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Monthly Archives: June 2017
Meet Penny, an AI That Predicts a Neighborhood’s Wealth From … – WIRED
Posted: June 29, 2017 at 11:16 am
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AI enters the hospital room – CNET
Posted: at 11:16 am
Imagine you're stuck in a hospital bed after having surgery. You can't even close the window blinds without a nurse's help. And you can forget about requesting a blanket to take off the chill or getting details on visiting hours when everyone's busy handling more-pressing matters.
You feel powerless.
But what if you got what you needed just by saying it? You could instantly open the blinds, find out more about your doctor's expertise or turn up the room temperature. Sounds great, right? All you'd need is one of today's digital voice assistants that constantly listen for a request, send your query to the internet and either answer your question or complete a task.
Unfortunately, you can't do that right now with the current crop of smart assistants like Apple's Siri, Amazon's Alexa and Google's Assistant because they can't satisfy hospitals' privacy and security requirements. Yet according to Bret Greenstein, vice president of IBM's Watson Internet of Things platform, some medical staff can spend nearly 10 percent of their time with patients answering questions about lunch, physician credentials and visiting hours. If a smart speaker can answer those questions, doctors and nurses could spend more time on patient care.
Harman's JBL clock radio packs smarts from IBM's AI technology to help patients get information and control their hospital room's lighting and temperature.
It's why Thomas Jefferson University Hospitals in Philadelphia decided to work with audio giant Harman and IBM's Watson artificial intelligence technology. Together, they developed smart speakers that will respond to about a dozen commands. When a patient says "Watson," the speakers can, for instance, play calming sounds and adjust the room's lighting, thermostat and blinds.
"This is a way for patients to get some simple comfort measures addressed just by speaking," says Dr. Andrew Miller, associate chief medical officer at the Philadelphia hospital group. "How great is that?"
For the hospital, it's just the beginning.
Like Amazon's popular Echo speaker, Harman's JBL clock radio packs smarts that respond to command words it hears spoken.
Jefferson Hospital experimented with Amazon's popular Echo speaker, but found the hospital couldn't simultaneously control multiple speakers from one management system. What's more, the Echo couldn't access the hospital's secure Wi-Fi network, and it didn't have the right "skills," or capabilities, for a medical environment.
Dr. Andrew Miller
"It would have done simple things people are used to doing in the home, but not the things we wanted to do," says Neil Gomes, the hospital's chief digital officer.
So late last year, Jefferson Hospital started testing five prototype speakers that Harman made using the external casing of a regular JBL cylindrical speaker and components specially designed for artificial intelligence.
The initial trial tested two models. One required patients to press a button to wake up the device, getting around privacy concerns of an ever-listening microphone. The other woke when someone said "Watson," the name of IBM's AI technology that won the $1 million first-place prize on "Jeopardy" in 2011.
"The button gives a sense of privacy, but it proved to be very frustrating to users because they had to keep pushing it," says Greenstein.
Harman's JBL smart speakers have gone through a range of shapes and sizes.
The newest speakers, now built into Harman's round JBL clock radios, rely solely on voice commands. The hospital is testing about 40 of the new speakers, with IBM and Harman tweaking the smarts as they go. The speakers also tie into the hospital's automated facilities management system, which lets administrators control things like heating, air conditioning and lighting online. That's a convenience for everyone.
"When my father-in-law was in the hospital, we had to talk to the nurse about adjusting the thermostat," says Kevin Hague, vice president of technology strategy at Harman. "It was absurd that we had to have an RN come in and figure out on the computer how to adjust the temperature."
As of this writing, the hospital hadn't decided if it would stick with "Watson" or go with some other wake-up word, like "Jefferson."
It's fair to say we'd rather voice assistants do our bidding in a hotel room instead of in a hospital.
Some hotels are exploring that option and finding that off-the-shelf digital assistants work just fine.
Marriott, for instance, has been testing Apple's Siri and Amazon's Alexa at an Aloft Hotel in Boston. The hotel installed iPad tablets and Echo speakers in 10 rooms, letting guests speak commands to control the TV and adjust the lighting. That sounds awfully tempting considering how tough it can be sometimes to figure out which switch does what.
See more from CNET Magazine.
"The room would become an extension to your personal tech," says Toni Stoeckl, Marriott global brand leader and vice president. "I don't think we're there quite yet."
In the meantime, Jefferson Hospital, Harman and IBM are working on ways to teach their smart speaker to branch out beyond simple tasks. The possibilities are intriguing. Maybe Watson could follow you home to make sure you're taking your medication correctly. Or it could prompt you to take a walk so you could heal faster, easily change pharmacies or arrange follow-up appointments.
Right now, the speakers don't need regulatory approval, although that could change if they provide information about your diagnosis or explain your medications.
No matter how the hospital ends up using them, one thing is certain. It sucks being in a hospital. Having a little control over your environment could make it suck a little less.
This story appears in the summer 2017 edition of CNET Magazine. Click here formore magazine stories.
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This is why AI shouldn’t design inspirational posters – CNET – CNET
Posted: at 11:16 am
Inspirational posters have their place. But if you're not the kind of person to take workplace spark from a beautiful photograph of a random person canoeing at twilight or an eagle soaring, you might want to turn the poster-making over to an artificial intelligence.
An AI dubbed InspiroBot, brought to our attention by IFL Science, puts together some of the most bizarre (and thus delightful) inspirational posters around.
This one's probably not a good idea for either a stranger or a friend.
The dog's cute, but this isn't great advice either.
Hard to argue with this one, which is kinda Yoda-esque.
Hey! Who you callin' "desperate"?
This bot obviously doesn't know many LARPers, or hang around at Renaissance Faires.
The bot's posters fall in between Commander Data trying to offer advice and a mistranslated book of quaint sayings. And they're mostly fun. Except sometimes, when the AI gets really dark and it's time to leave the site entirely and Google kittens fighting themselves in the mirror.
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A Brutal Intelligence: AI, Chess, and the Human Mind – lareviewofbooks
Posted: at 11:16 am
JUNE 29, 2017
CHESS IS THE GAME not just of kings but of geniuses. For hundreds of years, it has served as standard and symbol for the pinnacles of human intelligence. Staring at the pieces, lost to the world, the chess master seems a figure of pure thought: brain without body. Its hardly a surprise, then, that when computer scientists began to contemplate the creation of an artificial intelligence in the middle years of the last century, they adopted the chessboard as their proving ground. To build a machine able to beat a skilled human player would be to fabricate a mind. It was a compelling theory, and to this day it shapes public perceptions of artificial intelligence. But, as the former world chess champion Garry Kasparov argues in his illuminating new memoir Deep Thinking, the theory was flawed from the start. It reflected a series of misperceptions about chess, about computers, and about the mind.
At the dawn of the computer age, in 1950, the influential Bell Labs engineer Claude Shannon published a paper in Philosophical Magazine called Programming a Computer for Playing Chess. The creation of a tolerably good computerized chess player, he argued, was not only possible but would also have metaphysical consequences. It would force the human race either to admit the possibility of a mechanized thinking or to further restrict [its] concept of thinking. He went on to offer an insight that would prove essential both to the development of chess software and to the pursuit of artificial intelligence in general. A chess program, he wrote, would need to incorporate a search function able to identify possible moves and rank them according to how they influenced the course of the game. He laid out two very different approaches to programming the function. Type A would rely on brute force, calculating the relative value of all possible moves as far ahead in the game as the speed of the computer allowed. Type B would use intelligence rather than raw power, imbuing the computer with an understanding of the game that would allow it to focus on a small number of attractive moves while ignoring the rest. In essence, a Type B computer would demonstrate the intuition of an experienced human player.
When Shannon wrote his paper, he and everyone else assumed that the Type A method was a dead end. It seemed obvious that, under the time restrictions of a competitive chess game, a computer would never be fast enough to extend its analysis more than a few turns ahead. As Kasparov points out, there are over 300 billion possible ways to play just the first four moves in a game of chess, and even if 95 percent of these variations are terrible, a Type A program would still have to check them all. In 1950, and for many years afterward, no one could imagine a computer able to execute a successful brute-force strategy against a good player. Unfortunately, Shannon concluded, a machine operating according to the Type A strategy would be both slow and a weak player.
Type B, the intelligence strategy, seemed far more feasible, not least because it fit the scientific zeitgeist. Fascination with digital computers intensified during the 1950s, and the so-called thinking machines began to influence theories about the human mind. Many scientists and philosophers came to assume that the brain must work something like a digital computer, using its billions of networked neurons to calculate thoughts and perceptions. Through a curious kind of circular logic, this analogy in turn guided the early pursuit of artificial intelligence: if you could figure out the codes that the brain uses in carrying out cognitive tasks, youd be able to program similar codes into a computer. Not only would the machine play chess like a master, but it would also be able to do pretty much anything else that a human brain can do. In a 1958 paper, the prominent AI researchers Herbert Simon and Allen Newell declared that computers are machines that think and, in the near future, the range of problems they can handle will be coextensive with the range to which the human mind has been applied. With the right programming, a computer would turn sapient.
It took only a few decades after Shannon wrote his paper for engineers to build a computer that could play chess brilliantly. Its most famous victim: Garry Kasparov.
One of the greatest and most intimidating players in the history of the game, Kasparov was defeated in a six-game bout by the IBM supercomputer Deep Blue in 1997. Even though it was the first time a machine had beaten a world champion in a formal match, to computer scientists and chess masters alike the outcome wasnt much of a surprise. Chess-playing computers had been making strong and steady gains for years, advancing inexorably up the ranks of the best human players. Kasparov just happened to be in the right place at the wrong time.
But the story of the computers victory comes with a twist. Shannon and his contemporaries, it turns out, had been wrong. It was the Type B approach the intelligence strategy that ended up being the dead end. Despite their early optimism, AI researchers utterly failed in getting computers to think as people do. Deep Blue beat Kasparov not by matching his insight and intuition but by overwhelming him with blind calculation. Thanks to years of exponential gains in processing speed, combined with steady improvements in the efficiency of search algorithms, the computer was able to comb through enough possible moves in a short enough time to outduel the champion. Brute force triumphed. It turned out that making a great chess-playing computer was not the same as making a thinking machine on par with the human mind, Kasparov reflects. Deep Blue was intelligent the way your programmable alarm clock is intelligent.
The history of computer chess is the history of artificial intelligence. After their disappointments in trying to reverse-engineer the brain, computer scientists narrowed their sights. Abandoning their pursuit of human-like intelligence, they began to concentrate on accomplishing sophisticated, but limited, analytical tasks by capitalizing on the inhuman speed of the modern computers calculations. This less ambitious but more pragmatic approach has paid off in areas ranging from medical diagnosis to self-driving cars. Computers are replicating the results of human thought without replicating thought itself. If in the 1950s and 1960s the emphasis in the phrase artificial intelligence fell heavily on the word intelligence, today it falls with even greater weight on the word artificial.
Particularly fruitful has been the deployment of search algorithms similar to those that powered Deep Blue. If a machine can search billions of options in a matter of milliseconds, ranking each according to how well it fulfills some specified goal, then it can outperform experts in a lot of problem-solving tasks without having to match their experience or insight. More recently, AI programmers have added another brute-force technique to their repertoire: machine learning. In simple terms, machine learning is a statistical method for discovering correlations in past events that can then be used to make predictions about future events. Rather than giving a computer a set of instructions to follow, a programmer feeds the computer many examples of a phenomenon and from those examples the machine deciphers relationships among variables. Whereas most software programs apply rules to data, machine-learning algorithms do the reverse: they distill rules from data, and then apply those rules to make judgments about new situations.
In modern translation software, for example, a computer scans many millions of translated texts to learn associations between phrases in different languages. Using these correspondences, it can then piece together translations of new strings of text. The computer doesnt require any understanding of grammar or meaning; it just regurgitates words in whatever combination it calculates has the highest odds of being accurate. The result lacks the style and nuance of a skilled translators work but has considerable utility nonetheless. Although machine-learning algorithms have been around a long time, they require a vast number of examples to work reliably, which only became possible with the explosion of online data. Kasparov quotes an engineer from Googles popular translation program: When you go from 10,000 training examples to 10 billion training examples, it all starts to work. Data trumps everything.
The pragmatic turn in AI research is producing many such breakthroughs, but this shift also highlights the limitations of artificial intelligence. Through brute-force data processing, computers can churn out answers to well-defined questions and forecast how complex events may play out, but they lack the understanding, imagination, and common sense to do what human minds do naturally: turn information into knowledge, think conceptually and metaphorically, and negotiate the worlds flux and uncertainty without a script. Machines remain machines.
That fact hasnt blunted the publics enthusiasm for AI fantasies. Along with TV shows and movies featuring scheming computers and bloody-minded robots, weve seen a slew of earnest nonfiction books with titles like Superintelligence, Smarter Than Us, and Our Final Invention, all suggesting that machines will soon be brainier than we are. The predictions echo those made in the 1950s and 1960s, and, as before, theyre founded on speculation, not fact. Despite monumental advances in hardware and software, computers give no sign of being any nearer to self-awareness, volition, or emotion. Their strength what Kasparov describes as an amnesiacs objectivity is also their weakness.
In addition to questioning the common wisdom about artificial intelligence, Kasparov challenges our preconceptions about chess. The game, particularly when played at its highest levels, is far more than a cerebral exercise in logic and calculation, and the expert player is anything but a stereotypical egghead. The connection between chess skill and the kind of intelligence measured by IQ scores, Kasparov observes, is weak at best. There is no more truth to the thought that all chess players are geniuses than in saying that all geniuses play chess, he writes. [O]ne of the things that makes chess so interesting is that its still unclear exactly what separates good chess players from great ones.
Chess is a grueling sport. It demands stamina, resilience, and an aptitude for psychological warfare. It also requires acute sensory perception. Move generation seems to involve more visuospatial brain activity than the sort of calculation that goes into solving math problems, writes Kasparov, referring to recent neurological experiments. To the chess master, the boards 64 squares definenot just an abstract geometry but an actual terrain. Like figures on a landscape, the pieces form patterns that the master, drawing on years of experience, reads intuitively, often at a glance. Methodical analysis is important, too, but it is carried out as part of a multifaceted and still mysterious thought process involving the body and its senses as well as the brains neurons and synapses.
The contingency of human intelligence, the way it shifts with health, mood, and circumstance, is at the center of Kasparovs account of his historic duel with Deep Blue. Having beaten the machine in a celebrated match a year earlier, the champion enters the 1997 competition confident that he will again come out the victor. His confidence swells when he wins the first game decisively. But in the fateful second game, Deep Blue makes a series of strong moves, putting Kasparov on the defensive. Rattled, he makes a calamitous mental error. He resigns the game in frustration after the computer launches an aggressive and seemingly lethalattack on his queen. Only later does he realize that his position had not been hopeless; he could have forced the machine into a draw. The loss leaves Kasparov confused and in agony, unable to regain his emotional bearings. Though the next three games end in draws, Deep Blue crushes him in the sixth and final game to win the match.
One of Kasparovs strengths as a champion had always been his ability to read the minds of his adversaries and hence anticipate their strategies. But with Deep Blue, there was no mind to read. The machines lack of personality, its implacable blankness, turned out to be one of its greatest advantages. It disoriented Kasparov, breeding doubts in his mind and eating away at his self-confidence. I didnt know my opponent at all, he recalls. This intense confusion left my mind to wander to darker places. The irony is that the machines victory was as much a matter of psychology as of skill.[1]
If Kasparov hadnt become flustered, he might have won the 1997 match. But that would have just postponed the inevitable. By the turn of the century, the era of computer dominance in chess was well established. Today, not even the grandest of grandmasters would bother challenging a computer to a match. They know they wouldnt stand a chance.
But if computers have become unbeatable at the board, they remain incapable of exhibiting what Kasparov calls the ineffable nature of human chess. To Kasparov, this is cause for optimism about the future of humanity. Unlike the eight-by-eight chessboard, the world is an unbounded place, and making sense of it will always require more than mathematical or statistical calculations. The inherent rigidity of computer intelligence leavesplenty of room for humans to exercise their flexible and intuitive intelligence. If we remain vigilant in turning the power of our computers to our own purposes, concludes Kasparov, our machines will not replace us but instead propel us to ever-greater achievements.
One hopes hes right. Still, as computers become more powerful and more adept at fulfilling our needs, there is a danger. The benefits of computer processing are easy to measure in speed, in output, in dollars while the benefits of human thought are often impossible to express in hard numbers. Given contemporary societys worship of the measurable and suspicion of the ineffable, our own intelligence would seem to be at a disadvantage as we rush to computerize more and more aspects of our jobs and lives. The question isnt whether the subtleties of human thought will continue to lie beyond the reach of computers. They almost certainly will. The question is whether well continue to appreciate the value of those subtleties as we become more dependent on the mindless but brutally efficient calculations of our machines. In the face of the implacable, the contingent can seem inferior, its strengths appearing as weaknesses.
Near the end of his book, Kasparov notes, with some regret, that humans today are starting to play chess more like computers. Once again, the ancient game may be offering us an omen.
Nicholas Carr is the author of several books about computers and culture, including The Shallows, The Glass Cage, and, most recently, Utopia Is Creepy.
[1] A bit of all-too-human deviousness was also involved in Deep Blues win. IBMs coders, it was later revealed, programmed the computer to display erratic behavior delaying certain moves, for instance, and rushing others in an attempt to unsettle Kasparov. Computers may be innocents, but that doesnt mean their programmers are.
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How artificial intelligence is taking on ransomware – ABC News
Posted: at 11:16 am
Twice in the space of six weeks, the world has suffered major attacks of ransomware malicious software that locks up photos and other files stored on your computer, then demands money to release them.
It's clear that the world needs better defenses, and fortunately those are starting to emerge, if slowly and in patchwork fashion. When they arrive, we may have artificial intelligence to thank.
Ransomware isn't necessary trickier or more dangerous than other malware that sneaks onto your computer, but it can be much more aggravating, and at times devastating. Most such infections don't get in your face about taking your digital stuff away from you the way ransomware does, nor do they shake you down for hundreds of dollars or more.
Despite those risks, many people just aren't good at keeping up with security software updates. Both recent ransomware attacks walloped those who failed to install a Windows update released a few months earlier.
Watchdog security software has its problems, too. With this week's ransomware attack , only two of about 60 security services tested caught it at first, according to security researchers.
"A lot of normal applications, especially on Windows, behave like malware, and it's hard to tell them apart," said Ryan Kalember, an expert at the California security vendor Proofpoint.
HOW TO FIND MALWARE
In the early days, identifying malicious programs such as viruses involved matching their code against a database of known malware. But this technique was only as good as the database; new malware variants could easily slip through.
So security companies started characterizing malware by its behavior. In the case of ransomware, software could look for repeated attempts to lock files by encrypting them. But that can flag ordinary computer behavior such as file compression.
Newer techniques involve looking for combinations of behaviors. For instance, a program that starts encrypting files without showing a progress bar on the screen could be flagged for surreptitious activity, said Fabian Wosar, chief technology officer at the New Zealand security company Emsisoft. But that also risks identifying harmful software too late, after some files have already been locked up.
An even better approach identifies malware using observable characteristics usually associated with malicious intent for instance, by quarantining a program disguised with a PDF icon to hide its true nature.
This sort of malware profiling wouldn't rely on exact code matches, so it couldn't be easily evaded. And such checks could be made well before potentially dangerous programs start running.
MACHINE VS. MACHINE
Still, two or three characteristics might not properly distinguish malware from legitimate software. But how about dozens? Or hundreds? Or even thousands?
For that, security researchers turn to machine learning, a form of artificial intelligence. The security system analyzes samples of good and bad software and figures out what combination of factors is likely to be present in malware.
As it encounters new software, the system calculates the probability that it's malware, and rejects those that score above a certain threshold. When something gets through, it's a matter of tweaking the calculations or adjusting the threshold. Now and then, researchers see a new behavior to teach the machine.
AN ARMS RACE
On the flip side, malware writers can obtain these security tools and tweak their code to see if they can evade detection. Some websites already offer to test software against leading security systems. Eventually, malware authors may start creating their own machine-learning models to defeat security-focused artificial intelligence.
Dmitri Alperovitch, co-founder and chief technology officer at the California vendor CrowdStrike, said that even if a particular system offers 99 percent protection, "it's just a math problem of how many times you have to deviate your attack to get that 1 percent."
Still, security companies employing machine learning have claimed success in blocking most malware, not just ransomware. SentinelOne even offers a $1 million guarantee against ransomware; it hasn't had to pay it yet.
A FUNDAMENTAL CHALLENGE
So why was ransomware still able to spread in recent weeks?
Garden-variety anti-virus software even some of the free versions can help block new forms of malware, as many are also incorporating behavioral-detection and machine-learning techniques. But such software still relies on malware databases that users aren't typically good at keeping up to date.
Next-generation services such as CrowdStrike, SentinelOne and Cylance tend to ditch databases completely in favor of machine learning.
But these services focus on corporate customers, charging $40 to $50 a year per computer. Smaller businesses often don't have the budget or the focus on security for that kind of protection.
And forget consumers; these security companies aren't selling to them yet. Though Cylance plans to release a consumer version in July, it says it'll be a tough sell at least until someone gets attacked personally or knows a friend or family member who has.
As Cylance CEO Stuart McClure puts it: "When you haven't been hit with a tornado, why would you get tornado insurance?"
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IBM Watson Heads to Washington to Argue That Artificial Intelligence Isn’t Really That Bad – Inc.com
Posted: at 11:16 am
IBM is convinced that its Watson supercomputer is capable of doing a whole lot more than winning at Jeopardy--and the company wants to make sure it stays that way.
To that end, IBM is making a push this week to urge lawmakers not to fall victim to artificial intelligence fear mongering. David Kenny, IBM Watson's senior vice president, sent a letter to Congress on Tuesday stressing the importance of pushing A.I. forward instead of restricting it. According to Recode, he's meeting with a group of Representatives today to discuss the technology.
"When you actually do the science of machine intelligence," Kenny wrote in the letter, which IBM published Tuesday, "and when you actually apply it in the real world of business and society ... you understand that this technology does not support the fear-mongering commonly associated with the AI debate today."
Kenny argued that fears of "massive job loss, or even an eventual AI 'overlord' " are overblown. "I must disagree with these dystopian views," he wrote. "The real disaster would be abandoning or inhibiting cognitive technology before its full potential can be realized."
IBM has an interest in ensuring that the government chooses not to restrict the use of artificial intelligence. While the Watson system is perhaps most famous for beating Jeopardy champ Ken Jennings in 2011, it's since been applied to a variety of tasks. Watson is used to recommend treatments for patients in medical facilities including the Cleveland Clinic and New York's Sloan-Kettering Cancer Center. H&R Block has begun using Watson to prepare client's tax returns. In April, the software was applied to the Masters golf tournament, letting online viewers quickly see the most exciting highlights, which it selected automatically based on factors like crowd noise and player reactions.
Even so, in recent months, some in the A.I. world have expressed surprise that Watson isn't further along in its capabilities, given what it did six years ago. The company's ambitions for more widespread applications of its tech mean the company has a lot at stake.
As A.I.'s abilities expand to tasks like driving, reading X-rays, diagnosing illnesses, and performing paralegal work--all of which it's already capable of doing on some level--millions of jobs could be lost. Recent expert predictions on the number of jobs lost have ranged from from 6 percent by 2021 to 50 percent by 2035.
Yet IBM is the latest A.I. company to assure the public that its fears of the technology are overblown. Adam Cheyer, co-founder of Apple's Siri and virtual assistant A.I. startup Viv, compared the fears that A.I. will become too smart to worrying about overpopulation on Mars. "We're barely at the beginning of A.I.," he said. "There's nothing to even be done yet."
Last month, Jeff Bezos, whose popular Amazon Alexa relies heavily on A.I., said during a chat at the Internet Association that the problem with artificial intelligence is that we don't have more of it. "Basically," he said, "there's no institution in the world that cannot be improved with machine learning."
Google co-founders Larry Page and Sergey Brin have also spoken out in defense of A.I.
Meanwhile, there's also a vocal group within the tech industry that errs on the fear-mongering side. A recent survey of academics and industry leaders found they believe, on average, that A.I. will be capable of performing any task--from driving trucks to writing novels--better than humans by 2060.
Elon Musk, whose Tesla vehicles rely on artificial intelligence, soon chimed in with the notion that this would happen closer to 2030. "I hope I'm wrong," he tweeted.
Other Silicon Valley giants have warned against the technology. Peter Thiel co-founded OpenAI, a non-profit to ensure A.I.'s safe use, along with Musk. Earlier this year, Bill Gates suggested that robot taxes could help slow the loss of jobs to automation. And Tim Berners-Lee, inventor of the world wide web, recently warned that A.I. could one day replace financial institutions and control the world economy.
The lobbying push from IBM comes about a month after the formation of the Congressional Artificial Intelligence Caucus, a group of Representatives that will study A.I. and seek to create policies related to its use and implementation. Congressman John K. Delaney of Maryland, one of the group's co-founders, recently met with Amazon and Google, according to CNBC. The meeting with IBM on Wednesday is the group's next step.
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Curiosity Mars Rover is now using artificial intelligence to pick its own targets – Fox News
Posted: at 11:16 am
The Curiosity Mars Rover is now smart enough to pick its own targets for exploration, according to a new study.
The secret to Curiosity's better brain was a software update sent from the ground in October 2015, called the Autonomous Exploration for Gathering Increased Science (AEGIS). This was the first time artificial intelligence had been tried on a remote probe, and the results have shown that similar AI techniques could be applied to future missions, according to the NASA scientists working on the project.
AEGIS allows the rover to be "trained" to identify rocks with certain characteristics that scientists on the ground want to investigate. This is valuable because Curiosity's human controllers can't be in direct contact with the rover all the time. Instead of waiting for instructions to "go there and sample that piece of rock," Curiosity can now look for targets even when it isn't in contact with its human controllers, according to a new study that describes Curiosity's use of the software. [Amazing Mars Rover Curiosity's Latest Photos]
"We can't be in constant contact with the rover Mars rotates and when [Curiosity is] on the far side we can't get in touch with it," Raymond Francis, lead system engineer for the deployment of AEGIS, told Space.com.
According to the study, once the AEGIS system was deployed, it was used 54 times between May 13, 2016, and April 7, 2017. Without intelligent targeting, Curiosity could be expected to hit a target the scientists were interested in about 24 percent of the time; with AEGIS, the rover managed 93 percent, according to the study.
Even when the rover is in contact, the signals from Earth to Mars take time to get there and back. In May 2016, Mars was the closest it had been to Earth in 11 years 46.8 million miles. A radio signal would take just over 4 minutes to get there and four more to get back. So if there is something planetary scientists want a closer look at, it can take a while to send the commands.
Idle time is often lost science time for the rover mission, and because sending a robot to Mars is expensive and difficult, it's not ideal. A few hours hanging around each day may not seem like much, but it adds up over the course of an entire mission. With AEGIS, the rover could drive to a location, choose targets for investigation and gather data while it waits for radio contact with Earth again. That means Earth-bound scientists are free to choose a new target once they re-establish contact with the rover.
For the study, the NASA team trained Curiosity, with the AEGIS software, to analyze bedrock in a feature called the Murray formation after each drive. The Murray formation is a rocky outcrop with characteristic bands of mudstone, possibly laid down by lakes of liquid water. One question the scientists wanted to answer was whether the chemical composition of the Murray formation changed over time, because that could reveal changes in the water chemistry, divulging more about the history of water on Mars.
This analysis of the Murray formation required taking many samples of the mudstone, but doing them would take time away from other experiments and observations. With AEGIS, Curiosity took care of these repetitive observations when it was out of touch with Earth, and researchers would not be using it for more advanced tasks. One could use AEGIS to train Curiosity to look for other types of rock, Francis said.
The AEGIS system works by using two of the rover's cameras, the Chemistry and Camera instrument (ChemCam) and the navigation cameras. The software uses images captured by the cameras, and tries to recognize edges of objects in the frame, and looks for edges that connect to create a "loop."
"If you find edges that close into a loop you've found an object and on Mars that's usually a rock," Francis said. AEGIS can also look at the relative brightness of the various objects in the frame (the navigation cameras don't have color vision). The combination of edges and brightness allows AEGIS to identify objects.
The science team will have criteria for what kinds of things count as interesting for example, brightly colored bedrock and the rover can then use the cameras to "choose" a target. The ChemCam can then use a powerful instrument called a laser spectrometer that uses light to find out what a target is made of.
There are limitations to AEGIS' abilities; for example, the rover sometimes identified a rock's shadow as part of the rock's outline. Even so, the software has proved a useful tool, the study said.
Francis notes that the autonomy will likely become a fixture for many future robotic missions.
"The farther you go in the solar system, the longer the light time delay, the more decisions need to be made on the spot," he said.
The study appears in the June 21 issue of the journal Science Robotics.
Follow us @Spacedotcom , Facebook and Google+ . Original article on Space.com .
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This Artificial Intelligence Kiosk Will Spot Liars at Airports, Keep You Safer – Inc.com
Posted: at 11:16 am
From Alexa and self-driving cars to job applicant screening processes, artificial intelligence is fast becoming the norm in business. But it also could start playing far bigger roles in security, helping law enforcement and other protective agents figure out who's up to no good. As Fredrick Kunkle of The Washington Post reports, there's now an AI-based kiosk designed to detect whether travelers are fibbing.
Designed by Aaron Elkins, assistant professor of the Fowler College of Business Administration at San Diego State University, the new AI lie detector goes by the name Automated Virtual Agent for Truth Assessments in Real Time, or AVATAR for short. Once you've scanned your ID or passport, the kiosk asks you a bunch of questions. The inquiries are a good mix of inquiries you could practice (e.g., when were you born) and questions that might throw you if you're faking it (e.g., describe what you did today). You can see some of the process in the video below:
If everything goes well, security personnel should let you go on your way. If the results suggest you're being dishonest, security personnel might detain you for questioning or a search.
As you answer questions from AVATAR, the system uses sensors to gather data your body gives off. More specifically, the system looks at factors like voice (tone, pronoun use, etc.), pupil dilation and eye movement, facial expression (e.g., engagement of muscles around the corners of the eyes and mouth in a Duchenne smile) and posture. The theory is that it takes less effort to tell the truth than to maintain a faade. You subconsciously reveal that effort through physical cues, many of which researchers are still studying and pinning down. The AI is a big step forward from traditional polygraphs, which aren't practical for general, large-scale screening, use more limited physiological data (e.g, heart rate) and generally aren't considered very reliable.
In theory, AVATAR could become a widely applied staple in local law enforcement agencies around the world, helping police sort out a variety of conflicts. But its main intent is for border security checkpoints and airports. These facilities are of concern in part because of the high traffic they receive. But they are also worry points because of the current worldwide focus on terrorism. Although these types of attacks can come from many different individuals or groups and can be domestic or foreign in nature, the increasing activity of the Islamic State of Iraq and Syria (ISIS) has been particularly alarming for leaders around the globe. Attacks have led U.S. President Donald Trump, for instance, to call for a controversial travel ban against travelers from six majority-Muslim countries. AVATAR might one day help screen out individuals associated with ISIS or similar groups.
Right now, AVATAR is still in its infant stages. It's only collecting research data at border crossings in Mexico and Romania. But even at this point, it's a beautiful demonstration of how science and technology can blend toward a practical social good.
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New Artificial Intelligence Hub At CMU Aims To Make Pittsburgh A World Leader In AI – 90.5 WESA
Posted: at 11:16 am
Faculty and staff from several schools at Carnegie Mellon University are joining forces in an effort to accelerate the science of Artificial Intelligence.
University leaders said they hope that by pulling together more than 100 faculty through the creation of CMU AI, it will maintain the universitys role as a leader in the field.
CMU School of Computer Sciencedean Andrew Moore said the confederation of faculty and students from various disciplines, which will allow the school to offer what he calls full stack education and research.
That means [the students] need to be able to hang out and work on projects in labs not just with the technology experts on specific parts of AI, like machine learning or computer vision, but they have seen examples of putting everything together, Moore said.
Moore said the university has been able to build great AI systems that combine technologies from several different disciplines. However, they have been dependent on individual faculty members and students with a special vision.
Were relying on the fact tat weve got a few smart faculty and students whove got the way to do this sitting in their head, Moore said. Its almost an art rather than a technology. By creating CMU AI, were going to turn this into a technology stack so we can educate eventually hundreds of thousands of people to be able to assemble these large robot systems.
The idea was born about a year ago when a group of potential students told him they were impressed by CMU but they wanted to learn about artificial intelligence, not just its parts.
Artificial Intelligence is slowly becoming a part of our every day lives, and Moore said the nation needs this type of confederation to lead the way. He said the mix of educators, researchers and commercial endeavors, such as Uber, in Pittsburgh puts the city in a unique position to be the worlds leader.
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TurboPatent aims to improve the patent process with new artificial … – GeekWire
Posted: at 11:16 am
A look at TurboPatents new RoboReview service. (TurboPatent Photo)
Seattle startup TurboPatent is releasing a pair of new products designed to improve the patent application process, with help from artificial intelligence.
TurboPatent, which raised $1.4 millionin funding earlier this year, focuses on corporations and law firms, automating taskslike formatting or document preparation, for example, freeing up people to work on more complex, high-value work. The service is designed to cut costs, save time and lead to more accurate patent documentation.
The new products are called RoboReview and RapidResponse. RoboReview uses AI and predictive analytics to automatically analyze and review draft patent applications. The company says this will reduce to seconds a process that can normally take several people multiple days to complete. RapidResponse helps speed upoffice actions,written correspondence between an applicant and patent examiner during the application process.
Like any procedure that involves people, the review process is subject to human error, said TurboPatent CEO James Billmaier. TurboPatent automates the most tedious and time-consuming parts of the process, which drastically cuts down on the likelihood of potential issues going unnoticed. That leaves humans to decide whether or not to submit a patent and if so, what alterations need to be made.
Formerly known as Patent Navigation, TurboPatent has an experienced team led by co-foundersBillmaier and Charles Mirho. Billmaier was previously CEO of Melodeo, a cloud-based media platform company that sold to HP in 2010. He also teamed up with Paul Allen in 1999 to launchhome-entertainment technology company Digeo, which was eventually sold in 2009 to ARRIS Group Inc.
Mirho, meanwhile, is a patent law veteran, having worked as a patent counsel at Intel and later as a managing partner of a patent law firm. He also has a computer science degree from Rutgers.
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