Artificial Intelligence | The Knowledge Dynasty

Artificial Intelligence

The robot debate is over: the jobs are gone and they aren’t coming back

New report shows automation is already causing losses, depressing wages and likely to have lasting, devastating effect.

In 2013, the Oxford Martin School released a report that looked at the automation of work, assessing the likelihood that robots and other technologies would replace humans. It concluded that of the 702 job categories examined, 47% were susceptible to automation within the next 20 years. The report completely upended our ideas about the future of work.

Now, a new report by the National Bureau of Economic Research (NBER) in the United States is set to be an even bigger wake-up call. Written by economists Daron Acemoglu (MIT) and Pascual Restrepo (Boston University), it not only adds support to the Oxford Martin conclusions, it actually suggests the jobs are already lost and unlikely to come back.

It contends that in the US between 1990 and 2007, the addition of each robot into manufacturing industries resulted in the loss, on average, of 6.2 human jobs. It also suggests automation depressed wages by between a quarter and a half of one per cent. Using this approach, the report says, we estimate large and robust negative effects of robots on employment and wages across commuting zones.

There is another important insight: these jobs losses and lower wages are likely to have a lasting and devastating effect. Author Daron Acemoglu told the New York Times that, even if overall employment and wages recover, there will be losers in the process, and its going to take a very long time for these communities to recover. The market economy is not going to create the jobs by itself for these workers who are bearing the brunt of the change.

These are game-changing findings, so let me put them into context of the overall debate.

There has been a rather unproductive back-and-forth over whether or not robots are going to take our jobs. This dead end approach was something I warned about in my book Why The Future Is Workless when I wrote, Lets not go down the same route we have with climate change and mindlessly divide ourselves into camps of sceptics and advocates. Lets instead bypass the ultimately futile argument about whether or not robots will take our jobs (they will) and make the imaginative leap, together, into a workless future that can liberate us all.

Much of the argument has rested on the claim that technology ultimately creates as many jobs as it destroys (an approach that author Calum Chace calls the reverse Luddite fallacy).

Probably the most influential proponent of this argument is MIT economist David Autor. His important paper, Why Are There Still So Many Jobs?, although careful to allow for the fact that past behaviour is not always a great predictor of future outcomes, nonetheless notes that journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor.

As recently as last week, Australian economic commentator, Ross Gittins, ran a similar line in a strongly worded piece decrying so-called futurologists for scaring everyone about job losses. He wrote, improving the productivity of a nations labour increases its real income. When that income is spent, jobs are created somewhere in the economy. Technological advance doesnt destroy jobs, it displaces them from one part of the economy to another.

This claim, of course, was always as much a guess about the future of work as anything offered by dreaded futurologists, but the point is, the NBER report makes it even more tenuous than it was. In fact, Acemoglu and Restrepo specifically argue there is little evidence of new jobs being created, saying the results indicate a very limited set of offsetting employment increases in other industries and occupations.

What lends the NBER report added authority is it doesnt rely on modelling to predict what robots are likely to do to jobs in the future, but on hard data to look at what robots are already doing to jobs in the present. The results are so startling that even the authors were surprised, having previously taken a much more sceptical line.

So where does this leave us? Well, we need to keep things in perspective. The future of work is a hugely complex issue, social and political as much as technological, and one new report, however important, hardly settles the matter. Nonetheless, Acemoglu and Restrepos findings do give us a new baseline for our discussions.

In so doing, they will likely reanimate calls for a universal basic income, because if there really are fewer jobs, we are going to need new ways of distributing wealth.

The report also challenges the neoliberal tenet that unregulated markets are a surefire way to full employment, and it can reasonably be taken to imply a large role for governments in managing the change that is coming. Additionally, it undermines the persistent claim that technology will create enough jobs in the future because this is what happened in the past.

Most importantly, the results suggest politicians and others who carelessly promise jobs and growth need to stop waffling and start taking seriously the fact that the future of work is going to be a very different beast to the past and present of work. We are likely to face not just different sorts of work, but far fewer jobs.

How we respond to this reality will be a huge test for our democracies, and this report is an important contribution to the ongoing debate.

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‘Future of Sex’ podcast tackles the frontiers of sex and technology

Bryony Cole wants women to build the sex tech future they want.

Image: Shutterstock / WhiteHaven

We built the internet, we got online porn. We invented haptic engines that can automatically convey a remote sense of touch, we got teledildonics.

Technological advances and the shifting of sexual boundaries are intertwined, and this is the world Bryony Cole tackles on her podcast, Future of Sex.

After a career in technology, including a stint at Microsoft, she was fascinated by the way digital platforms were entering the bedroom. “I made it my mission to uncover what the hell was going to happen to our intimate lives,” she laughed. And why not in audio-form?

Since October, she’s spoken to industry leaders including Stephanie Alys, the excellently-titled chief pleasure officer at toy company, Mystery Vibe, as well as Cindy Gallop, creator of the site Make Love Not Porn, which shares videos of real people having sex.

In Brisbane, Australia, to address the Myriad startup festival (her speech was unfortunately rained out by Cyclone Debbie), Cole argued there’s an enormous opportunity for women to take the lead in sex tech, and to move away from male-focused porn and cheaply manufactured intimate products.

“Finally, we’re talking about female sexual health and female pleasure where we’ve never really done it before,” she said. “We have examples of vibrators that match to erotic literature on your iPad, or we have vibrators that look like beautiful necklaces or pieces that could be in a gallery.

“There’s a real focus on beautiful high-end design, but also the emphasis on research and engineering that never really was going into things like sex toys before.”

Take Dame Products, which launched its Fin vibrator on Kickstarter in 2016. Founded by Alexandra Fine and Janet Lieberman, with backgrounds in sexual health and engineering respectively, the pair create small, elegant products with a focus on female pleasure.

Still, there’s work to be done. Virtual reality porn, Cole argued, is still mostly shot from the male angle. “There’s probably just a handful that exist from a female point of view. It’s not quite there yet,” she said.

Despite this gap in the market, Cole is particularly excited about the role VR can play in sex education. She pointed to BaDoink VR and one of its popular pieces of content, a program called Virtual Sexology. Developed with the help of marriage therapist Hernando Chaves, it’s intended to help users learn bedroom skills as well as relaxation and confidence.

“You see VR being used for education in science and history already, so it makes sense that it would move over to sex education,” Cole added. “It’s nothing to do with the porn category, but people are hungry for information.”

Despite the innovation and openness, Cole admitted some sex tech ideas remain partially taboo. There are plenty of questions to be asked about lab-grown genitals, for example, and while robot sex and sex dolls are themes explored by TV shows like Westworld, they’re not the distant fantasy the Hollywood treatment may present them as.

In a Mar. 29 podcast episode, Cole spoke to the creator of RealDoll, Matt McMullen, whose company offers customised companions with highly realistic genitalia.

A worker ships crates of finished silicone RealDoll sex dolls at the Abyss Creations factory on February 5, 2004 in San Marcos, California.

Image: David McNew / getty

McMullen said his team are working on adding artificial intelligence to the dolls, a development that could see the dolls, mostly cast as female, become quasi-intelligent beings built for sex.

“What we’re doing is developing an app that will allow a user to create a custom personality profile for an artificial intelligence,” he said on the show. “The AI can be connected to the robotic components we’re going to be introducing to the doll, including animated head, facial expressions, eye movement … face tracking, facial recognition.”

While the basis of the AI is “really rather genderless,” he admitted the introductory version will likely be female, as most of its dolls are sold as women.

If this makes you uncomfortable, you’re not alone. As sex tech develops, the industry will have to ask if sexism and racism, not mention invasive corporate practices, are being hardwired into the algorithms and robotics that govern our most intimate moments. After all, internet-connected vibrators have already been caught and fined for data-mining users.

That’s where Cole hopes the Future of Sex will come in to help you make sense of these sexual frontiers. As the podcast says, “Real talk. No b*******. That’s the future of sex.”

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Japanese authorities decry ongoing robot failures at Fukushima

Six years ago, a massive earthquake, consequent tsunami and nuclear crisis struck Japan. International organizations rushed to help the countrys devastated residents, and to figure out how to clean up Fukushima Daiichi, the wrecked nuclear power plant. Robots offered a ray of hope amid unfathomable loss. At least they did, until recently.

As the Asahi Shimbun reported yesterday, members of Japans Nuclear Regulation Authority are now urging plant operators Tokyo Electric Power Company to find new technology and methods to aid in the cleanup. Robots keep getting fried on their missions, literally from radiation damage, or stranded on-site wastingprecious money and time.

The implication is that, perhaps, the clean up will move faster if Tepcos energy and the governments money is redirected to chemistry, biology, and so-called safe containment, building some sort of structure around Fukushima Daiichi like the sarcophagus around Chernobyl. Or perhaps humans need to trust AI to move robots through some of their tasks. All of the robots deployed in the cleanup effort have been remote-operated by humans, so far. The governmentwatchdogs critical comments followed the latest robo-fail revealed by Tepco.

The PMORPH survey robot is being used to clean up Fukushima.

On March 23 the company said it had attempted to send a survey robot into a containment vessel to find fuel debris, information it needs to decommission the plant. But the PMORPH survey robot, developed by Hitachi-GE Nuclear Energy and the International Research Institute for Nuclear Decommissioning (IRID), couldnt get its cameras to the predetermined location. As a result, it only sent back a partial report.

Just one month earlier, Tepco aborted a mission using a Toshiba scorpion robot that was built to scramble over rubble, capture images and data inside the plants facilities. The robot could tolerate up to 1,000 sieverts of radiation. And yet, it had troublewithin the hostile environs of the number 2 reactor where it was dispatched.

These followed a string of earlier robot losses at the plant going back to the Quince 1, the first robot to enter the facility after the disaster. Developed by the Chiba Institute of Technology, the International Rescue System Institute, and Tohoku University in Japan, Quince went into the power plants reactor 2 building where it measured radiation levels, collected dust samples and video footage. It ran several missions but eventually disconnected from its communications cable and got stranded within the building.

This scorpion robot was builttoinvestigate inside containment vessels at the Fukushima Daiichi Nuclear Power Plant.

It’s not like anyone thought it would be easy to make robots capable of finding and retrieving molten nuclear fuel, or decommissioning and decontaminating a nuclear power plant. Japanese researchers have been trying to create robots with these capabilities since the 80s, as Timothy Hornyak wrote in the journal Science last year. Robots remain incredibly tantalizing technology.

With cameras, dosimeters, and other tools on board, robots can ostensibly go where conditions would prove fatal to humans. If they were strong and agile enough, they might be able to bring core samples up for scientists to test, or find and plug leaks, clear paths and scour away radioactive materials. The ultimate task would be for robots to identify and retrieve some 600 tons of molten nuclear fuel and debris from Fukushima.

Despite the nuclear watchdogs most recent admonition, many robots, even the fried ones, have been helpful in what little progress has been made in cleaning up the site.

iRobots PackBot 510 E.T.

Early on, iRobots ground-based PackBot and Warrior robots, and Honeywells T-Hawk drones helped TEPCO get a handle on radioactivity and conditions around its facilities, including around damaged reactors within weeks of the disaster. Swimming and crawling robots, also developedbyHitachi and GE Nuclear energy, were used in a 2014 mission to capture images and readings fromwithin a damaged reactor.

Still, with every failed or aborted mission, every $1 million spent, it gets harder to tell people devastated by a crisis that robots are their greatest hope. Japan’s 3/11 crisis killed tens of thousands, left thousands missing and displaced a quarter of a million people. As radiation first gushed from the Fukushima-Daiichi nuclear plant, millions of residents were left mourning without electricity or water through cold and wet, end-of-winter weather.

More than half of those who fled or were evacuated from the area have no plans to come back, even still, according to Japanese government surveys. Scientific studies have concluded that certain areas are safe for residents’ return. But there’s not much in the way of schools, stores or other critical community support around Fukushima, and fears linger. The Japanese government estimates the cleanup effort will cost $189 billion and will take decades.

Let’s hope the next step change in technology, whether in robotics or another promising area, will hasten the Fukushima recovery, and prevent nuclear disasters from ever happening again.

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Advances in AI and ML are reshaping healthcare

The healthcare technology sector has given rise to some of the most innovative startups in the world, which are poised to help people live longer, better lives. The innovations have primarily been driven by the advent of software and mobility, allowing the health sector to digitize many of the pen and paper-based operations and processes that currently slow down service delivery.

More recently, we’re seeing software become far more intelligent and independent. These new capabilities studied under the banner of artificial intelligence and machine learning are accelerating the pace of innovation in healthcare. Thus far, the applications of AI and ML in healthcare have enabled the industry to take on some of its biggest challenges in these areas:

  • Personal genetics
  • Drug discovery
  • Disease identification and management

Upon close evaluation of the opportunities that exist within each area, it becomes obvious that the stakes are high. As such, those that are first to market with a sustainable product differentiation and value-add will benefit tremendously.

Ushering in a new era of personal genetics

The most significant application of AI and ML in genetics is understanding how DNA impacts life. Although the last several years saw the complete sequencing of the human genome and a mastery of the ability to read and edit it, we still dont know what most of the genome is actually telling us. Genes are constantly acting out of place in combination with other variables such as food, environment and body types.

If we are to understand what influences life and biology, we must first understand the language that is DNA. This is where ML algorithms come in and the advent of systems such as Googles Deep Mind and IBMs Watson. Now, more than ever, it has become possible to digest immense amounts of data (e.g. patient records, clinical notes, diagnostic images, treatment plans) and perform pattern recognition in a short period of time which otherwise would have taken a lifetime to complete.

Businesses such as Deep Genomics are making meaningful progress in this realm. The company is developing the capability to interpret DNAby creating a system that predicts the molecular effects of genetic variation. Their database is able to explain how hundreds of millions of genetic variations can impact a genetic code.

Once a better understanding of human DNA is established, there is an opportunity to go one step further and provide personalized insights to individuals based on their idiosyncratic biological dispositions. This trend is indicative of a new era of personalized genetics, whereby individuals are able to take full control of their health through access to unprecedented information about their own bodies.

The technology must have access to vast amounts of data in order to better curate lifestyle changes for individuals.

Consumer genetics companies such as 23andMe and Rthm represent a few of the first movers in this domain. They have developed consumerized genetic diagnostic tools to help individuals understand their genetic makeup. With Rthm, users are able to go one step further and leverage the insights produced from their genetic test to implement changes to their everyday routine through a mobile application, all in real time.

As is the case with any application of AI/ML, the technology must have access to vast amounts of data in order to better curate lifestyle changes for individuals. Startups that are focused on mastering the delivery of personal genetics are doing so by considering the following key activities, as highlighted by Japan-based researcher Takashi Kido:

  • Acquiring reliable personal genome data and genetic risk prediction
  • Conducting behavior pattern analyses on peoples attitude to the personal genome to determine what kind of information is valuable/helpful and what type of information is damaging
  • Data mining for scientific discovery

The second point is interesting in that not all genetic information about a patients biological predispositions is productive. Being able to control the information in a manner that is conducive to psychological well-being is critical.

Hyper targeted drugs are the future

Another exciting application of AI/ML in healthcare is the reduction of both cost and time in drug discovery. New drugs typically take 12 to 14 years to make it to market, with the average cost hovering around $2.6 billion. During the process of drug discovery, chemical compounds are tested against every possible combination of different cell type, genetic mutation and other conditions relating to a particular ailment.

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As the task of doing this is time-consuming, this limits the number of experiments or diseases that scientists can look to attack. ML algorithms can allow computers to learn how to make predictions based on the data they have previously processed or choose (and in some cases, even conduct) what experiments need to be done. Similar types of algorithms also can be used to predict the side effects of specific chemical compounds on humans, speeding up approvals.

San Francisco-based startup Atomwise is looking to replace test tubes with supercomputers during the drug development process. The company uses ML and 3D neural networks that sift through a database of molecular structures to uncover therapies, helping to discover the effectiveness of new chemical compounds on diseases and identifying what existing medications can be repurposed to cure another ailment.

In 2015, the company applied its solution and uncovered two new drugs which may significantly reduce Ebola infectivity. The analysis was completed in one day as opposed to years, which is common using traditional methods of drug development. A recent study by Insilico Medicine solidified the approach Atomwise is taking, showing that deep neural networks can be used to predict pharmacologic properties of drugs and drug repurposing.

The application of AI/ML in healthcare is reshaping the industry and making what was once impossible into a tangible reality.

Berg Health, a Boston-based biopharma company, attacks drug discovery from a different angle. Berg mines patient biological data using AI to determine why some people survive diseases, and then applies this insight to improve current therapies or create new ones.

BenevolentAI, a London-based startup, aims to expedite the drug discovery process by harnessing AI to look for patterns in scientific literature. Only a small portion of globally generated scientific information is actually used or usable by scientists, as new healthcare-related studies are published every 30 seconds. BenevolentAI enables analysis on vast amounts of data to provide experts with insights they need to dramatically expedite drug discovery and research. Recently, the company identified two potential chemical compounds that may work on Alzheimers, attracting the attention of pharmaceutical companies.

As advances in ML and AI continue, the future of drug discovery looks promising.A recent Google Research paper notes that using data from various sources can better determine which chemical compounds will serve as effective drug treatments for a variety of diseases, and how ML can save a lot of time by testing millions of compounds at scale.

Discovering and managing new diseases

Most diseases are far more than just a simple gene mutation. Despite the healthcare system generating copious amounts of (unstructured) data which is progressively improving in quality we have previously not had the necessary hardware and software in place to analyze it and produce meaningful insights.

Disease diagnosis is a complicated process that involves a variety of factors, from the texture of a patients skin to the amount of sugar that he or she consumes in a day. For the past 2,000 years, medicine has been governed by symptomatic detection, where a patients ailment is diagnosed based on the symptoms they are displaying (e.g. if you have a fever and stuffy nose, you most likely have the flu).

But often the arrival of detectable symptoms is too late, especially when dealing with diseases such as cancer and Alzheimers. With ML, the hope is that faint signatures of diseases can be discovered well in advance of detectable symptoms, increasing the probability of survival (sometimes by up to 90 percent) and/or treatment options.

The opportunities continue to grow and inspire healthcare practitioners to find new ways to enhance our health and well-being.

Freenome, a San Francisco-based startup, has created an Adaptive Genomics Engine that helps dynamically detect disease signatures in your blood. To make this possible, the company uses your freenome the dynamic collection of genetic material floating in your blood that is constantly changing over time and provides a genomic thermometer of who you are as you grow, live and age.

When looking at disease diagnosis and treatment plans, companies such as Enlitic are focused on improving patient outcomes by coupling deep learning with medical data to distill actionable insights from billions of clinical cases. IBMs Watson is working with Memorial Sloan Kettering in New York to digest reams of data on cancer patients and treatments used over decades to present and suggest treatment options to doctors in dealing with unique cancer cases.

In London, Googles Deep Mind is mining through medical records of Moorfields Eye Hospital to analyze digital scans of the eye to help doctors better understand and diagnose eye disease. In parallel, Deep Mind also has a project running to help with radiation therapy mapping for patients suffering from neck and head cancer, freeing up hours of planning for oncologists to allow them to focus on more patient care-oriented tasks.

What does all of this mean?

The application of AI/ML in healthcare is reshaping the industry and making what was once impossible into a tangible reality.

For AI/ML to become pervasive in healthcare, continued access to relevant data is essential to success. The more proprietary data a system can ingest, the smarter it will become. As a result, companies are going to great lengths to acquire data (which resides in an anonymized format). For example, IBM bought out healthcare analytics company Truven Health for $2.6 billion in February 2016 primarily to gain access to their repository of data and insights. In addition, they recently partnered with Medtronic to further Watsons ability to make sense of diabetes through gaining access to real-time insulin data.

As the data becomes richer and the technology keeps advancing, the opportunities continue to grow and inspire healthcare practitioners to find new ways to enhance our health and well-being.

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The only way to prove you aren’t a robot is to solve this chess puzzle

Check it out, Mate!

Image: James Tagg

Check out the chess board above: looks wrong, right?

If you’ve ever played chess, you know something’s amiss, here. For one thing, someone chose to exchange a pawn for another bishop instead of a queen. For another, virtually all the action’s moved to the left side of a board.

It’s hard to imagine how the game got here: it’s even harder to imagine what happens next, let alone a scenario in which four white pawns and a white king could play to a draw, or even win this game.

The Penrose Chess Puzzle: Can you find the solution that results in either a white win or a game draw?

Image: James tagg

Yet: scientists at the newly-formed Penrose Institute say it’s not only possible, but that human players see the solution almost instantly, while chess computers consistently fail to find the right move.

“We plugged it into Fritz, the standard practice computer for chess players, which did three-quarters of a billion calculations, 20 moves ahead,” explained James Tagg Co-Founder and Director of the Penrose Institute, which was founded this week to understand human consciousness through physics.

“It says that one-side or the other wins. But,” Tagg continued, “the answer that it gives is wrong.”

Tagg and his co-founder, Mathematical Physicist and professor Sir Roger Penrose who successfully proved that black holes have a singularity in them cooked up the puzzle to prove a point: Human brains think differently.

(Those who figure out the puzzle can send their answers to Penrose to be entered to win the professor’s latest book.)

“Humans can look at a problem like this strange chess board configuration,” said Tagg, “and understand it. What a computer does is brute force calculation, which is different.” This is set up, rather exquisitely, to show the difference, he added.

They forced the computer out of its comfort zone by, at least in part, making an unusual choice: the third bishop.

“All those bishops can move in lots of different ways, so you get computation explosion. To calculate it out would suck up more computing power than is available on earth,” claimed Tagg.

Tagg told us that there is, in fact, a natural way to get to this board configuration.

We’re trying to figure it out here, but lacked an extra black bishop. So we tagged one to keep track.

Image: lance ulanoff/mashable

Sir Richard Penrose’s brother is, according to Tagg, a very strong chess player. He assures me that it’s a position you can get to, but I have not played it through. Question is, is there a rational game that gets you there?

In fact, those who can figure out that second puzzle and get the answer to Penrose, could also receive a free copy of Professor Penrose’s book.

Chess computers fail at Penrose’s chess puzzle because they have a database of end-games to choose from. This board is not, Tagg and Penrose believe, in the computer’s playbook. “We’re forcing the chess machine to actually think about the position, as opposed to cheat and just regurgitate a pre-programmed answer, which computers are perfect at,” said Tagg.

So far, Tagg and the Penrose Institute haven’t heard from any artificial intelligence experts refuting their claims. “I’m quite surprised,” said Tagg.

Mashable has contacted several AI experts for comment and will update this post with their response.

Aside from the fun of solving this puzzle (Tagg said hundreds already have and claim they have done so in seconds), it poses a deeper question: Are we executing some fiendishly clever algorithm in our brain, that cuts through the chaff? It is just a higher level of computation, one that computers can still aspire to or something unique to brain-matter-based thought?

Tagg said Penrose Institute falls into the latter camp.

Penrose and Tagg don’t think you can simply call a brain a machine. It sits in skull, made of gray matter and we don’t understand how it works. “Simply calling it a clever computer, this sort of puzzle shows that it clearly is not,” he said.

You can send your Chess Puzzle solution to the Penrose Institute here:

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Can this Al win a $200,000 poker battle?

An AI system named Libratus will go up against top poker players during matches later this month in Pittsburgh.

Image: joe raedle/ Getty Images

Artificial intelligence has already become a part of our everyday lives through AI-assisted services like Siri. But Al has its own hobbies too, including the mind-whirling game of poker.

Scholars at Carnegie Mellon University have developed an AI system named Libratus that will wage a poker battle (with a $200,000 pot) against four of the best human pros out there. In a tournament called Brains Vs. Artificial Intelligence: Upping the Ante, Libratus and the humans will play matches of Heads-Up No-Limit Texas Holdem beginning on Jan. 11 at the Rivers Casino in Pittsburgh, Pennsylvania, the university announced in a press release.

A total of 120,000 hands will be played during the 20-day tournament. The professional players Libratus will compete with include Jason Les, Dong Kim, Daniel McAulay, and Jimmy Chou.

It wouldn’t be the first time bots have played some big games against humans. Indeed such match-ups go back decades. Last March, a Google-produced bot named AlphaGo easily beat 18-time world Go champion Lee Se-dol.

South Korean professional Go player Lee Se-Dol lost to Google’s artificial intelligence program, AlphaGo, back in March 2016 in Seoul.

Image: Google via getty Images

But the thing is that poker isn’t like many of these other games.

As explained by the MIT Technology Review, the game of poker may not be as easily dominated by computerized players as others on accounts of its unique rules.

“Unlike board games such as Go or chess, poker is a game of ‘imperfect information,’ and for this reason it has proved even more resistant to computerization than Go,” the MIT Technology Review‘s Will Knight once wrote.

In games like chess, “you know exactly what has happened in the game so far,” explains Carnegie Mellon computer science researcher Tuomas Sandholm, who developed Libratus alongside Ph.D. student Noam Brown. Such an advantage doesn’t exist in poker, where there’s crucial unknown information like your opponents’ hands.

And the game of Heads-Up No-Limit Texas Holdem has been especially difficult for AI to take on since it deals with a huge scale of probabilities making it a “much bigger game” than Limit Texas Hold’em, Sandholm said.

In the past, game-playing AI has used strategies like programmed human knowledge or machine learning. But more recently, the approximation of equilibrium strategies or rational play has been more effectively used by AI to compete and sometimes beat human players, Sandholm told Mashable.

“It’s very different from how humans play this game.”

Libratus uses the Nash Equilibrium, named after the famed mathematics scholar John Nash, the subject of A Beautiful Mind.

“Named for the late Carnegie Mellon alumnus and Nobel laureate John Forbes Nash Jr., a Nash equilibrium is a pair of strategies (one per player) where neither player can benefit from changing strategy as long as the other players strategy remains the same,” a statement from CMU says. “One of Libratus new technologies is a faster equilibrium-finding method. It identifies some paths for playing a hand as not promising. Over time, the algorithm starts to ignore those bad paths.”

Sandholm and others also created Claudico, an AI system that ultimately lost to human players at poker matches last year. But Sandholm said Libratus has been improved, with more core hours put into programming the system, two new algorithms intended to better play the game and more computing resources and hardware.

It has been programmed to know the rules of poker, but Libratus doesn’t rely on any other information. For example, it has never referenced material from a poker book or poker expert or any other sources, Sandholm told Mashable. This means it will sometimes play the game in a totally new way, making moves that may seem unfavorable to the best of traditional human players.

“It’s very different from how humans play this game,” Sandholm said of the Libratus approach.

“It plays like a martian,” he said. “It’s deriving its own strategy from just the rules of the game.”

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Kazuo Ishiguro: ‘Were coming close to the point where we can create people who are superior to others’

Social changes unleashed by new technologies could undermine core human values unless we engage with science, warns author.

Imagine a two-tiered society with elite citizens, genetically engineered to be smarter, healthier and to live longer, and an underclass of biologically run-of-the-mill humans. It sounds like the plot of a dystopian novel, but the world could be sleepwalking towards this scenario, according to one of Britain’s most celebrated writers.

Kazuo Ishiguro argues that the social changes unleashed by gene editing technologies, such as Crispr, could undermine core human values.

“We’re going into a territory where a lot of the ways in which we have organised our societies will suddenly look a bit redundant,” he said. “In liberal democracies, we have this idea that human beings are basically equal in some very fundamental way. We’re coming close to the point where we can, objectively in some sense, create people who are superior to others.”

Ishiguro spoke to the Guardian ahead of the opening of a new permanent mathematics gallery at the Science Museum in London, which features a machine to predict coastal storm surges built by his oceanographer father, Shizuo Ishiguro.

The author hopes that the 5 million exhibition, and others like it, will encourage people to engage with the process of science and its future trajectory, rather than simply tuning in for the headline results of research and only then worrying about the implications.

Despite the atom bomb and things like this, were still in the habit of compartmentalising scientific endeavour, he said. Its important that we, as a society, get much more interested in science and maths, that we dont silo it off in our minds … until theres some breakthrough product that turns up.

Ishiguro cites three areas – gene editing, robotics and Artificial Intelligence – that he believes could transform the way we live and interact with each other over the next 30 years.


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We are on the brink of all kinds of discoveries that will completely alter the way we run our lives, said the author, whose 2005 book, Never Let Me Go, imagines a dark future in which human clones are raised to be organ donors.

The gene editing tool, Crispr, allows scientists to cut, paste and delete single letters of the genome with unprecedented precision, meaning aberrant genes can be overwritten with working copies, and, potentially, functional genes replaced with enhanced versions. Chinese scientists are already trialling the technology in patients to treat lung cancer.

“When you get to the point where you can say that person is actually intellectually or physically superior to another person because you have removed certain possibilities for that person getting ill or because they’re enhanced in other ways, that has enormous implications for very basic values that we have,” said Ishiguro.

He also has concerns that in AI and robotics the bulk of intellectual capital lies with the Silicon Valley masters of the universe rather than universities or government-funded labs.

“There are some very powerful and rich people who want to do enormous research in this area,” he said. “Some of them might want to come out with things that are very beneficial, but it’s probably outside of regulation and so, yes, I think society as a whole needs to be more engaged.”

Ishiguros father, an oceanographer originally based in Nagasaki, moved with the family to Guildford, Surrey, to work at the National Institute of Oceanography in 1957, when Ishiguro was five.

Dr Shizuo Ishiguro with his electronic analogue machine, which converted meteorological and ocean data into electrical signals on a series of wire meshes. This allowed the height of storm surges, and where and when they would make coastal impact, to be predicted. Photograph: Image courtesy of NOC Archive.

“Despite the two countries having been at war just a decade previously, the family were made welcome,” he said. “The British people of that era had a very sophisticated sense of the international community because they had come through the war,” he said. “They knew the difference between serious things and less serious things, [having] lived through a period when they thought they were going to be under Nazi occupation.”

Ishiguro contrasts this with the anti-immigrant rhetoric that dominated the Brexit and US election campaigns.

“We have become much more multi-cultural and much more cosmopolitan in many ways, but the attitude to, say, the refugee crisis, I think is quite different to what I remember from the Britain I grew up in,” he said.

His father’s electronic analogue machine, which is the size of a large wardrobe, converted meteorological and ocean data (wind speed, tidal motion, water depth and so on) into electrical signals on a series of wire meshes. This allowed the height of storm surges, and where and when they would make coastal impact, to be predicted. The system was originally developed to help Japanese fishing fleets, but modified to be applied to the North Sea, where floods following a storm surge in 1953 led to more than 2,000 deaths.

Ultimately, the machine was superseded by digital computers, but the scientist continued to perfect his creation in the family garden shed in Guildford until his death in 2007; a fact Ishiguro describes as entirely unsurprising.

Despite having taken a different career path, Ishiguro inherited an obsessive attitude towards work from his father, he said, recalling him mulling over equations every evening while watching American thrillers on television.

“Looking back now I can see that the whole approach to his work is quite like my approach to my work as a writer,” he said. “He didn’t think of it as a job at all. It was something he obsessively thought about the whole time. That was my model.”

Mathematics: The Winton Gallery, designed by Zaha Hadid Architects, opens on 8 December. It spans 400 years of mathematics, focusing on ideas and objects that have influenced everyday lives.

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Meet the drones that will be delivering blood and medicine to hospitals in Rwanda

Where the roads don’t go, the skies do. Starting later this year, hospitals and clinics in western Rwanda that are difficult to reach on the ground will be able to receive important medical supplies via delivery drones.

Zipline, an American startup company, recently signed an agreement with the Government of Rwanda to provide all last mile blood deliveries in the country. Those potentially lifesaving packages will be airlifted to their destination by Zip, the company’s high-speed, autonomous aircraft.

According to Zipline CEO Keller Rinaudo, the fixed-wing fliers weigh in at approximately 20 pounds and capable of carrying several additional pounds of cargo while covering nearly 75 miles round trip on a single charge. Zip lives up to its name, traveling at around 180 miles an hour to reach its destination, which is dictated by an onboard SIM card. The crafts can even stay airborne through elements like strong winds and heavy rain.

“We deploy these vehicles in modified shipping containers that we call Nests, which are set up next to existing medical warehouses in the countries we operate in,” Rinaudo explained. Nests contain between 10 and 15 Zips, each of which will perform between 30 and 60 deliveries per day to 22 transfusing facilities located in the Western half of Rwanda.

“Zips takeoff and land at the Nest, and make deliveries by descending close to the ground and air dropping the package to a designated spot at a health center which we call a mailbox,” Rinaudo said.

The Zips will be a welcome addition to the country’s infrastructure. The World Health Organization (WHO) estimates that one-third of the world’s population lacks access to essential medicine and nowhere is that impact felt more than in Africa, where the figure can skyrocket to as high as half the citizenry.

The numbers are even worse for women and children, who too often pay with their lives for a lack of access to basic care. Nearly three million children under age five die every year from lack of access to basic medical needs, while 150,000 pregnancy-related deaths could be avoided each year if mothers could reliably receive safe blood. Africa in particular suffers from the the highest rate in the world of maternal death due to postpartum hemorrhaging.

“Increasing access to lifesaving blood transfusions is critically important for women across the continent,” Rinaudo said.

While women will be considerable beneficiaries of the improved method of delivery, blood transfusions are vitally important to every part of the population. Rinaudo explained that 50 percent of all blood transfusions in the country are for primary pulmonary hypertension and 30 percent are for children with anemia from malaria.

Rinaudo’s plan to use drones to overcome infrastructure challenges for medical deliveries came during a visit to the Ifakara Health Institute in Tanzania in 2014. At the institute, which is one of Africa’s most prominent health research organizations, he spoke with a graduate student who had built a mobile alert system for health workers to text emergency requests for medicine and vaccines.

“Health workers had made thousands of emergency requests, that were not possible before thanks to this system,” he said. “Unfortunately, there was no way for the government to fulfill these requests. We were looking at a database of death with thousands of names, addresses, ages, phone numbers.”

Zipline is designed to be the other half of the system, the fulfillment center that will receive those emergency requests and be able to respond by delivering the necessary supplies with unprecedented speed.

In a nation like Rwanda, where the technology infrastructure is considerably less developed than in other parts of the world, it can be hard to imagine such a system operating. But, according to Rinaudo, it’s actually easier to launch a program like Zipline in a developing country than in a place like the United States.

“In places where the medical needs are extremely urgent, and the airspace is less complex, there is more willingness to innovate,” he explained. “Rwanda has leapfrogged bigger and more technologically advanced countries like the United States and now leads the world in UAV innovation.”

The prospects for autonomous delivery drones are still muddled in the United States, where regulatory bodies and commercial interests are competing to define the rules for such services and attempting to cut up airspacethough the products being delivered by the unmanned aircraft would, in most cases, be bringing items that are superfluous rather than essential.

July 2016 marks the date of liftoff for the Zipline system. According to Rinaudo, once the company has fully rolled out its service to the Eastern portion of the country in 2017, Zips deploying from the Nests will be able to fulfill country-wide delivery requests for essential and lifesaving medicines in 30 minutesor less for nearly all ofRwanda’s 11 million citizens.

“This system will save thousands of lives and dramatically increase the standard of care and access to medical products for millions of Rwandan citizens,” Rinaudo said.

Rwanda will be the biggest test case for Zipline, but it won’t be the only one; the company will be expanding its services to other developing countries later this year as well.

H/T Engadget

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Cool Artificial Intelligence images

Check out these Artificial Intelligence images:

artificial intelligence
Artificial Intelligence
Image by miuenski
Jun 29, 2009
Highest position: 90 on Monday, August 3, 2009

Hitachi with Artificial Intelligence
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Image by deltaMike
At state surplus. Man, Artificial Intelligence huh?

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