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It can read lips and create new food recipes. It can win at chess, Jeopardy and the game Go. Every major technology company appears to be integrating it into how they organize and operate their business. And it seems like just about every new app in existence claims its software uses some sort of machine learning to make life even better.
Artificial intelligence is splashed across headlines like never before. The AI revolution is here, and the most obvious question to ask as 2016 draws to an end is: what’s next?
We recently asked James Hendler this question. Hendler is director of the Rensselaer Institute for Data Exploration and Applications and one of the developers of the semantic web. He recently co-wrote, with Alice M. Mulvehill, the book Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity.
The book is less about predictions and more about setting expectations about what AI can and can’t do. The problem, as Hendler sees it, is that many people view AI with Terminator trepidation or as a utopian dream, while completely taking humanity out of the equation.
“People want to paint this technology in black and white,” he explains. “It needs humans in the loop, and humans are better at dealing with the grays.”
To borrow a slightly used political slogan: we—humans and AI—are stronger together. That’s Hendler’s a priori when discussing the future of artificial intelligence.
Packaging AI for mass programming
“I think the thing that excites me short-term is how much of AI technology [is being] made accessible at a much simpler level for programmers to use. It’s no longer a specialist thing,” Hendler says.
A class he is currently teaching on AI cognitive computing illustrates this point. Undergraduates are doing projects, like creating a chatbot able to answer questions about the Harry Potter universe, in a matter of weeks. A few years ago, such a feat would have been fodder for a PhD thesis.
What’s changed?
It’s no longer necessary to build deep learning, computer vision or natural language components from scratch. Just download an open source package and integrate it into your system with some tweaking. It’s a bit like playing with WordPress, though Hendler prefers to talk about the nascent days of the internet. In the early 1990s, with some basic understanding of HTML, it was possible to build a website thanks to a sort of pre-packaged code that could be installed on a machine.
“AI has been packaged in a usable way,” Hendler says. “[It’s] more like putting the pieces together and finding what works than doing the basic research into what those components are, at least for the more applied side to the technology space.”
Opening the doors to innovation
In the short term, Hendler says, that opens up the game to players of all sizes.
“We’re going to see a huge amount of innovation in small companies using existing techniques for deep learning, vision and language tasks,” he says. “The heavyweights—Microsoft, Google, Facebook—will invest heavily in the technology they do but in new directions.”
Meanwhile, academia and government will continue to play roles in the evolution of AI-related technologies. Hendler uses the example of autonomous vehicles, first developed by universities like Stanford to win the DARPA Grand Challenge. Google then further matured the technology. Now it seems every car company on the planet is working to put robotic cars on the road.
While there is still a need to develop new AI technologies to solve problems, Hendler says the near-term focus will be on the sorts of business cases that can be made with existing tools.
“I think that kind of innovation is where you see entrepreneurs and startups starting to focus now. I think we’re going to see a tremendous amount of that,” Hendler says.
Solving developed and developing world problems
And what might the casual technology user see from AI in 2017 and beyond? In this case, more may mean less, as technology slips seamlessly into the background.
“It’s not going to be as obvious as you buy something and the whole world is different,” Hendler says.

Take Siri, Apple’s ubiquitous virtual assistant. Siri’s competence at performing increasingly complex tasks is constantly improving, but it still (and often) defaults back to a web search for the answer. One day not too far into the future, one could imagine asking Siri or one of her counterparts a question like, “Show me a photo of my kids from lunch today,” and the machine quickly and correctly pulling out the results.
In fact, we see some of the startups Hendler mentions already on the cusp of such achievements. A company called Snips uses an AI technique called "context aware" to build a sort of memory, almost an alter ego, on a user’s mobile device, by sorting through data like contacts, emails, calendars, photos and so on. It learns what is important in the user’s life over time, serving as the single portal to all the apps and information stored on the device.
“It’s about using this artificial intelligence to make technology disappear in a way that you can just go about your day and not care about it anymore,” says Rand Hindi, CEO and founder of Snips, during a TEDx talk in 2015.
Of course, these are developed world problems—making technology disappear. Hendler is optimistic that projects to improve conditions in developing countries will involve the appearance of AI in the near future. In particular, he and others are working with IBM to bring literacy to one billion people in the next five years.
“You’re talking about being able to significantly change the lives of huge amounts of people, especially in countries where literacy rates are currently low,” he says. “That’s where those people will see technology suddenly come into their lives in a way it never has before.”
Education is key
Upheavals and massive disruptions—both good and bad—are ahead in a world increasingly powered by artificial intelligence and related technologies.
On one side of the argument are people like the 1.8 million truck drivers who could feasibly be put out of work in less than a generation by self-driving vehicles. On the flip side are the potential savings in industries like medicine, where AI is already being employed on a large scale with IBM’s Watson, the poster child—computer—for those high-tech services. Consider that health care accounts for 17.5 percent of US GDP, according to the Centers for Medicare and Medicaid Services.
Hendler says government needs to be involved to help manage these changes without setting up roadblocks to innovation. Education will be key to the AI revolution, he maintains, so people will understand where computers excel and where they struggle.
“That’s where we need people to be smarter, and for technical people to help policy makers to understand those differences and where they lie,” he says. “It’s understanding those differences that will be so important.”
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ARTIFICIAL INTELLIGENCE Big Tech's AI Predictions for 2017 Lolita Taub | The Huffington Post "For the final Cognitive Business post of the year, I asked artificial intelligence centric Fortune 500 leaders for their 2017 enterprise AI predictions. Microsoft, IBM, Baidu, NVIDIA, Qualcomm, GE, SAS, and Oracle responded. What they had to say is exciting…" CYBERSECURITY Artificial […] Continue reading →

NEWS RELEASE
Technological revolution means robots no longer are the song of the future. The Governor of the Bank of England predicts today that up to half of British workforce face redundancy in the imminent ‘second machine age’. No wonder, the research of multi-robot systems generates serious buzz both for promising (albeit at times scary) results and for their application prospects in the real world.
According to a leading American roboticist Ken Goldberg, people are fascinated with robots because robots reflect people. And hardly anything defines humans better indeed than their ability to communicate. Recent progress in programming, language processing and machine learning allows robot to display more and more complex communication abilities. Underlying these advances are solutions to significant problems of different origins, including mechanical design, sensing technologies, maintenance and power sources. With improved efficiency and the elimination of a single point of failure, multiple robots outperform single robots in domains that require greater capability and knowledge and can duly interact with each other, sharing information and executing tasks.
But how the multi-robot system is supposed to handle increasingly complex and precise tasks? One fairly obvious answer to this question lies within the implementation of an innovative algorithm, which would expand communicational capabilities for multi-robot collaborative task. For the system to work, it needs to be less prone to error, fast, and reliable comparing to the any other, including, human approaches.
A ground-breaking research conducted by the Moscow Power Engineering Institute, just published in Paladyn, Journal of Behavioral Robotics, reveals new findings in the emerging field of a multi-robot cooperative system design from its experimental side. In the article, – Vladimir Alexandrov, Konstantin Kirik and Alexander Kobrin propose the implementation of a hardware-based modeling system for multi-robot collaborative tasks focusing on the development and implementation phase of an algorithm/system creation. Their approach results in speeding up implementation iterations, ultimately leading to enhanced communicational capabilities of research objects. The Muscovite researchers concentrate not only on architecture and implementation of the research robot, but also on communication system with parallel radio and infrared bidirectional data exchange, and on strategies of implementation of simulation tool chain.
Due to significant progress made in the development of general problems concerning single-robot control and basic multi-robot behavior, many researchers shifted their focus to a study of multi-robot coordination and deep cooperation behavior. Robots themselves shall be capable to perform all necessary algorithmic steps. Therefore, using tightly coupled modelling hardware and simulation tool chain, that transfers the full implementation of algorithms onto the hardware, can bring certain benefits.
“The new methods are attractive, as they integrate different new ideas concerning the algorithm design process, event-driven robot software design, and an autonomous mobile research robot equipped with an advanced sensor subsystem”, says Professor Radu-Emil Precup, a specialist in development of new control systems and algorithms.
The original article is fully open access and available to read, download and share on De Gruyter Online.
DOI 10.1515/pjbr-2016-0003

About the Journal:Paladyn. Journal of Behavioral Robotics is an open access, peer-reviewed journal that publishes original research on topics broadly related to neuronally and psychologically inspired robots and other behaving autonomous systems.
About De Gruyter Open: De Gruyter Open is a leading publisher of Open Access academic content. Publishing in all major disciplines, De Gruyter Open is home to more than 500 scholarly journals and over 100 books. Formerly known as Versita, the company is part of the De Gruyter Group (www.degruyter.com) and is a member of the Association of Learned and Professional Society Publishers (ALPSP). De Gruyter Open’s book and journal programs have been endorsed by the international research community and some of the world’s top scientists, including Nobel laureates. The company’s mission is to make the very best in academic content freely available to scholars and lay readers alike.
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How industrial robots will shape the work to come
Industry 4.0 is one of the most discussed topic worldwide. All over the world, the race for innovation has become one on the main challenges that nations and companies have to face, working to create new policies able to take into account factors like Big Data, digitization and industrial robots.
According to many, the latter is about to revolutionize every piece of our lives, and is about to transform the world into something where everything is automated and interconnected, where everything is based on digital technologies, and where men and machines work together.
The age of the robot rush
Industrial robots fascinate as much as they worry, mainly due to the fear of a massive job loss. Nobody can really estimate the impact of robot automation and how this will affect global employment. Will humans be replaced in all their usual tasks? Will brand new jobs come out of this robot diffusion?
It’s no secret that robot sales increase exponentially in the last years, with Asia showing an enormous growth in terms of density. Nowadays, sectors like electronics or automotive build most of their working processes on automated systems, as evidenced by this Tesla Motors factory in Detroit where 160 robots work along with 3000 human employees, for instance. No wonder why it’s easy to see these technologies as a menace to global employment!Shall we then be afraid of industrial robots?Actually, there’s more to see under this threatening surface. History shows us how previous industrial revolutions reshaped the way we work and live.
If we take a closer look to the impacts of robotization, we notice that what is happening right now already happened 200 and 150 years ago, during the first two Industrial Revolutions. Although they are now considered as periods of unprecedented progress, their consequences were drastic for workers, as they witnessed they activity evolving towards something new.
That’s exactly what is happening today: we are moving into the unknown. And the unknown is scary.

What to expect from an automated coworker?
Some late trends in robotics present a more collaborative approach between human and robot coworkers – a trend that finds in Baxter and Sawyer its best manifestation. Industrial robots were traditionally perceived as replacements for humans, while collaborative robots stepped into the discussion showing a new way to interact with the machines, as they no longer work for humans but WITH them, making technology not only a tool to forge the world but a source of (still) unpredictable advancements.
It is still hard to predict which sectors (if not all) will rely on automated processes: right now, the range of possibilities is still wide open. In a few words, once the threat of a massive job loss is left behind, an astonishing world of opportunities is there to be explored, creating a workplace where human and robot could potentially cooperate, redefining traditional views of work and employment.

Check out TradeMachines infographic to learn more about it!
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A video of a giant humanoid robot is exciting, but there are questions about its origins. Continue reading →