Daily Archives: February 15, 2017

Vendors at this week's RSA cybersecurity show in San Francisco are pushing artificial intelligence and machine learningas the new wayto detect the latest threats, but RSA CTO Zulfikar Ramzan is giving visitors a reality check.

"I think it (the technology) moves the needle," he said on Wednesday. "The real open question to me is how much has that needle actually moved in practice?"

It's not as much as vendors claim, Ramzan warned, but for customers it won't be easy cutting through the hype and marketing. The reality is that a lot of the technology now being pushedisnt necessarily new.

In particular, he was talking about machine learning, a subfield in A.I. thats become a popular marketing term in cybersecurity. In practice, it essentially involves building algorithms to spot bad computer behavior from good.

RSA CTO Zulfikar Ramzan speaking at RSA 2017 in February.

However, Ramzan pointed out that machine learning in cybersecurity has been around for well over a decade. For instance, email spam filters, antivirus software and online fraud detection are all based on this technique of detecting the bad from good.

Certainly, machine learning has advanced over the years and it can be particularly useful at spotting certain attacks, like those that dont use malware, he said. But the spotlight on A.I. technologies also has to deal with marketing and building up hype.

Now all of a sudden, were seeing this resurgence of people using the how as a marketing push, he said, after his speech.

The result has created a lemons market, where clients might have trouble distinguishing between useful security products. Not all are equal in effectiveness, Ramzan claimed. For example, some products may generate too many false positives or fail to detect the newest attacks from hackers.

Theres no doubt you can catch some things that you couldnt catch with these techniques, he said. But theres a disparity between what a vendor will say and what it actually does.

Nevertheless, A.I. technologies will still benefit the cybersecurity industry, especially in the area of data analysis, other vendors say.

Right now, its an issue of volume. Theres just not enough people to do the work, said Mike Buratowski, a senior vice president at Fidelis Cybersecurity. Thats where an A.I. can come in. It can crunch so much data, and present it to somebody.

One example of that is IBM's latest offering. On Wednesday, the companyannouncedthat its Watson supercomputer can now help clients respond to security threats.

Within 15 minutes, Watson can come up with a security analysis to a reported cyber threat, when for a human it might have taken a week, IBM claimed.

Recorded Future is another security firm thats been using machine learning to offer intelligence to analysts and companies about the latest cybercriminal activities. The companys technology works by essentially scanning the internet, including black market forums, to pinpoint potential threats.

That might include a hacker trying to sell software exploits or stolen data, said AndreiBarysevich, director of advanced collection at the company.

When you cover almost a million sources and you only have 8 hours a day, to find that needle in the hay stack, you have to have some help from artificial intelligence, he said.

The RSA 2017 show floor.

Customers attending this weeks RSA show may be overwhelmed with the marketing around machine-learning, but itll only be a matter time, before the shoddier products are weeded out, Barysevich said.

We have hundreds of vendors here, from all over the country. But among them, there are five or ten that have a superior product, he said. "Eventually, the market will identify the best of the best.

Read more:

AI faces hype, skepticism at RSA cybersecurity show - PCWorld

As the development of artificial intelligence continues at breakneck speed, questions about whether we understand what we are getting ourselves into persist. One fear is that increasingly intelligent robots will take all our jobs. Another fear is that we will create a world where a superintelligence will one day decide that it has no need for humans. This fear is well-explored in popular culture, through books and films like the Terminator series.

Another possibility is maybe the one that makes the most sense - since humans are the ones creating them, the machines and machine intelligences are likely to behave just like humans. For better or worse. DeepMind, Googles cutting-edge AI company, has shown just that.

The accomplishments of the DeepMind program so far include learning from its memory, mimicking human voices, writing music, and beating the best Go player in the world.

Recently, the DeepMind team ran a series of tests to investigate how the AI would respond when faced with certain social dilemmas. In particular, they wanted to find out whether the AI is more likely to cooperate or compete.

One of the tests involved 40 million instances of playing the computer game Gathering, during which DeepMind showed how far its willing to go to get what it wants. The game was chosen because it encapsulates aspects of the classic Prisoners Dilemma from game theory.

Pitting AI-controlled characters (called agents) against each other, DeepMind had them compete to gather the most virtual apples. Once the amount of available apples got low, the AI agents started to display "highly aggressive" tactics, employing laser beams to knock each other out. They would also steal the opponents apples.

Heres how one of those games played out:

The DeepMind AI agents are in blue and red. The apples are green, while the laser beams are yellow.

The DeepMind team described their test in a blog postthis way:

We let the agents play this game many thousands of times and let them learn how to behave rationally using deep multi-agent reinforcement learning. Rather naturally, when there are enough apples in the environment, the agents learn to peacefully coexist and collect as many apples as they can. However, as the number of apples is reduced, the agents learn that it may be better for them to tag the other agent to give themselves time on their own to collect the scarce apples.

Interestingly, what appears to have happened is that the AI systems began to develop some forms of human behavior.

This model... shows that some aspects of human-like behaviour emerge as a product of the environment and learning. Less aggressive policies emerge from learning in relatively abundant environments with less possibility for costly action.The greed motivation reflects the temptation to take out a rival and collect all the apples oneself, said Joel Z. Leibo from the DeepMind team to Wired.

Besides the fruit gathering, the AI was also tested via a Wolfpack hunting game. In it, two AI characters in the form of wolves chased a third AI agent - the prey. Here the researchers wanted to see if the AI characters would choose to cooperate to get the prey because they were rewarded for appearing near the prey together when it was being captured.

"The idea is that the prey is dangerous - a lone wolf can overcome it, but is at risk of losing the carcass to scavengers. However, when the two wolves capture the prey together, they can better protect the carcass from scavengers, and hence receive a higher reward, wrote the researchers in their paper.

Indeed, the incentivized cooperation strategy won out in this instance, with the AI choosing to work together.

This is how that test panned out:

The wolves are red, chasing the blue dot (prey), while avoiding grey obstacles.

If you are thinking Skynet is here, perhaps the silver lining is that the second test shows how AIs self-interest can include cooperation rather than the all-out competitiveness of the first test. Unless, of course, its cooperation to hunt down humans.

Here's a chart showing the results of the game tests that shows a clear increase in aggression during "Gathering":

Movies aside, the researchers are working to figure out how AI can eventually control complex multi-agent systems such as the economy, traffic systems, or the ecological health of our planet all of which depend on our continued cooperation.

One nearby AI implementation where this could be relevant - self-driving cars which will have to choose safest routes, while keeping the objectives of all the parties involved under consideration.

The warning from the tests is that if the objectives are not balanced out in the programming, the AI might act selfishly, probably not for everyones benefit.

Whats next for the DeepMind team? Joel Leibo wants the AI to go deeper into the motivations behind decision-making:

Going forward it would be interesting to equip agents with the ability to reason about other agents beliefs and goals, said Leibo to Bloomberg.

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Google's AI Learns Betrayal and "Aggressive" Actions Pay Off - Big Think

The booming growth of machine learning and artificial intelligence (AI), like most transformational technologies, is both exciting and scary. Its exciting to consider all the ways our lives may improve, from managing our calendars to making medical diagnoses, but its scary to consider the social and personal implications and particularly the implications for our careers. As machine learning continues to grow, we all need to develop new skills in order to differentiate ourselves. But which ones?

Its long been known that AI and automation/robotics will change markets and workforces. Self-driving cars will force over three thousand truck drivers to seek new forms of employment, and robotic production lines like Teslas will continue to eat away at manufacturing jobs, which are currently at 12 million and falling. But this is just the beginning of the disruption. As AI improves, which is happening quickly, a much broader set of thinking rather than doing jobs will be affected. Were talking about jobs, that, until the last few years, we couldnt imagine being done without the participation of an actual, trained human being. Jobs like teacher, doctor, financial advisor, stockbroker, marketer, and business consultant.

There are just a lot of things that machines can do better than human beings, and we shouldnt be too proud to admit it. Many skilled jobs follow the same general workflow:

We can look at any number of occupations to see that this holds true. Doctors perform tests, analyze the results, interpret the results to make a diagnosis, plan a course of treatment, and then work with the patient to make this treatment plan a reality.

Financial advisers gather and analyze data about their clients and potential investment vehicles, interpret the implications given a variety of factors such as risk tolerance, recommend an investment strategy, and help their clients carry this strategy out over time.

Business consultants do much of the same, but diagnose and solve business problems.

These highly skilled workers can command high rates because of three capabilities. Their abilities to go through the early rote tasks quickly and accurately; their experiences and judgment in determining a course of action; and their savviness for helping clients navigate that course. AI and machine learning will quickly surpass our abilities on the first two capabilitiesand this will shift the skillset required for any worker wishing to stay in these careers as they are transformed by artificial intelligence.

Its easy to see the role of automated systems in data gathering and analysis. Weve accepted that machines can do these types of tasks efficiently. However, their potential goes much further. Human beings are limited, and often biased. Doctors will never be able to keep up with every new publication in their areas of expertise. Instead,they must rely ona small number of personal experiences rather than the complete knowledge in their field. Consultants, too, can only experience so many company transformations over their careers. From a narrow set of experiences, they form their preferences, expectations, and insights. Human beings cant just plug in more servers when we reach our limits processing new information. Instead, we must rely on our own, often biased, preferences, habits, and rules of thumb.

Some people may say that we will never trust machines with important decisions such as the management of our health and money, but this is twentieth century thinking. But a new generation is engaging withsmart machines that they trust, and oftenprefer. Further, its hard for anyone to argue with results. IBMs Watson is already cracking medical cases that stump doctors, and investors are fleeing expensive, actively managed funds for better-performing passive ones. The value of some of our most prized career paths is already being eroded.

Those that want to stay relevant in their professions will need to focus on skills and capabilities that artificial intelligence has trouble replicating understanding, motivating, and interacting with human beings. A smart machine might be able to diagnose an illness and even recommend treatment better than a doctor. It takes a person, however, to sit with a patient, understand their life situation (finances, family, quality of life, etc.), and help determine what treatment plan is optimal.

Similarly, a smart machine may be able to diagnose complex business problems and recommend actions to improve an organization. A human being, however, is still best suited to jobs like spurring the leadership team to action, avoiding political hot buttons, and identifying savvy individuals to lead change.

Its these human capabilities that will become more and more prized over the next decade. Skills like persuasion, social understanding, and empathy are going to become differentiators as artificial intelligence and machine learning take over our other tasks. Unfortunately, these human-oriented skills have generally been viewed as second priority in terms of training and education. Weve all experienced the doctor, financial planner, or consultant who is more focused on his or her reports and data than on our unique situations and desires.

For better or worse, these skills will become essential to anyone who wants to stay relevant in their field as automated systems proliferate. We have three recommendations:

What you have to offer what you can do better than any smart machine is relate to the people around you. Begin to nurture and invest in these abilities the same way that you have the more technical parts of your career. If you can be an outstanding motivator, manager, or listener, then you will still have a part to play as technology changes your industry.

Originally posted here:

The Rise of AI Makes Emotional Intelligence More Important - Harvard Business Review

Twenty-two years ago, researchers first reported that adolescents with autism spectrum disorder had increased brain volume. During the intervening years, studies of younger and younger children showed that this brain overgrowth occurs inchildhood.

Now, a team at the University of North Carolina, Chapel Hill, has detected brain growth changes linked to autism in children as young as 6 months old. And it piqued our interest because a deep-learning algorithm was able to use that data to predict whether a child at high-risk of autism would be diagnosed with the disorder at 24 months.

The algorithm correctly predicted the eventualdiagnosis in high-risk children with 81 percentaccuracy and 88 percentsensitivity. Thats pretty damn good compared withbehavioral questionnaires, which yield informationthat leads to early autism diagnoses(at around 12 months old) that are just 50 percent accurate.

This is outperforming those kinds of measures, and doing it at a younger age, says senior author Heather Hazlett, a psychologist and brain development researcher at UNC.

As part of the Infant Brain Imaging Study, a U.S. National Institues of Healthfunded study of early brain development in autism, the research team enrolled 106 infants withan older sibling who had been givenan autism diagnosis, and 42 infants with no family history of autism. They scanned each childs brainno easy feat with an infantat 6-, 12-, and 24 months.

The researchers saw no change in any of the babies overall brain growth between 6- and12-month mark.But there was a significant increase in the brain surface area of the high-risk children who were later diagnosed with autism. That increase in surface area was linked to brain volume growth that occurred between ages 12 and 24 months. In other words, in autism, the developing brain first appears to expand in surface area by 12 months, then in overall volume by 24 months.

The team also performed behavioral evaluations on the children at 24 months, when they were old enough to begin to exhibit the hallmark behaviors of autism, such as lack of social interest, delayed language, and repetitive body movements. The researchers note that the greater thebrain overgrowth, the more severe a childs autistic symptoms tended to be.

Though thenew findings confirmed that brain changes associated with autism occur very early in life,the researchers did not stop there. In collaboration with computer scientists at UNC and the College of Charleston, the team built an algorithm, trained it with the brain scans, and tested whether it could use these early brain changes to predict which children would later be diagnosed with autism.

It worked well. Using just three variablesbrain surface area, brain volume, and gender (boys are more likely to have autism than girls)the algorithm identified up eight out of 10 kids with autism. Thats pretty good, and a lot better than some behavioral tools, says Hazlett.

To train the algorithm, the team initially used halfthe data for training and the other half for testingthe cleanest possible analysis, according to team memberMartin Styner, co-director of the Neuro Image Analysis and Research Lab at UNC. But at the request of reviewers, they subsequently performed a more standard 10-fold analysis, in which data is subdivided into 10 equal parts. Machine learning is then done 10 times, each time with 9 folds used for training and the 10th saved for testing. In the end, the final program gathers together the testing only results from all 10 rounds to use in its predictions.

Happily, the two types of analysesthe initial 50/50 and the final 10-foldshowed virtually the same results, says Styner. And the team was pleased with the prediction accuracy. We do expect roughly the same prediction accuracy when more subjects are added, said co-author Brent Munsell, an assistant professor at College of Charleston, in an email to IEEE. In general, over the last several years, deep learning approached that have been applied to image data have proved to be very accurate, says Munsell.

But, like our other recent stories on AI out-performing medical professionals, the results need to be replicated before well see a computer-detected biomarker for autism. That will take some time, because it is difficult and expensive to get brain scans of young children for replication tests, emphasizes Hazlett.

And such an expensive diagnostic test will not necessarily be appropriate for all kids, she adds. Its not something I can imagine being clinically useful for every baby being born. But if a child were found to have some risk for autism through a genetic test or other marker, imaging could help identify brain changes that put them at greater risk, she notes.

IEEE Spectrums biomedical blog, featuring the wearable sensors, big data analytics, and implanted devices that enable new ventures in personalized medicine.

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AI Predicts Autism From Infant Brain Scans - IEEE Spectrum

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how its being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

The Difference between AI, Machine Learning and Deep Learning

With all the quickly evolving nomenclature in the industry today, its important to be able to differentiate between AI, machine learning and deep learning. The simplest way to think of their relationship is to visualize them as a concentric model as depicted in the figure below. Here, AI the idea that came firsthas the largest area, followed by machine learningwhich blossomed later and is shown as a subset of AI. Finally deep learningwhich is driving todays AI explosion fits inside both.

AI has been part of our thoughts and slowly evolving in academic research labs since a group of computer scientists first defined the term at the Dartmouth Conferences in 1956 and provided the genesis of the field of AI. In the long decades since, AI has alternately been heralded as an all-encompassing holy grail, and thrown on technologys bit bucket as a mad conception of overactive academic imaginations. Candidly, until around 2012, it was a bit of both.

Over the past few years, especially since 2015, AI has exploded on the scene. Much of that enthusiasm has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe including images, video, text, transactions, geospatial data, etc.

On the same trajectory, deep learning has enabled many practical applications of machine learning and by extension the overall field of AI. Deep learning breaks down tasks in ways that make all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon. AI is the foundation for the present and the future.

DownloadtheinsideBIGDATA Guide to Deep Learning & Artificial Intelligence, courtesy of NVIDIA.

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Defining AI, Machine Learning, and Deep Learning - insideHPC

Facebook (FB) is making the right choice in targeting video as a top priority, allowing it more time to catch up in the field of artificial intelligence, where it lags, says Richard Windsor, an analyst at Edison Investment Research.

Windsor, in an email note to clients, said that of three key areas of digital consumption gaming, search and video placing video first is the right move by Facebook

"This is because Facebook already has a lot of traction in this space and also because it is the least demanding in terms of requiring intelligent automation," he wrote.

Microsoft (MSFT), Alphabet (GOOGL) and Amazon (AMZN) have been investing heavily in artificial intelligence. Among the areas of AI applications are digital assistants for the home. Amazon has a big lead with its Echo device, while Alphabet has Google Home.

Microsoft has deep roots in artificial intelligence with its Cortana platform, available on mobile devices and PCs. This is also true of Apple (AAPL) with its Siri platform. While Siri is available on the iPhone and iPad, Apple has not yet signaled when it might introduce an in-home digital assistant.

IBD'S TAKE:Facebook and Amazon are two of the four closely watched FANG stocks, along with Netflix and Google's parent company, Alphabet. Internet stocks are expected tooutperform the broader marketin 2017.

Facebook on Tuesday made several announcements related to video, which could pose a threat to the YouTube business of Alphabet. The announcements included a Facebook app designed for watching Facebook videos on a television. The app will "roll out soon" to app stores for Apple TV, Amazon Fire TV and Samsung Smart TV, with more platforms to come," Facebook said in a blog post.

Facebook, in October, initially rolled out the ability for users to stream videos from Facebook to your TV. The announcement Tuesday expands this capability.

"With the launch of a TV app being just the latest move Facebook has made in video, it is increasingly clear that media consumption is Facebook's No. 1 priority for 2017," Windsor wrote.

While the Facebook TV app will initially be available on Amazon TV and Apple TV, Windsor says it will quickly spread to Microsoft Xbox, Sony (SNE) PlayStation and other streaming TV devices. He does not expect to find the app on Chromecast by Alphabet, "as Facebook's video aspirations are clearly a challenge to YouTube," he wrote.

The focus on video gives Facebook more time to develop its artificial intelligence technology.

"Wecontinue to see Facebook as the laggard in AI," Windsor wrote. "Targeting video is sensible as it gives it more time to improve its AI before having to apply it to more difficult tasks."

The fact that video is a fast-growing, but maturing, medium for digital advertising also means that the time to really address it is now, Windsor wrote. "The app on the TV is just the beginning and we would not be surprised to see this being followed up with premium content taking it into the realm of Netflix (NFLX), Hulu, YouTube and Amazon Prime."

Facebook, when it reported fourth-quarter earnings on Feb. 2 that blew past expectations, continued to benefit from a surge in advertiser demand, driven by newer ad formats such as video.

On the earnings conference call CEO Mark Zuckerberg said, "I see video as a megatrend on the same order as mobile. That's why we're going to keep putting video first across our family of apps and making it easier for people to capture and share video in new ways."

Facebook stock was down 0.3% to 133.44 onthe stock market today, and still in a buy zone from a breakout last month at 129.37, out of a cup-with-handle base.

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Facebook Push Into Video Allows Time To Catch Up On AI Applications - Investor's Business Daily

A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

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A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

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A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

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Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty Images

Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty Images

The giant human-like robot bears a striking resemblance to the military robots starring in the movie 'Avatar' and is claimed as a world first by its creators from a South Korean robotic company

Jung Yeon-Je/AFP/Getty Images

Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty Images

Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi

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Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session

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A test line of a new energy suspension railway resembling the giant panda is seen in Chengdu, Sichuan Province, China

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A test line of a new energy suspension railway, resembling a giant panda, is seen in Chengdu, Sichuan Province, China

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A concept car by Trumpchi from GAC Group is shown at the International Automobile Exhibition in Guangzhou, China

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A Mirai fuel cell vehicle by Toyota is displayed at the International Automobile Exhibition in Guangzhou, China

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A visitor tries a Nissan VR experience at the International Automobile Exhibition in Guangzhou, China

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A man looks at an exhibit entitled 'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London

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A new Israeli Da-Vinci unmanned aerial vehicle manufactured by Elbit Systems is displayed during the 4th International conference on Home Land Security and Cyber in the Israeli coastal city of Tel Aviv

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Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S

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The Jaguar I-PACE Concept car is the start of a new era for Jaguar. This is a production preview of the Jaguar I-PACE, which will be revealed next year and on the road in 2018

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Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03' and other robots during a demonstration in Tokyo, Japan

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Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03'

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Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot 'TRX03' performs during its unveiling in Tokyo, Japan

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Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China

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Singulato Motors' concept car Tigercar P0

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The interior of Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China

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A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China

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Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo. The Connected company is a part of seven Toyota in-house companies that was created in April 2016

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A Toyota Motors employee demonstrates a smartphone app with the company's pocket plug-in hybrid (PHV) service on the cockpit of the latest Prius hybrid vehicle during Toyota's "connected strategy" press briefing in Tokyo

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An exhibitor charges the battery cells of AnyWalker, an ultra-mobile chasis robot which is able to move in any kind of environment during Singapore International Robo Expo

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A robot with a touch-screen information apps stroll down the pavillon at the Singapore International Robo Expo

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An exhibitor demonstrates the AnyWalker, an ultra-mobile chasis robot which is able to move in any kind of environment during Singapore International Robo Expo

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Robotic fishes swim in a water glass tank displayed at the Korea pavillon during Singapore International Robo Expo

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An employee shows a Samsung Electronics' Gear S3 Classic during Korea Electronics Show 2016 in Seoul, South Korea

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Visitors experience Samsung Electronics' Gear VR during the Korea Electronics Grand Fair at an exhibition hall in Seoul, South Korea

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Amy Rimmer, Research Engineer at Jaguar Land Rover, demonstrates the car manufacturer's Advanced Highway Assist in a Range Rover, which drives the vehicle, overtakes and can detect vehicles in the blind spot, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Ford EEBL Emergency Electronic Brake Lights is demonstrated during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Chris Burbridge, Autonomous Driving Software Engineer for Tata Motors European Technical Centre, demonstrates the car manufacturer's GLOSA V2X functionality, which is connected to the traffic lights and shares information with the driver, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire

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Full-scale model of 'Kibo' on display at the Space Dome exhibition hall of the Japan Aerospace Exploration Agency (JAXA) Tsukuba Space Center, in Tsukuba, north-east of Tokyo, Japan

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Miniatures on display at the Space Dome exhibition hall of the Japan Aerospace Exploration Agency (JAXA) Tsukuba Space Center, in Tsukuba, north-east of Tokyo, Japan. In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module 'Kibo' of the International Space Station (ISS) and develop launch vehicles

EPA

The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight. At this biennial event, the participating companies exhibit their latest service robotic technologies and components

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The robot developed by Seed Solutions sings and dances to music during the Japan Robot Week 2016 at Tokyo Big Sight

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Government and industry are working together on a robot-like autopilot system that could eliminate the need for a second human pilot in the cockpit

AP

Aurora Flight Sciences' technicians work on an Aircrew Labor In-Cockpit Automantion System (ALIAS) device in the firm's Centaur aircraft at Manassas Airport in Manassas, Va.

AP

Stefan Schwart and Udo Klingenberg preparing a self-built flight simulator to land at Hong Kong airport, from Rostock, Germany

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An elated customer at the launch of PlayStation VR at the GAME Digital Westfield White City midnight launch.

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Elon Musk: Humans must become cyborgs to avoid AI domination - The Independent