US Navy Autonomous Swarm Boats

A fleet of U.S. Navy boats approached an enemy vessel like sharks circling their prey. 

Iin this case, part of an exercise conducted by the U.S. Office of Naval Research (ONR), the boats operated without any direct human control: they acted as a robot boat swarm.

The tests on Virginia’s James River this past summer represented the first large-scale military demonstration of a swarm of autonomous boats designed to overwhelm enemies. This capability points to a future where the U.S. Navy and other militaries may deploy underwater, surface, and flying robotic vehicles to defend themselves or attack a hostile force.

“What’s new about the James River test was having five USVs [unmanned surface vessels] operating together with no humans on board,” said Robert Brizzolara, an ONR program manager.

In the test, five robot boats practiced an escort mission that involved protecting a main ship against possible attackers. To command the boats, the Navy use a system called the Control Architecture for Robotic Agent Command and Sensing (CARACaS). The system not only steered the autonomous boats but also coordinated its actions with other vehicles—a larger group of manned and remotely-controlled vessels.

IBM's Liquid-Based Transistors Process Data Like Human Brain

A team of researchers at IBM has given the transistor a major makeover, and it may enable the company to build computers that function more like the way the human brain works.

If it pans out, IBM could use the technology to build chips that are highly efficient and use much less electrical power. That could lead to a revolution in mobile devices, which today are bound by short battery lives and electrical inefficiency. The whole process is not unlike the charged electrical fluids sloshing around in our brains. If the brain can do it, an artificially crafted material might be able to do it too.

The new technology is based on materials called “correlated electron oxides,” which can be combined with an ionic liquid, or a mixture where half of the molecules carry a positive charge and half are negative. When you apply a tiny ionic voltage to the liquid, the charged particles move to opposite sides of the surface of the oxide material. The charge leaves the oxide and goes into the liquid, changing its conductive state from an insulator to a metal, or from something that does not conduct electricity to something that does.

And it maintains its electrical state until another charge is applied. That part of the research is new and is particularly encouraging. IBM believes it can create non-volatile memory, or chips that save data whether electricity is on or off. It can also make logic chips that would use less power than today’s silicon-based semiconductor chips, which are the brains of everything electronic.

“We are using tiny currents of ions of atoms generated by these electrical signals to change the state of matter of this oxide material,” he said. “It is a means to build low-energy, highly efficient devices by turning on and off their conducting state. We turn this material into a metal and maintain it without any need to supply power.”

That latter part is remarkable, as silicon chips require constant voltages to function.

Chips that use electricity have been evolving for decades, with progress marked by Moore’s Law, named after Intel chairman emeritus Gordon Moore, who predicted in 1965 that the number of components on a chip would double every year or so. That prediction has been very accurate, but experts worry that progress will slow as semiconductor technology runs into fundamental limits. IBM is working on new solutions, including traditional semiconductor chips that process data like the human brain does.

Parkin said that by applying a charged ionic liquid electrolyte to a substance, the team has been able to create a stable insulating and conducting state of an oxide material. This discovery has opened a way for making oxide-based transistors and logic gates.

“This is an alternative to a slowdown in Moore’s Law,” Parkin said. “Our inspiration is the brain and how it operates. It is full of liquids and ionic currents. We could build more brain-like devices.”

The picture at right shows a drawing of a nanofluidic circuit, which operates by passing the green ionic fluid through conduits fabricated on top of the orange planar oxide surface. When a voltage is applied to the liquid (the blue part), the yellow balls from the oxide surface in the liquid are metallized. When no voltage is applied, there is no ionic motion and the oxide surface is an insulator, which does not conduct electrons. Circuits can be dynamically formed on the surface of the oxide.

Source: Venture Beat

Amazing AI Robot

Courtesy Of NOVA

You can view the full episode here: "PBS"

The Future Of Super-Intelligent Transport Systems

... the future of transportation will rely on super-intelligence in vehicles, control systems and planning tools. That was the main premise of the Dutch foresight project “Transportation of tomorrow starts today”. Let's go deeper into the super-intelligence of transport systems and find out what it could do to the future of transportation.

Super-intelligence implies that the intelligence of the system as a whole reaches higher levels, because of the integration of intelligence in all the components of the system. Intelligent vehicles, an intelligent infrastructure, intelligent control system, intelligent commuters, intelligent cargo and intelligent enablers of information. Following the Dutch foresigt study, these are the components of an intelligent transportation system.

In super-intelligent vehicles there is no need of a driver. The intelligence makes it really autonomous. Not only drives the vehicle by itself, it also responds to signals from other vehicles or the infrastructure. This is the future that Google car presents us. A partial solution where drivers could interfer, would be steering a car by using only your eyes.EyeDriver does that for you. It can drive autonomous, but can also be steered by gaze control. BrainDriver is a bit of the same concept, but then thinking about a direction is already sufficient. Driverless public transportis already very much mainstream. Think about airport shuttles and metro trains. Passenger airlines without human pilots are also a real possibility, just like the military drones.

An intelligent infrastructure communicates to other aspects of the transport system. LED’s in the road surface providing warnings about incidents, self healing concrete that enhances life span of roads and bridges, bike lanes that converts sunlight into electricity.

Intelligent control systems use real-time information to direct traffic streams and respond to vehicles and everything else in order to find solutions for traffic situations. Smart grids can communicate with vehicles to bring electricity to those cars and bikes which have the biggest need.

When cars, roads and control systems become more intelligent, the human commuter cannot stay behind.Augmented reality, like Google glass, can present alternative routes, when traffic jams are expected. The smart agents of such apps can be customized to know a person’s preferences and respond to their daily habits.

Even cargo can become more intelligent and communicate about its destination. Intelligent cargo enables packages to interact with their surroundings and make context aware decisions. In this way the planning process of cargo freight happens autonomously.

For all these forms of intelligence, processing huge amount of data is crucial. When the computational power of computer processors goes up, their will be endless new opportunities for artificial intelligence. Current ideas for augmented reality and the many other concepts discussed here, are all just waiting for the fast and tiny processors of tomorrow’s smart devices. The smart cities of the future could embrace this new technology and we might see unprecendented smooth and autonomous transportation.

Via: "World Future Society"

Silicon Brains To Oversee Satellites

A beautiful and expensive sight: upwards of €6 million-worth of silicon wafers, crammed with the complex integrated circuits that sit at the heart of each and every ESA mission. Years of meticulous design work went into these tiny brains, empowering satellites with intelligence. 

The image shows a collection of six silicon wafers that contain some 14 different chip designs developed by several European companies during the last eight years with ESA’s financial and technical support. 

Each of these 20 cm-diameter wafers contains between 30 and 80 replicas of each chip, each one carrying up to about 10 million transistors or basic circuit switches.

To save money on the high cost of fabrication, various chips designed by different companies and destined for multiple ESA projects are crammed onto the same silicon wafers, etched into place at specialised semiconductor manufacturing  plants or ‘fabs’, in this case LFoundry (formerly Atmel) in France.

Once manufactured, the chips, still on the wafer, are tested. The wafers are then chopped up. They become ready for use when placed inside protective packages – just like standard terrestrial microprocessors – and undergo final quality tests.

Through little metal pins or balls sticking out of their packages these miniature brains are then connected to other circuit elements – such as sensors, actuators, memory or power systems – used across the satellite.

To save the time and money needed to develop complex chips like these, ESA’s Microelectronics section maintains a catalogue of chip designs, known as Intellectual Property (IP) cores, available to European industry through ESA licence. 

Think of these IP cores as the tiniest mission ‘building blocks’: specialised designs to perform particular tasks in space, laid down within a microchip.

These IP cores range from single ‘simpler’ functions such as decoding signals from Earth to control the satellite to highly complex computer tasks such as operating a complete spacecraft.

The latter is achieved for example by the SCOC-3 ‘spacecraft controller on a chip’ developed by ESA and Astrium, which itself combines more than 20 different IP cores from other sources, seen at the bottom of the image.

SCOC3 on silicon wafer

Once manufactured, the chips, still on the wafer, are tested. The wafers are then chopped up. They become ready for use when placed inside protective packages – just like standard terrestrial microprocessors – to undergo final quality tests.

Through little metal pins or balls sticking out of their packages these miniature brains are then connected to other circuit elements – such as sensors, actuators, memory or power systems – used across the satellite.

To save the time and money needed to develop complex chips like these, ESA’s Microelectronics section maintains a catalogue of chip designs, known as Intellectual Property (IP) cores, freely available to European industry.

Think of these IP cores as the tiniest mission ‘building blocks’: specialised designs to perform particular tasks in space, laid down within a microchip.

These IP cores range from single ‘simpler’ functions such as decoding signals from Earth to control the satellite to highly complex computer tasks such as operating a complete spacecraft.

The latter is achieved for example by the SCOC3 ‘spacecraft controller on a chip’ developed by ESA and Astrium, which itself combines more than 20 different IP cores from other sources, seen at the bottom of the image.

Each IP core is coded in a ‘hardware description language’ that can then guide the manufacturing process. Today’s state-of-the-art minimum sizes of integrated circuit tracks are measured in tens of nanometres.

Via: "ESA"

Google To Create An Artificial Super-Mind

Famed AI researcher and incorrigable singularity forecaster Ray Kurzweil recently shed some more light on what his new job at Google will entail. It seems that he does, indeed, plan to build a prodigious artificial intelligence, which he hopes will understand the world to a much more sophisticated degree than anything built before–or at least that will act as if it does.

Kurzweil’s AI will be designed to analyze the vast quantities of information Google collects and to then serve as a super-intelligent personal assistant. He suggests it could eavesdrop on your every phone conversation and email exchange and then provide interesting and important information before you ever knew you wanted it. It sounds like a scary-smart version of Google Now (see “Google’s Answer to Siri Thinks Ahead”).

Kurzweil says this of his project at Google, in a video posted by The Singularity Hub:

“There’s no more important project than understanding Intelligence and recreating it. I do envision a fundamental approach based on everything we understand about how the human brain [works]. And there are some things we don’t yet understand so I plan to go off and explore some of my own ideas about how certain things work.”

Kurzweil makes it sound like the effort will be based on the theory of the put forward in his new book, How to Create a Mind. In this work, based largely on observations about current trends in AI research, and his own work on speech and character recognition, Kurzweil suggests a fairly simple mechanism by which information is captured and accessed hierarchically throughout the neo-cortex, and posits that this phenomenon can explain the miracle of human conscious experience.

Kurzweil’s claims are certainly bold, and some have criticized them as hopelessly naïve. Indeed, it’s easy to dismiss any predictions he makes because of the outlandish ones he’s made in the past. But Kurzweil is nothing if not a brilliant inventor, and he indicates that at Google he’ll be rolling his sleeves up and doing real engineering. It’ll be fascinating to see how far this remarkable project takes both the inventor and the company.

Via: "Technology Review"
Image by Anatole Branch
(AKA: Morpheus)

Google Building Your ‘Cybernetic Friend’

World-renowned artificial intelligence expert and Google’s new Director of Engineering, Ray Kurzweil, wants to build a search engine so sophisticated that it could act like a ‘cybernetic friend,’ who knows users better than they know themselves. “I envision in some years that the majority of search queries will be answered without you actually asking,” he said at an intimate gathering at Singularity University’s NASA campus.

Language, Kurzweil argues, is the window to creating a genuine artificial brain, that can understand the meaning of ideas and concepts. “If you write a blog post, you’re not just creating a bag of words, you’re creating some meaningful sentences.” For now, search engines have brute-force algorithms that pick out key words in popular pages and hope that the results, on average, will yield the best information.

So-called “semantic” search parses the meaning and intentions behind words. Semantic search aims to solve the ‘hotdog’ problem, as explained by Google’s Chairman, Eric Schmidt,

“Is it a ‘hot dog’ or a ‘hotdog.’ And, if you knew something about whether the person had dogs, or whether the person was a vegetarian, you’d have a very different potential answer to that question.”

Eventually Google will understand why users are searching for information and provide them with answers they didn’t even know they needed. The education of such an omnipotent new mind will take the vast stores of Google’s database. Perhaps more than any other company, explains Kurzweil, Google has access to the “things you read, what you write, in your emails or blog posts, and so on, even your conversations, what you hear, what you say.”

Google can combine the personalized recommendations of a friend (who often know us better than we know ourselves) with the sum of all human knowledge, creating a sort of super best friend.

This friend of yours, this cybernetic friend, that knows that you that have certain questions about certain health issues or business strategies. And, It can then be canvassing all the new information that comes out in the world every minute and then bring things to your attention without you asking about them

Kurzweil was quick to dispel the myth he was given “unlimited” funds, but humbly suggests that Google is giving him “sufficient resources for a very important project.”

Via: "Tech Crunch"

Anticipatory Computing

I was so intrigued when I came across a new iPad app called MindMeld that is based on the emerging science of “anticipatory computing.”

Using video and voice chat capabilities similar to Skype, MindMeld not only facilitates the discussion, but also adds pertinent photos or videos to the conversation as it interprets what is being said.

We tend to worry about computers that are smarter than we are, automating our skills and taking our jobs. But if computers become more human-like in their thinking, adding our own emotional values to everything we think is important, the heartless machine-only qualities of these technologies will disappear, moving computers away from the paradigm of human-replacer to something more akin to human-enhancer. Here’s what I see happening.

The MindMeld Approach

“We have a predictive model that changes second to second and surfaces relevant information without searching,” says Tuttle. He views this as an effective application of “anticipatory computing” because of the predictive nature of its computational decision-making.

Over time, MindMeld will accrue intelligence as it becomes better at reading and aggregating ambient data.

Capturing Real Human Intelligence

Artificial intelligence only goes so far. But finding a way to capture pieces of real human intelligence can give us far more pertinent information.

As example, if someone conducts a typical search on a search engine, the connecting path between the search terms and the final destination is a very real piece of human intelligence.

Certainly not everyone will agree on the final site selected from a set of search terms. But that will improve over time.

According to Futurist John Smart, in 1998 the average online search phrase consisted of 2 words. Today, the average search contains 5.2 words, trending towards something far longer, more akin to a natural question of 8-10 words.

Over time, capturing millions of “paths” will yield a base of growing intelligence based on the cumulative thinking of everyone involved.

Similarly, when groups of people get into a conversation, and a variety of images are displayed, a selection process where users click on applicable images to help refine the “yield,” the anticipatory computing process will get much smarter, and more relevant, over time.

Via: "The Futurist Speaker"

Google's Virtual Brain Technology

Google set a new landmark in the field of artificial intelligence with software that learned how to recognize cats, people, and other things simply by watching YouTube videos (see "Self-Taught Software"). That technology, modeled on how brain cells operate, is now being put to work making Google's products smarter, with speech recognition being the first service to benefit.

Google's learning software is based on simulating groups of connected brain cells that communicate and influence one another. When such a neural network, as it's called, is exposed to data, the relationships between different neurons can change. That causes the network to develop the ability to react in certain ways to incoming data of a particular kind—and the network is said to have learned something.

The company's neural networks decide for themselves which features of data to pay attention to, and which patterns matter, rather than having humans decide that, say, colors and particular shapes are of interest to software trying to identify objects.  

Google is now using these neural networks to recognize speech more accurately, a technology increasingly important to Google's smartphone operating system, Android, as well as the search app it makes available for Apple devices (see "Google's Answer to Siri Thinks Ahead"). "We got between 20 and 25 percent improvement in terms of words that are wrong," says Vincent Vanhoucke, a leader of Google's speech-recognition efforts. "That means that many more people will have a perfect experience without errors." The neural net is so far only working on U.S. English, and Vanhoucke says similar improvements should be possible when it is introduced for other dialects and languages.

Other Google products will likely improve over time with help from the new learning software. The company's image search tools, for example, could become better able to understand what's in a photo without relying on surrounding text. And Google's self-driving cars (see "Look, No Hands") and mobile computer built into a pair of glasses (see "You Will Want Google's Goggles") could benefit from software better able to make sense of more real-world data.

The new technology grabbed headlines back in June of this year, when Google engineers published results of an experiment that threw 10 million images grabbed from YouTube videos at their simulated brain cells, running 16,000 processors across a thousand computers for 10 days without pause.

"Most people keep their model in a single machine, but we wanted to experiment with very large neural networks," says Jeff Dean, an engineer helping lead the research at Google. "If you scale up both the size of the model and the amount of data you train it with, you can learn finer distinctions or more complex features."

The neural networks that come out of that process are more flexible. "These models can typically take a lot more context," says Dean, giving an example from the world of speech recognition. If, for example, Google's system thought it heard someone say "I'm going to eat a lychee," but the last word was slightly muffled, it could confirm its hunch based on past experience of phrases because "lychee" is a fruit and is used in the same context as "apple" or "orange."

Dean says his team is also testing models that understand both images and text together. "You give it 'porpoise' and it gives you pictures of porpoises," he says. "If you give it a picture of a porpoise, it gives you 'porpoise' as a word."

A next step could be to have the same model learn the sounds of words as well. Being able to relate different forms of data like that could lead to speech recognition that gathers extra clues from video, for example, and it could boost the capabilities of Google's self-driving cars by helping them understand their surroundings by combining the many streams of data they collect, from laser scans of nearby obstacles to information from the car's engine.

Google's work on making neural networks brings us a small step closer to one of the ultimate goals of AI—creating software that can match animal or perhaps even human intelligence, saysYoshua Bengio, a professor at the University of Montreal who works on similar machine-learning techniques. "This is the route toward making more general artificial intelligence—there's no way you will get an intelligent machine if it can't take in a large volume of knowledge about the world," he says.

In fact, the workings of Google's neural networks operate in similar ways to what neuroscientists know about the visual cortex in mammals, the part of the brain that processes visual information, says Bengio. "It turns out that the feature learning networks being used [by Google] are similar to the methods used by the brain that are able to discover objects that exist."

However, he is quick to add that even Google's neural networks are much smaller than the brain, and that they can't perform many things necessary to intelligence, such as reasoning with information collected from the outside world.

Dean is also careful not to imply that the limited intelligences he's building are close to matching any biological brain. But he can't resist pointing out that if you pick the right contest, Google's neural networks have humans beat.

"We are seeing better than human-level performance in some visual tasks," he says, giving the example of labeling, where house numbers appear in photos taken by Google's Street View car, a job that used to be farmed out to many humans.

 "They're starting to use neural nets to decide whether a patch [in an image] is a house number or not," says Dean, and they turn out to perform better than humans. It's a small victory—but one that highlights how far artificial neural nets are behind the ones in your head. "It's probably that it's not very exciting, and a computer never gets tired," says Dean. It takes real intelligence to get bored.

Via: "Technology Review"

U.S. To Replace Human Surveillance With Computers

The U.S. government has funded the development of so-called automatic video surveillance technology by a pair of Carnegie Mellon University researchers who disclosed details about their work this week -- including that it has an ultimate goal of predicting what people will do in the future.

"The main applications are in video surveillance, both civil and military," Alessandro Oltramari, a postdoctoral researcher at Carnegie Mellon who has a Ph.D. from Italy's University of Trento, told CNET yesterday.

Oltramari and fellow researcher Christian Lebiere say automatic video surveillance can monitor camera feeds for suspicious activities like someone at an airport or bus station abandoning a bag for more than a few minutes. "In this specific case, the goal for our system would have been to detect the anomalous behavior," Oltramari says.

Think of it as a much, much smarter version of a red light camera: the unblinking eye of computer software that monitors dozens or even thousands of security camera feeds could catch illicit activities that human operators -- who are expensive and can be distracted or sleepy -- would miss. It could also, depending on how it's implemented, raise similar privacy and civil liberty concerns.

Alessandro Oltramari, left, and Christian Lebiere say their software will "automatize video-surveillance, both in military and civil applications."

(Credit: Carnegie Mellon University)

A paper (PDF) the researchers presented this week at the Semantic Technology for Intelligence, Defense, and Security conference outside of Washington, D.C. -- today's sessions arereserved only for attendees with top secret clearances -- says their system aims "to approximate human visual intelligence in making effective and consistent detections."

Their Army-funded research, Oltramari and Lebiere claim, can go further than merely recognizing whether any illicit activities are currently taking place. It will, they say, be capable of "eventually predicting" what's going to happen next.

This approach relies heavily on advances by machine vision researchers, who have made remarkable strides in last few decades in recognizing stationary and moving objects and their properties. It's the same vein of work that led to Google's self-driving cars, face recognition software used on Facebook and Picasa, and consumer electronics like Microsoft's Kinect.

When it works well, machine vision can detect objects and people -- call them nouns -- that are on the other side of the camera's lens.

But to figure out what these nouns are doing, or are allowed to do, you need the computer science equivalent of verbs. And that's where Oltramari and Lebiere have built on the work of other Carnegie Mellon researchers to create what they call a "cognitive engine" that can understand the rules by which nouns and verbs are allowed to interact.

Their cognitive engine incorporates research, called activity forecasting, conducted by a team led by postdoctoral fellow Kris Kitani, which tries to understand what humans will do by calculating which physical trajectories are most likely. They say their software "models the effect of the physical environment on the choice of human actions."

Both projects are components of Carnegie Mellon's Mind's Eye architecture, a DARPA-created project that aims to develop smart cameras for machine-based visual intelligence.

Predicts Oltramari: "This work should support human operators and automatize video-surveillance, both in military and civil applications."

Via: "C-Net"

The Consequences Of Machine Intelligence

The question of what happens when machines get to be as intelligent as and even more intelligent than people seems to occupy many science-fiction writers. The Terminator movie trilogy, for example, featured Skynet, a self-aware artificial intelligence that served as the trilogy's main villain, battling humanity through its Terminator cyborgs. Among technologists, it is mostly "Singularitarians" who think about the day when machine will surpass humans in intelligence. 

The term "singularity" as a description for a phenomenon of technological acceleration leading to "machine-intelligence explosion" was coined by the mathematician Stanislaw Ulam in 1958, when he wrote of a conversation with John von Neumann concerning the "ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue." More recently, the concept has been popularized by the futurist Ray Kurzweil, who pinpointed 2045 as the year of singularity. Kurzweil has also founded Singularity University and the annual Singularity Summit.

It is fair to say, I believe, that Singularitarians are not quite in the mainstream. Perhaps it is due to their belief that by 2045 humans will also become immortal and be able to download their consciousness to computers. It was, therefore, quite surprising when in 2000, Bill Joy, a very mainstream technologist as co-founder of Sun Microsystems, wrote an article entitled "Why the Future Doesn't Need Us" for Wired magazine. "Our most powerful 21st-century technologies -- robotics, genetic engineering, and nanotech -- are threatening to make humans an endangered species," he wrote. Joy's article was widely noted when it appeared, but it seems to have made little impact.

It is in the context of the Great Recession that people started noticing that while machines have yet to exceed humans in intelligence, they are getting intelligent enough to have a major impact on the job market. In their 2011 book, Race Against The Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy authors Erik Brynjolfsson and Andrew McAfee, argued that "technological progress is accelerating innovation even as it leaves many types of workers behind." Indeed, over the past 30 years, as we saw the personal computer morph into tablets, smartphones, and cloud computing, we also saw income inequality grow worldwide. While the loss of millions of jobs over the past few years has been attributed to the Great Recession, whose end is not yet in sight, it now seems that technology-driven productivity growth is at least a major factor. Such concerns have gone mainstream in the past year, with articles in newspapers and magazines carrying titles such as "More Jobs Predicted for Machines, Not People," "Marathon Machine: Unskilled Workers Are Struggling to Keep Up With Technological Change," "It's a Man vs. Machine Recovery," and "The Robots Are Winning."

Early AI pioneers were brimming with optimism about the possibilities of machine intelligence. Alan Turing's 1950 paper, "Computing Machinery and Intelligence" is perhaps best known for his proposal of an "Imitation Game", known today as "the Turing Test", as an operational definition for machine intelligence. 

But the main focus of the 1950 paper is actually not the Imitation Game but the possibility of machine intelligence. Turing carefully analyzed and rebutted arguments against machine intelligence. He also stated his belief that we will see machine intelligence by the end of the 20th century, writing "I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted."

While we now know that Turing was too optimistic on the timeline, AI's inexorable progress over the past 50 years suggests that Herbert Simon was right when he wrote in 1956 "machines will be capable ... of doing any work a man can do." I do not expect this to happen in the very near future, but I do believe that by 2045 machines will be able to do if not any work that humans can do, then a very significant fraction of the work that humans can do. Bill Joy's question deserves therefore not to be ignored: Does the future need us? By this I mean to ask, if machines are capable of doing almost any work humans can do, what will humans do? I have been getting various answers to this question, but I find none satisfying.

A typical answer to my raising this question is to tell me that I am a Luddite. (Luddism is defined as distrust or fear of the inevitable changes brought about by new technology.) This is an ad hominem attack that does not deserve a serious answer.

We are facing the prospect of being completely out-competed by our own creations.

A more thoughtful answer is that technology has been destroying jobs since the start of the Industrial Revolution, yet new jobs are continually created. The AI Revolution, however, is different than the Industrial Revolution. In the 19th century machines competed with human brawn. Now machines are competing with human brain. Robots combine brain and brawn. We are facing the prospect of being completely out-competed by our own creations. Another typical answer is that if machines will do all of our work, then we will be free to pursue leisure activities. The economist John Maynard Keynes addressed this issue already in 1930, when he wrote, "The increase of technical efficiency has been taking place faster than we can deal with the problem of labour absorption." Keynes imagined 2030 as a time in which most people worked only 15 hours a week, and would occupy themselves mostly with leisure activities.

I do not find this to be a promising future. First, if machines can do almost all of our work, then it is not clear that even 15 weekly hours of work will be required. Second, I do not find the prospect of leisure-filled life appealing. I believe that work is essential to human well-being. Third, our economic system would have to undergo a radical restructuring to enable billions of people to live lives of leisure. 

Unemployment rate in the US is currently under 9 percent and is considered to be a huge problem. 

Finally, people tell me that my concerns apply only to a future that is so far away that we need not worry about it. I find this answer to be unacceptable. 2045 is merely a generation away from us. We cannot shirk responsibility from concerns for the welfare of the next generation.

In 2000, Bill Joy advocated a policy of relinquishment -- "to limit development of the technologies that are too dangerous, by limiting our pursuit of certain kinds of knowledge." I am not sure I am ready to go that far, but I do believe that just because technology can do good, it does not mean that more technology is always better. Turing was what we call today a "techno-enthusiast", writing in 1950 that "we may hope that machines will eventually compete with men in all purely intellectual fields ... we can see plenty there that needs to be done." But his incisive analysis about thepossibility of machine intelligence was not accompanied by an analysis of the consequences of machine intelligences. It is time, I believe, to put the question of these consequences squarely on the table. We cannot blindly pursue the goal of machine intelligence without pondering its consequences.

Via: "The Atlantic"

Autonomous Terminator Drones With AI

The US Navy has executed the first launch of a stealth drone set to be the first robot aircraft piloted by artificial intelligence. The “killer robot” might be the next step in the development of machines with the power to decide who lives or dies.

After five-years in the making, the X-47B Unmanned Combat Air System (UCAS) demonstrator completed its first land-based catapult launch, “marking the start for a new era of naval aviation,” the navy announced on Thursday.

With a wingspan of 62-feet (18.9m), the subsonic drone will be the first tailless aircraft ever to land on a carrier.

"The X-47B shore-based catapult launch we witnessed here today will leave a mark in history," the navy quotes Vice Adm. David Dunaway, NAVAIR commander, as saying.

"We are working toward the future integration of unmanned aircraft on the carrier deck, something we didn't envision 60 years ago when the steam catapult was first built here," he continued.

Engineers had originally planned 50 test flights from the X-47B, but after performing beyond expectations, they stopped after 16 trials.

Following the dozen-plus successful trials, the next step came on Monday, when the drone was hoisted on to the flight deck of aircraft carrier USS Harry S Truman.

After a series of upcoming sea trials planned for 2013, the X-47B is set to become the world’s first unmanned aircraft piloted by artificial intelligence rather than a remote human operator.

Contractors hoist the X-47B Unmanned Combat Air System (UCAS) demonstrator to the flight deck of the aircraft carrier USS Harry S. Truman at Naval Station Norfolk, Virginia, in this U.S. Navy handout photo dated November 26, 2012 . (Reuters/U.S. Navy/Seaman Christopher A. Morrison/Handout)

The subsonic stealth drone, first dreamed up by the Defense Advanced Research Projects Agency (DARPA) and later taken over by the navy, has been given a robot brain, putting it miles above the thousands of other unmanned drones currently circling the skies. While automation has long been a feature of robots, the X-47B will truly be autonomous.

People will still have a say in the X-47B’s overall mission, though the drone will be able to make split-second decisions in a real-time environment all on its own.

So while a living and breathing operator might select its flight path, a medley of GPS equipment, accelerometers, altimeters, gyroscopes, collision avoidance sensors and its highly-evolved Control Display Unit will leave the X-47B’s moment-to-moment decisions out of human hands.

With two weapon bays capable of carrying up to 4,500lbs (2 tonnes) of ordnance, the X-47B certainly has the ability to kill, though for now, it does not have the will.

The X-47B Unmanned Combat Air System (UCAS) demonstrator is hoisted onto the flight deck of the aircraft carrier USS Harry S. Truman at Naval Station Norfolk, Virginia, in this U.S. Navy handout photo dated November 26, 2012. (Reuters/U.S. Navy/Mass Communication Specialist 3rd Class Lorenzo J. Burleson/Handout)

Contractors prepare to hoist the X-47B Unmanned Combat Air System (UCAS) demonstrator onto the flight deck of the aircraft carrier USS Harry S. Truman at Naval Station Norfolk, Virginia, in this U.S. Navy handout photo dated November 26, 2012. (Reuters/U.S. Navy/Mass Communication Specialist 3rd Class Lorenzo J. Burleson/Handout)

Via: "Russia Today"

MIT Develops Miniature Shape-Shifting Robots

 ... these simplistic devices are a far cry from the dream of real-life Transformers. Now a group at MIT's Center for Bits and Atoms has created a robot that could point the way toward the real thing.

Developed by lab director Neil Gershenfeld, visiting scientist Ara Knaian, and graduate student Kenneth Cheung, the Milli-Motein is a reconfigurable robot that can be programmed to fold itself into a number of different shapes. And, after the robot has shifted into a new shape, it can hold that shape even when its power is cut off by using a what is known as an electro-permanent motor. Gershenfeld said, "[The Milli-Motein is] effectively a one-dimensional robot that can be made in a continuous strip, without conventionally moving parts, and then folded into arbitrary shapes."

However, the project's research paper, recently presented at the 2012 Intelligent Robots and Systems conference, warns that real world deployment of such robots will require cheaper, more durable materials, as well as better software and algorithms.

You can see the Milli-Motein in action in the video below.

Via Center for Bits and Atoms

Via: "Dvice"