit?that?s a good thing.?
Available link for download
Thursday, October 13, 2016
This Handheld Laser Could Replace the Vegetable Squeeze Test BW
This Handheld Laser Could Replace the Vegetable Squeeze Test BW
Hello World visits Israel to try out SCiO, a pocket molecular sensor that can reveal the calorie count of a steak?or spot counterfeit drugs.
We?ve all squeezed a fruit to see if it?s ripe. What if you could gauge its exact calorie count and sugar and fat content while you were at it?
A startup in Israel called Consumer Physics has developed a keychain-size device called the SCiO that can shoot a beam of light at, say, an avocado and instantly tell you just that.
Above, Hello World host Ashlee Vance takes the SCiO for a spin in a Tel Aviv market, joined by Consumer Physics?s chief executive officer, Dror Sharon. They zap cheese, meat, and even a dog to figure out what makes them tick. Beyond analyzing food, the SCiO can spot watered-down fuel at the pump and even tell counterfeit drugs from real ones.
The device plays to Israel?s strength in computer chips. Consumer Physics has taken a big, boxy piece of scientific equipment known as a spectrometer and shrunk it down to a chip the size of a coin. By coupling the chip with a near-infrared light, the SCiO can stir up the molecules in an object and tell a story about its makeup. It has already caught the public?s attention through one of the biggest Kickstarter campaigns in Israel?s history, and Sharon hopes the technology will soon be small enough to fit in everyone?s cell phone.
Available link for download
Friday, October 7, 2016
THE SUPERGROUP REMAKING STAR WARS AND JURASSIC WORLD IN VR BW
THE SUPERGROUP REMAKING STAR WARS AND JURASSIC WORLD IN VR BW
Standing on the desert surface of Tatooine, you instinctively duck as the Millennium Falcon swoops in for a thunderous and dramatic landing beside you. Through the lenses of your virtual-reality headset, it looks real. That?s because it is, in the sense that it?s the same 3D computer model that appeared in Star Wars: The Force Awakens. After you help Han and Chewie complete an urgent repair, R2D2 presents you with a lightsaber. A squad of Stormtroopers appears on the distant ridge. Not to worry, the Force is strong with you.
This is Trials on Tatooine, a new virtual-reality demonstration from Industrial Light & Magic?s Experience Lab (ILMxLAB), the supergroup of artists, engineers, sound designers, and storytellers prototyping the future of interactive, immersive cinema. The ILMxLAB is Lucasfilm?s research and development arm, leveraging graphics technology invented for traditional filmmaking and applying it to virtual reality, augmented reality, and theme park attractions. (The latter is no surprise given that Walt Disney acquired Lucasfilm in 2012.)
?The vision is to design, produce, and release story-based immersive entertainment experiences,? says Vicki Dobbs Beck, ILM?s executive in charge of strategic planning.
Dobbs Beck (center) with (from left) Rob Bredow, John Jack, Diana Williams, Tim Alexander, Mark Miller, Hilmar Koch, Daniel Aasheim, John Gaeta, and Wayne Billheimer of Industrial Light & Magic?s xLAB.
ILM announced its skunkworks last year and made a scene at January?s Sundance Film Festival with Holo-Cinema, an augmented-reality experience where participants wore motion-tracked 3D glasses in a room of projection screens to step into Jurassic Park or meet the beloved droids of Star Wars. While the demos are new, the underlying technology had gestated at Lucasfilm for more than a decade.
As far back as Steven Spielberg?s A.I. in 2001, ILM has worked to provide directors on-set with a real-time preview of scenes that meld live action and computer imagery without having to wait for the digital images to be added later. It built the tools atop a heavily modified game engine, commercial software that brings computer game graphics to life rapidly enough that the player can interact with them.
?When there was a resurgence in interest in virtual reality, we already had this long history with real-time graphics tools, high-end filmmaking, and storytelling,? says Rob Bredow, Lucasfilm?s chief technology officer who helped launch the xLAB. ?All of it was actually coming together in this new medium.?
?We want to make it plausible for storytellers to imagine allowing the audience inside these worlds as if they exist for real?
The core xLAB team consists of a dozen creatives and engineers but draws talent from across the Lucasfilm universe depending on each project?s needs. It?s a culture of constant collaboration. The master storytellers from the Lucasfilm Story Group run into the engineers from the Advanced Development Group at the Javva the Hutt cafe on the main Lucasfilm campus. The audio wizards of Skywalker Sound are just across the bridge.
?The way we do technology development here is really hand-in-hand with the creative goals,? says Bredow, who was formerly Sony Pictures Imageworks? chief technologist. ?The R&D is always in service to the story.?
For example, to port the Millennium Falcon from the Star Wars film universe into the interactive realm, the Advanced Development Group engineers first had to figure out how the VR hardware could render the massive 3D model in just milliseconds, compared with hours or days for a film shot. Then Skywalker Sound built a surround system that realistically rumbles and whooshes as a Corellian starship should. Meanwhile, game designers and the storytellers hashed out the most compelling way for a Jedi-in-training (you) to battle an army of Stormtroopers with a lightsaber.
?We are a technology company, but well sit an engineer next to a game designer next to a writer and in that loop is where we get the kind of invention that can only happen here,? Bredow says.
The ILMxLAB leverages the tools of traditional filmmaking and the principles of game design, but are wedded to neither, or perhaps both. John Gaeta, the group?s executive creative director who is best known for his dazzling special effects work on the Matrix trilogy, frames their efforts as first-person immersive storytelling, in which the story itself, not the gameplay, sucks you in. Sure, there may be some game-like challenges to face as you move through the experience, but they?re driven by the story, not the other way around as in most video games.
?If the director has created a good story, you?ll know if that person in your space is a friend or there to kill you?
?We want to make it plausible for storytellers to imagine allowing the audience inside these worlds as if they exist for real, not limited to fantasy,? Gaeta says.
?If the director has created a good story, you?ll know if that person in your space is a friend or there to kill you?
To do that requires rethinking a century of film techniques. Consider the frequent camera pans in most movie scenes, which are a standard way of surveying a scene or following a character.
?If the scene shifts like that in virtual reality, you?ll wonder why you?re being moved against your will,? Bredow says.
Even something as simple as a close-up shot that in a regular movie focuses the audience?s attention on the actor?s emotional state needs to be reconsidered in VR. An unexpected close-up when you?re wearing VR goggles gives the distinct feeling that someone is invading your personal space. That?s why every possible moment needs to be in service of the story, says Lucasfilm Story Group?s Diana Williams.
?Yes, a close-up in VR can be jarring, but if the director has created a good story, you?ll know if that person in your space is a friend or there to kill you,? says Williams, who launched her career producing immersive (and, according to her, classified) experiences for the Department of Defense.
Discovering the grammar of what works in immersive entertainment?whether it?s delivered via a Valve VR headset, a smartphone, or some future Disneyland ride?will certainly take time and technology. But it?s a sure bet that no auteur or engineer will figure it out alone. And that?s what sets the xLAB apart, says Dobbs Beck.
?We?ve assembled an extraordinary group of dreamers and rocket builders,? Dobbs Beck says. ?The dreamers are constantly thinking about what?s possible and the rocket builders figure out how to get us there.?
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