Tag Archives: practical

#432882 Why the Discovery of Room-Temperature ...

Superconductors are among the most bizarre and exciting materials yet discovered. Counterintuitive quantum-mechanical effects mean that, below a critical temperature, they have zero electrical resistance. This property alone is more than enough to spark the imagination.

A current that could flow forever without losing any energy means transmission of power with virtually no losses in the cables. When renewable energy sources start to dominate the grid and high-voltage transmission across continents becomes important to overcome intermittency, lossless cables will result in substantial savings.

What’s more, a superconducting wire carrying a current that never, ever diminishes would act as a perfect store of electrical energy. Unlike batteries, which degrade over time, if the resistance is truly zero, you could return to the superconductor in a billion years and find that same old current flowing through it. Energy could be captured and stored indefinitely!

With no resistance, a huge current could be passed through the superconducting wire and, in turn, produce magnetic fields of incredible power.

You could use them to levitate trains and produce astonishing accelerations, thereby revolutionizing the transport system. You could use them in power plants—replacing conventional methods which spin turbines in magnetic fields to generate electricity—and in quantum computers as the two-level system required for a “qubit,” in which the zeros and ones are replaced by current flowing clockwise or counterclockwise in a superconductor.

Arthur C. Clarke famously said that any sufficiently advanced technology is indistinguishable from magic; superconductors can certainly seem like magical devices. So, why aren’t they busy remaking the world? There’s a problem—that critical temperature.

For all known materials, it’s hundreds of degrees below freezing. Superconductors also have a critical magnetic field; beyond a certain magnetic field strength, they cease to work. There’s a tradeoff: materials with an intrinsically high critical temperature can also often provide the largest magnetic fields when cooled well below that temperature.

This has meant that superconductor applications so far have been limited to situations where you can afford to cool the components of your system to close to absolute zero: in particle accelerators and experimental nuclear fusion reactors, for example.

But even as some aspects of superconductor technology become mature in limited applications, the search for higher temperature superconductors moves on. Many physicists still believe a room-temperature superconductor could exist. Such a discovery would unleash amazing new technologies.

The Quest for Room-Temperature Superconductors
After Heike Kamerlingh Onnes discovered superconductivity by accident while attempting to prove Lord Kelvin’s theory that resistance would increase with decreasing temperature, theorists scrambled to explain the new property in the hope that understanding it might allow for room-temperature superconductors to be synthesized.

They came up with the BCS theory, which explained some of the properties of superconductors. It also predicted that the dream of technologists, a room-temperature superconductor, could not exist; the maximum temperature for superconductivity according to BCS theory was just 30 K.

Then, in the 1980s, the field changed again with the discovery of unconventional, or high-temperature, superconductivity. “High temperature” is still very cold: the highest temperature for superconductivity achieved was -70°C for hydrogen sulphide at extremely high pressures. For normal pressures, -140°C is near the upper limit. Unfortunately, high-temperature superconductors—which require relatively cheap liquid nitrogen, rather than liquid helium, to cool—are mostly brittle ceramics, which are expensive to form into wires and have limited application.

Given the limitations of high-temperature superconductors, researchers continue to believe there’s a better option awaiting discovery—an incredible new material that checks boxes like superconductivity approaching room temperature, affordability, and practicality.

Tantalizing Clues
Without a detailed theoretical understanding of how this phenomenon occurs—although incremental progress happens all the time—scientists can occasionally feel like they’re taking educated guesses at materials that might be likely candidates. It’s a little like trying to guess a phone number, but with the periodic table of elements instead of digits.

Yet the prospect remains, in the words of one researcher, tantalizing. A Nobel Prize and potentially changing the world of energy and electricity is not bad for a day’s work.

Some research focuses on cuprates, complex crystals that contain layers of copper and oxygen atoms. Doping cuprates with various different elements, such exotic compounds as mercury barium calcium copper oxide, are amongst the best superconductors known today.

Research also continues into some anomalous but unexplained reports that graphite soaked in water can act as a room-temperature superconductor, but there’s no indication that this could be used for technological applications yet.

In early 2017, as part of the ongoing effort to explore the most extreme and exotic forms of matter we can create on Earth, researchers managed to compress hydrogen into a metal.

The pressure required to do this was more than that at the core of the Earth and thousands of times higher than that at the bottom of the ocean. Some researchers in the field, called condensed-matter physics, doubt that metallic hydrogen was produced at all.

It’s considered possible that metallic hydrogen could be a room-temperature superconductor. But getting the samples to stick around long enough for detailed testing has proved tricky, with the diamonds containing the metallic hydrogen suffering a “catastrophic failure” under the pressure.

Superconductivity—or behavior that strongly resembles it—was also observed in yttrium barium copper oxide (YBCO) at room temperature in 2014. The only catch was that this electron transport lasted for a tiny fraction of a second and required the material to be bombarded with pulsed lasers.

Not very practical, you might say, but tantalizing nonetheless.

Other new materials display enticing properties too. The 2016 Nobel Prize in Physics was awarded for the theoretical work that characterizes topological insulators—materials that exhibit similarly strange quantum behaviors. They can be considered perfect insulators for the bulk of the material but extraordinarily good conductors in a thin layer on the surface.

Microsoft is betting on topological insulators as the key component in their attempt at a quantum computer. They’ve also been considered potentially important components in miniaturized circuitry.

A number of remarkable electronic transport properties have also been observed in new, “2D” structures—like graphene, these are materials synthesized to be as thick as a single atom or molecule. And research continues into how we can utilize the superconductors we’ve already discovered; for example, some teams are trying to develop insulating material that prevents superconducting HVDC cable from overheating.

Room-temperature superconductivity remains as elusive and exciting as it has been for over a century. It is unclear whether a room-temperature superconductor can exist, but the discovery of high-temperature superconductors is a promising indicator that unconventional and highly useful quantum effects may be discovered in completely unexpected materials.

Perhaps in the future—through artificial intelligence simulations or the serendipitous discoveries of a 21st century Kamerlingh Onnes—this little piece of magic could move into the realm of reality.

Image Credit: ktsdesign / Shutterstock.com Continue reading

Posted in Human Robots

#432671 Stuff 3.0: The Era of Programmable ...

It’s the end of a long day in your apartment in the early 2040s. You decide your work is done for the day, stand up from your desk, and yawn. “Time for a film!” you say. The house responds to your cues. The desk splits into hundreds of tiny pieces, which flow behind you and take on shape again as a couch. The computer screen you were working on flows up the wall and expands into a flat projection screen. You relax into the couch and, after a few seconds, a remote control surfaces from one of its arms.

In a few seconds flat, you’ve gone from a neatly-equipped office to a home cinema…all within the same four walls. Who needs more than one room?

This is the dream of those who work on “programmable matter.”

In his recent book about AI, Max Tegmark makes a distinction between three different levels of computational sophistication for organisms. Life 1.0 is single-celled organisms like bacteria; here, hardware is indistinguishable from software. The behavior of the bacteria is encoded into its DNA; it cannot learn new things.

Life 2.0 is where humans live on the spectrum. We are more or less stuck with our hardware, but we can change our software by choosing to learn different things, say, Spanish instead of Italian. Much like managing space on your smartphone, your brain’s hardware will allow you to download only a certain number of packages, but, at least theoretically, you can learn new behaviors without changing your underlying genetic code.

Life 3.0 marks a step-change from this: creatures that can change both their hardware and software in something like a feedback loop. This is what Tegmark views as a true artificial intelligence—one that can learn to change its own base code, leading to an explosion in intelligence. Perhaps, with CRISPR and other gene-editing techniques, we could be using our “software” to doctor our “hardware” before too long.

Programmable matter extends this analogy to the things in our world: what if your sofa could “learn” how to become a writing desk? What if, instead of a Swiss Army knife with dozens of tool attachments, you just had a single tool that “knew” how to become any other tool you could require, on command? In the crowded cities of the future, could houses be replaced by single, OmniRoom apartments? It would save space, and perhaps resources too.

Such are the dreams, anyway.

But when engineering and manufacturing individual gadgets is such a complex process, you can imagine that making stuff that can turn into many different items can be extremely complicated. Professor Skylar Tibbits at MIT referred to it as 4D printing in a TED Talk, and the website for his research group, the Self-Assembly Lab, excitedly claims, “We have also identified the key ingredients for self-assembly as a simple set of responsive building blocks, energy and interactions that can be designed within nearly every material and machining process available. Self-assembly promises to enable breakthroughs across many disciplines, from biology to material science, software, robotics, manufacturing, transportation, infrastructure, construction, the arts, and even space exploration.”

Naturally, their projects are still in the early stages, but the Self-Assembly Lab and others are genuinely exploring just the kind of science fiction applications we mooted.

For example, there’s the cell-phone self-assembly project, which brings to mind eerie, 24/7 factories where mobile phones assemble themselves from 3D printed kits without human or robotic intervention. Okay, so the phones they’re making are hardly going to fly off the shelves as fashion items, but if all you want is something that works, it could cut manufacturing costs substantially and automate even more of the process.

One of the major hurdles to overcome in making programmable matter a reality is choosing the right fundamental building blocks. There’s a very important balance to strike. To create fine details, you need to have things that aren’t too big, so as to keep your rearranged matter from being too lumpy. This might make the building blocks useless for certain applications—for example, if you wanted to make tools for fine manipulation. With big pieces, it might be difficult to simulate a range of textures. On the other hand, if the pieces are too small, different problems can arise.

Imagine a setup where each piece is a small robot. You have to contain the robot’s power source and its brain, or at least some kind of signal-generator and signal-processor, all in the same compact unit. Perhaps you can imagine that one might be able to simulate a range of textures and strengths by changing the strength of the “bond” between individual units—your desk might need to be a little bit more firm than your bed, which might be nicer with a little more give.

Early steps toward creating this kind of matter have been taken by those who are developing modular robots. There are plenty of different groups working on this, including MIT, Lausanne, and the University of Brussels.

In the latter configuration, one individual robot acts as a centralized decision-maker, referred to as the brain unit, but additional robots can autonomously join the brain unit as and when needed to change the shape and structure of the overall system. Although the system is only ten units at present, it’s a proof-of-concept that control can be orchestrated over a modular system of robots; perhaps in the future, smaller versions of the same thing could be the components of Stuff 3.0.

You can imagine that with machine learning algorithms, such swarms of robots might be able to negotiate obstacles and respond to a changing environment more easily than an individual robot (those of you with techno-fear may read “respond to a changing environment” and imagine a robot seamlessly rearranging itself to allow a bullet to pass straight through without harm).

Speaking of robotics, the form of an ideal robot has been a subject of much debate. In fact, one of the major recent robotics competitions—DARPA’s Robotics Challenge—was won by a robot that could adapt, beating Boston Dynamics’ infamous ATLAS humanoid with the simple addition of a wheel that allowed it to drive as well as walk.

Rather than building robots into a humanoid shape (only sometimes useful), allowing them to evolve and discover the ideal form for performing whatever you’ve tasked them to do could prove far more useful. This is particularly true in disaster response, where expensive robots can still be more valuable than humans, but conditions can be very unpredictable and adaptability is key.

Further afield, many futurists imagine “foglets” as the tiny nanobots that will be capable of constructing anything from raw materials, somewhat like the “Santa Claus machine.” But you don’t necessarily need anything quite so indistinguishable from magic to be useful. Programmable matter that can respond and adapt to its surroundings could be used in all kinds of industrial applications. How about a pipe that can strengthen or weaken at will, or divert its direction on command?

We’re some way off from being able to order our beds to turn into bicycles. As with many tech ideas, it may turn out that the traditional low-tech solution is far more practical and cost-effective, even as we can imagine alternatives. But as the march to put a chip in every conceivable object goes on, it seems certain that inanimate objects are about to get a lot more animated.

Image Credit: PeterVrabel / Shutterstock.com Continue reading

Posted in Human Robots

#432519 Robot Cities: Three Urban Prototypes for ...

Before I started working on real-world robots, I wrote about their fictional and historical ancestors. This isn’t so far removed from what I do now. In factories, labs, and of course science fiction, imaginary robots keep fueling our imagination about artificial humans and autonomous machines.

Real-world robots remain surprisingly dysfunctional, although they are steadily infiltrating urban areas across the globe. This fourth industrial revolution driven by robots is shaping urban spaces and urban life in response to opportunities and challenges in economic, social, political, and healthcare domains. Our cities are becoming too big for humans to manage.

Good city governance enables and maintains smooth flow of things, data, and people. These include public services, traffic, and delivery services. Long queues in hospitals and banks imply poor management. Traffic congestion demonstrates that roads and traffic systems are inadequate. Goods that we increasingly order online don’t arrive fast enough. And the WiFi often fails our 24/7 digital needs. In sum, urban life, characterized by environmental pollution, speedy life, traffic congestion, connectivity and increased consumption, needs robotic solutions—or so we are led to believe.

Is this what the future holds? Image Credit: Photobank gallery / Shutterstock.com
In the past five years, national governments have started to see automation as the key to (better) urban futures. Many cities are becoming test beds for national and local governments for experimenting with robots in social spaces, where robots have both practical purpose (to facilitate everyday life) and a very symbolic role (to demonstrate good city governance). Whether through autonomous cars, automated pharmacists, service robots in local stores, or autonomous drones delivering Amazon parcels, cities are being automated at a steady pace.

Many large cities (Seoul, Tokyo, Shenzhen, Singapore, Dubai, London, San Francisco) serve as test beds for autonomous vehicle trials in a competitive race to develop “self-driving” cars. Automated ports and warehouses are also increasingly automated and robotized. Testing of delivery robots and drones is gathering pace beyond the warehouse gates. Automated control systems are monitoring, regulating and optimizing traffic flows. Automated vertical farms are innovating production of food in “non-agricultural” urban areas around the world. New mobile health technologies carry promise of healthcare “beyond the hospital.” Social robots in many guises—from police officers to restaurant waiters—are appearing in urban public and commercial spaces.

Vertical indoor farm. Image Credit: Aisyaqilumaranas / Shutterstock.com
As these examples show, urban automation is taking place in fits and starts, ignoring some areas and racing ahead in others. But as yet, no one seems to be taking account of all of these various and interconnected developments. So, how are we to forecast our cities of the future? Only a broad view allows us to do this. To give a sense, here are three examples: Tokyo, Dubai, and Singapore.

Tokyo
Currently preparing to host the Olympics 2020, Japan’s government also plans to use the event to showcase many new robotic technologies. Tokyo is therefore becoming an urban living lab. The institution in charge is the Robot Revolution Realization Council, established in 2014 by the government of Japan.

Tokyo: city of the future. Image Credit: ESB Professional / Shutterstock.com
The main objectives of Japan’s robotization are economic reinvigoration, cultural branding, and international demonstration. In line with this, the Olympics will be used to introduce and influence global technology trajectories. In the government’s vision for the Olympics, robot taxis transport tourists across the city, smart wheelchairs greet Paralympians at the airport, ubiquitous service robots greet customers in 20-plus languages, and interactively augmented foreigners speak with the local population in Japanese.

Tokyo shows us what the process of state-controlled creation of a robotic city looks like.

Singapore
Singapore, on the other hand, is a “smart city.” Its government is experimenting with robots with a different objective: as physical extensions of existing systems to improve management and control of the city.

In Singapore, the techno-futuristic national narrative sees robots and automated systems as a “natural” extension of the existing smart urban ecosystem. This vision is unfolding through autonomous delivery robots (the Singapore Post’s delivery drone trials in partnership with AirBus helicopters) and driverless bus shuttles from Easymile, EZ10.

Meanwhile, Singapore hotels are employing state-subsidized service robots to clean rooms and deliver linen and supplies, and robots for early childhood education have been piloted to understand how robots can be used in pre-schools in the future. Health and social care is one of the fastest growing industries for robots and automation in Singapore and globally.

Dubai
Dubai is another emerging prototype of a state-controlled smart city. But rather than seeing robotization simply as a way to improve the running of systems, Dubai is intensively robotizing public services with the aim of creating the “happiest city on Earth.” Urban robot experimentation in Dubai reveals that authoritarian state regimes are finding innovative ways to use robots in public services, transportation, policing, and surveillance.

National governments are in competition to position themselves on the global politico-economic landscape through robotics, and they are also striving to position themselves as regional leaders. This was the thinking behind the city’s September 2017 test flight of a flying taxi developed by the German drone firm Volocopter—staged to “lead the Arab world in innovation.” Dubai’s objective is to automate 25% of its transport system by 2030.

It is currently also experimenting with Barcelona-based PAL Robotics’ humanoid police officer and Singapore-based vehicle OUTSAW. If the experiments are successful, the government has announced it will robotize 25% of the police force by 2030.

While imaginary robots are fueling our imagination more than ever—from Ghost in the Shell to Blade Runner 2049—real-world robots make us rethink our urban lives.

These three urban robotic living labs—Tokyo, Singapore, Dubai—help us gauge what kind of future is being created, and by whom. From hyper-robotized Tokyo to smartest Singapore and happy, crime-free Dubai, these three comparisons show that, no matter what the context, robots are perceived as a means to achieve global futures based on a specific national imagination. Just like the films, they demonstrate the role of the state in envisioning and creating that future.

This article was originally published on The Conversation. Read the original article.

Image Credit: 3000ad / Shutterstock.com Continue reading

Posted in Human Robots

#432311 Everyone Is Talking About AI—But Do ...

In 2017, artificial intelligence attracted $12 billion of VC investment. We are only beginning to discover the usefulness of AI applications. Amazon recently unveiled a brick-and-mortar grocery store that has successfully supplanted cashiers and checkout lines with computer vision, sensors, and deep learning. Between the investment, the press coverage, and the dramatic innovation, “AI” has become a hot buzzword. But does it even exist yet?

At the World Economic Forum Dr. Kai-Fu Lee, a Taiwanese venture capitalist and the founding president of Google China, remarked, “I think it’s tempting for every entrepreneur to package his or her company as an AI company, and it’s tempting for every VC to want to say ‘I’m an AI investor.’” He then observed that some of these AI bubbles could burst by the end of 2018, referring specifically to “the startups that made up a story that isn’t fulfillable, and fooled VCs into investing because they don’t know better.”

However, Dr. Lee firmly believes AI will continue to progress and will take many jobs away from workers. So, what is the difference between legitimate AI, with all of its pros and cons, and a made-up story?

If you parse through just a few stories that are allegedly about AI, you’ll quickly discover significant variation in how people define it, with a blurred line between emulated intelligence and machine learning applications.

I spoke to experts in the field of AI to try to find consensus, but the very question opens up more questions. For instance, when is it important to be accurate to a term’s original definition, and when does that commitment to accuracy amount to the splitting of hairs? It isn’t obvious, and hype is oftentimes the enemy of nuance. Additionally, there is now a vested interest in that hype—$12 billion, to be precise.

This conversation is also relevant because world-renowned thought leaders have been publicly debating the dangers posed by AI. Facebook CEO Mark Zuckerberg suggested that naysayers who attempt to “drum up these doomsday scenarios” are being negative and irresponsible. On Twitter, business magnate and OpenAI co-founder Elon Musk countered that Zuckerberg’s understanding of the subject is limited. In February, Elon Musk engaged again in a similar exchange with Harvard professor Steven Pinker. Musk tweeted that Pinker doesn’t understand the difference between functional/narrow AI and general AI.

Given the fears surrounding this technology, it’s important for the public to clearly understand the distinctions between different levels of AI so that they can realistically assess the potential threats and benefits.

As Smart As a Human?
Erik Cambria, an expert in the field of natural language processing, told me, “Nobody is doing AI today and everybody is saying that they do AI because it’s a cool and sexy buzzword. It was the same with ‘big data’ a few years ago.”

Cambria mentioned that AI, as a term, originally referenced the emulation of human intelligence. “And there is nothing today that is even barely as intelligent as the most stupid human being on Earth. So, in a strict sense, no one is doing AI yet, for the simple fact that we don’t know how the human brain works,” he said.

He added that the term “AI” is often used in reference to powerful tools for data classification. These tools are impressive, but they’re on a totally different spectrum than human cognition. Additionally, Cambria has noticed people claiming that neural networks are part of the new wave of AI. This is bizarre to him because that technology already existed fifty years ago.

However, technologists no longer need to perform the feature extraction by themselves. They also have access to greater computing power. All of these advancements are welcomed, but it is perhaps dishonest to suggest that machines have emulated the intricacies of our cognitive processes.

“Companies are just looking at tricks to create a behavior that looks like intelligence but that is not real intelligence, it’s just a mirror of intelligence. These are expert systems that are maybe very good in a specific domain, but very stupid in other domains,” he said.

This mimicry of intelligence has inspired the public imagination. Domain-specific systems have delivered value in a wide range of industries. But those benefits have not lifted the cloud of confusion.

Assisted, Augmented, or Autonomous
When it comes to matters of scientific integrity, the issue of accurate definitions isn’t a peripheral matter. In a 1974 commencement address at the California Institute of Technology, Richard Feynman famously said, “The first principle is that you must not fool yourself—and you are the easiest person to fool.” In that same speech, Feynman also said, “You should not fool the layman when you’re talking as a scientist.” He opined that scientists should bend over backwards to show how they could be wrong. “If you’re representing yourself as a scientist, then you should explain to the layman what you’re doing—and if they don’t want to support you under those circumstances, then that’s their decision.”

In the case of AI, this might mean that professional scientists have an obligation to clearly state that they are developing extremely powerful, controversial, profitable, and even dangerous tools, which do not constitute intelligence in any familiar or comprehensive sense.

The term “AI” may have become overhyped and confused, but there are already some efforts underway to provide clarity. A recent PwC report drew a distinction between “assisted intelligence,” “augmented intelligence,” and “autonomous intelligence.” Assisted intelligence is demonstrated by the GPS navigation programs prevalent in cars today. Augmented intelligence “enables people and organizations to do things they couldn’t otherwise do.” And autonomous intelligence “establishes machines that act on their own,” such as autonomous vehicles.

Roman Yampolskiy is an AI safety researcher who wrote the book “Artificial Superintelligence: A Futuristic Approach.” I asked him whether the broad and differing meanings might present difficulties for legislators attempting to regulate AI.

Yampolskiy explained, “Intelligence (artificial or natural) comes on a continuum and so do potential problems with such technology. We typically refer to AI which one day will have the full spectrum of human capabilities as artificial general intelligence (AGI) to avoid some confusion. Beyond that point it becomes superintelligence. What we have today and what is frequently used in business is narrow AI. Regulating anything is hard, technology is no exception. The problem is not with terminology but with complexity of such systems even at the current level.”

When asked if people should fear AI systems, Dr. Yampolskiy commented, “Since capability comes on a continuum, so do problems associated with each level of capability.” He mentioned that accidents are already reported with AI-enabled products, and as the technology advances further, the impact could spread beyond privacy concerns or technological unemployment. These concerns about the real-world effects of AI will likely take precedence over dictionary-minded quibbles. However, the issue is also about honesty versus deception.

Is This Buzzword All Buzzed Out?
Finally, I directed my questions towards a company that is actively marketing an “AI Virtual Assistant.” Carl Landers, the CMO at Conversica, acknowledged that there are a multitude of explanations for what AI is and isn’t.

He said, “My definition of AI is technology innovation that helps solve a business problem. I’m really not interested in talking about the theoretical ‘can we get machines to think like humans?’ It’s a nice conversation, but I’m trying to solve a practical business problem.”

I asked him if AI is a buzzword that inspires publicity and attracts clients. According to Landers, this was certainly true three years ago, but those effects have already started to wane. Many companies now claim to have AI in their products, so it’s less of a differentiator. However, there is still a specific intention behind the word. Landers hopes to convey that previously impossible things are now possible. “There’s something new here that you haven’t seen before, that you haven’t heard of before,” he said.

According to Brian Decker, founder of Encom Lab, machine learning algorithms only work to satisfy their preexisting programming, not out of an interior drive for better understanding. Therefore, he views AI as an entirely semantic argument.

Decker stated, “A marketing exec will claim a photodiode controlled porch light has AI because it ‘knows when it is dark outside,’ while a good hardware engineer will point out that not one bit in a register in the entire history of computing has ever changed unless directed to do so according to the logic of preexisting programming.”

Although it’s important for everyone to be on the same page regarding specifics and underlying meaning, AI-powered products are already powering past these debates by creating immediate value for humans. And ultimately, humans care more about value than they do about semantic distinctions. In an interview with Quartz, Kai-Fu Lee revealed that algorithmic trading systems have already given him an 8X return over his private banking investments. “I don’t trade with humans anymore,” he said.

Image Credit: vrender / Shutterstock.com Continue reading

Posted in Human Robots

#432264 Robot Research in the Wild: Water ...

What can we learn from bringing practical robotics to people who have never seen a robot before? Continue reading

Posted in Human Robots