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#432646 How Fukushima Changed Japanese Robotics ...

In March 2011, Japan was hit by a catastrophic earthquake that triggered a terrible tsunami. Thousands were killed and billions of dollars of damage was done in one of the worst disasters of modern times. For a few perilous weeks, though, the eyes of the world were focused on the Fukushima Daiichi nuclear power plant. Its safety systems were unable to cope with the tsunami damage, and there were widespread fears of another catastrophic meltdown that could spread radiation over several countries, like the Chernobyl disaster in the 1980s. A heroic effort that included dumping seawater into the reactor core prevented an even bigger catastrophe. As it is, a hundred thousand people are still evacuated from the area, and it will likely take many years and hundreds of billions of dollars before the region is safe.

Because radiation is so dangerous to humans, the natural solution to the Fukushima disaster was to send in robots to monitor levels of radiation and attempt to begin the clean-up process. The techno-optimists in Japan had discovered a challenge, deep in the heart of that reactor core, that even their optimism could not solve. The radiation fried the circuits of the robots that were sent in, even those specifically designed and built to deal with the Fukushima catastrophe. The power plant slowly became a vast robot graveyard. While some robots initially saw success in measuring radiation levels around the plant—and, recently, a robot was able to identify the melted uranium fuel at the heart of the disaster—hopes of them playing a substantial role in the clean-up are starting to diminish.

In Tokyo’s neon Shibuya district, it can sometimes seem like it’s brighter at night than it is during the daytime. In karaoke booths on the twelfth floor—because everything is on the twelfth floor—overlooking the brightly-lit streets, businessmen unwind by blasting out pop hits. It can feel like the most artificial place on Earth; your senses are dazzled by the futuristic techno-optimism. Stock footage of the area has become symbolic of futurism and modernity.

Japan has had a reputation for being a nation of futurists for a long time. We’ve already described how tech giant Softbank, headed by visionary founder Masayoshi Son, is investing billions in a technological future, including plans for the world’s largest solar farm.

When Google sold pioneering robotics company Boston Dynamics in 2017, Softbank added it to their portfolio, alongside the famous Nao and Pepper robots. Some may think that Son is taking a gamble in pursuing a robotics project even Google couldn’t succeed in, but this is a man who lost nearly everything in the dot-com crash of 2000. The fact that even this reversal didn’t dent his optimism and faith in technology is telling. But how long can it last?

The failure of Japan’s robots to deal with the immense challenge of Fukushima has sparked something of a crisis of conscience within the industry. Disaster response is an obvious stepping-stone technology for robots. Initially, producing a humanoid robot will be very costly, and the robot will be less capable than a human; building a robot to wait tables might not be particularly economical yet. Building a robot to do jobs that are too dangerous for humans is far more viable. Yet, at Fukushima, in one of the most advanced nations in the world, many of the robots weren’t up to the task.

Nowhere was this crisis more felt than Honda; the company had developed ASIMO, which stunned the world in 2000 and continues to fascinate as an iconic humanoid robot. Despite all this technological advancement, however, Honda knew that ASIMO was still too unreliable for the real world.

It was Fukushima that triggered a sea-change in Honda’s approach to robotics. Two years after the disaster, there were rumblings that Honda was developing a disaster robot, and in October 2017, the prototype was revealed to the public for the first time. It’s not yet ready for deployment in disaster zones, however. Interestingly, the creators chose not to give it dexterous hands but instead to assume that remotely-operated tools fitted to the robot would be a better solution for the range of circumstances it might encounter.

This shift in focus for humanoid robots away from entertainment and amusement like ASIMO, and towards being practically useful, has been mirrored across the world.

In 2015, also inspired by the Fukushima disaster and the lack of disaster-ready robots, the DARPA Robotics Challenge tested humanoid robots with a range of tasks that might be needed in emergency response, such as driving cars, opening doors, and climbing stairs. The Terminator-like ATLAS robot from Boston Dynamics, alongside Korean robot HUBO, took many of the plaudits, and CHIMP also put in an impressive display by being able to right itself after falling.

Yet the DARPA Robotics Challenge showed us just how far the robots are from truly being as useful as we’d like, or maybe even as we would imagine. Many robots took hours to complete the tasks, which were highly idealized to suit them. Climbing stairs proved a particular challenge. Those who watched were more likely to see a robot that had fallen over, struggling to get up, rather than heroic superbots striding in to save the day. The “striding” proved a particular problem, with the fastest robot HUBO managing this by resorting to wheels in its knees when the legs weren’t necessary.

Fukushima may have brought a sea-change over futuristic Japan, but before robots will really begin to enter our everyday lives, they will need to prove their worth. In the interim, aerial drone robots designed to examine infrastructure damage after disasters may well see earlier deployment and more success.

It’s a considerable challenge.

Building a humanoid robot is expensive; if these multi-million-dollar machines can’t help in a crisis, people may begin to question the worth of investing in them in the first place (unless your aim is just to make viral videos). This could lead to a further crisis of confidence among the Japanese, who are starting to rely on humanoid robotics as a solution to the crisis of the aging population. The Japanese government, as part of its robots strategy, has already invested $44 million in their development.

But if they continue to fail when put to the test, that will raise serious concerns. In Tokyo’s Akihabara district, you can see all kinds of flash robotic toys for sale in the neon-lit superstores, and dancing, acting robots like Robothespian can entertain crowds all over the world. But if we want these machines to be anything more than toys—partners, helpers, even saviors—more work needs to be done.

At the same time, those who participated in the DARPA Robotics Challenge in 2015 won’t be too concerned if people were underwhelmed by the performance of their disaster relief robots. Back in 2004, nearly every participant in the DARPA Grand Challenge crashed, caught fire, or failed on the starting line. To an outside observer, the whole thing would have seemed like an unmitigated disaster, and a pointless investment. What was the task in 2004? Developing a self-driving car. A lot can change in a decade.

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#432487 Can We Make a Musical Turing Test?

As artificial intelligence advances, we’re encountering the same old questions. How much of what we consider to be fundamentally human can be reduced to an algorithm? Can we create something sufficiently advanced that people can no longer distinguish between the two? This, after all, is the idea behind the Turing Test, which has yet to be passed.

At first glance, you might think music is beyond the realm of algorithms. Birds can sing, and people can compose symphonies. Music is evocative; it makes us feel. Very often, our intense personal and emotional attachments to music are because it reminds us of our shared humanity. We are told that creative jobs are the least likely to be automated. Creativity seems fundamentally human.

But I think above all, we view it as reductionist sacrilege: to dissect beautiful things. “If you try to strangle a skylark / to cut it up, see how it works / you will stop its heart from beating / you will stop its mouth from singing.” A human musician wrote that; a machine might be able to string words together that are happy or sad; it might even be able to conjure up a decent metaphor from the depths of some neural network—but could it understand humanity enough to produce art that speaks to humans?

Then, of course, there’s the other side of the debate. Music, after all, has a deeply mathematical structure; you can train a machine to produce harmonics. “In the teachings of Pythagoras and his followers, music was inseparable from numbers, which were thought to be the key to the whole spiritual and physical universe,” according to Grout in A History of Western Music. You might argue that the process of musical composition cannot be reduced to a simple algorithm, yet musicians have often done so. Mozart, with his “Dice Music,” used the roll of a dice to decide how to order musical fragments; creativity through an 18th-century random number generator. Algorithmic music goes back a very long way, with the first papers on the subject from the 1960s.

Then there’s the techno-enthusiast side of the argument. iTunes has 26 million songs, easily more than a century of music. A human could never listen to and learn from them all, but a machine could. It could also memorize every note of Beethoven. Music can be converted into MIDI files, a nice chewable data format that allows even a character-by-character neural net you can run on your computer to generate music. (Seriously, even I could get this thing working.)

Indeed, generating music in the style of Bach has long been a test for AI, and you can see neural networks gradually learn to imitate classical composers while trying to avoid overfitting. When an algorithm overfits, it essentially starts copying the existing music, rather than being inspired by it but creating something similar: a tightrope the best human artists learn to walk. Creativity doesn’t spring from nowhere; even maverick musical geniuses have their influences.

Does a machine have to be truly ‘creative’ to produce something that someone would find valuable? To what extent would listeners’ attitudes change if they thought they were hearing a human vs. an AI composition? This all suggests a musical Turing Test. Of course, it already exists. In fact, it’s run out of Dartmouth, the school that hosted that first, seminal AI summer conference. This year, the contest is bigger than ever: alongside the PoetiX, LimeriX and LyriX competitions for poetry and lyrics, there’s a DigiKidLit competition for children’s literature (although you may have reservations about exposing your children to neural-net generated content… it can get a bit surreal).

There’s also a pair of musical competitions, including one for original compositions in different genres. Key genres and styles are represented by Charlie Parker for Jazz and the Bach chorales for classical music. There’s also a free composition, and a contest where a human and an AI try to improvise together—the AI must respond to a human spontaneously, in real time, and in a musically pleasing way. Quite a challenge! In all cases, if any of the generated work is indistinguishable from human performers, the neural net has passed the Turing Test.

Did they? Here’s part of 2017’s winning sonnet from Charese Smiley and Hiroko Bretz:

The large cabin was in total darkness.
Come marching up the eastern hill afar.
When is the clock on the stairs dangerous?
Everything seemed so near and yet so far.
Behind the wall silence alone replied.
Was, then, even the staircase occupied?
Generating the rhymes is easy enough, the sentence structure a little trickier, but what’s impressive about this sonnet is that it sticks to a single topic and appears to be a more coherent whole. I’d guess they used associated “lexical fields” of similar words to help generate something coherent. In a similar way, most of the more famous examples of AI-generated music still involve some amount of human control, even if it’s editorial; a human will build a song around an AI-generated riff, or select the most convincing Bach chorale from amidst many different samples.

We are seeing strides forward in the ability of AI to generate human voices and human likenesses. As the latter example shows, in the fake news era people have focused on the dangers of this tech– but might it also be possible to create a virtual performer, trained on a dataset of their original music? Did you ever want to hear another Beatles album, or jam with Miles Davis? Of course, these things are impossible—but could we create a similar experience that people would genuinely value? Even, to the untrained eye, something indistinguishable from the real thing?

And if it did measure up to the real thing, what would this mean? Jaron Lanier is a fascinating technology writer, a critic of strong AI, and a believer in the power of virtual reality to change the world and provide truly meaningful experiences. He’s also a composer and a musical aficionado. He pointed out in a recent interview that translation algorithms, by reducing the amount of work translators are commissioned to do, have, in some sense, profited from stolen expertise. They were trained on huge datasets purloined from human linguists and translators. If you can train an AI on someone’s creative output and it produces new music, who “owns” it?

Although companies that offer AI music tools are starting to proliferate, and some groups will argue that the musical Turing test has been passed already, AI-generated music is hardly racing to the top of the pop charts just yet. Even as the line between human-composed and AI-generated music starts to blur, there’s still a gulf between the average human and musical genius. In the next few years, we’ll see how far the current techniques can take us. It may be the case that there’s something in the skylark’s song that can’t be generated by machines. But maybe not, and then this song might need an extra verse.

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#432482 This Week’s Awesome Stories From ...

A Brain-Boosting Prosthesis Moves From Rats to Humans
Robbie Gonzalez | WIRED
“Today, their proof-of-concept prosthetic lives outside a patient’s head and connects to the brain via wires. But in the future, Hampson hopes, surgeons could implant a similar apparatus entirely within a person’s skull, like a neural pacemaker. It could augment all manner of brain functions—not just in victims of dementia and brain injury, but healthy individuals, as well.”

Here’s How the US Needs to Prepare for the Age of Artificial Intelligence
Will Knight | MIT Technology Review
“The Trump administration has abandoned this vision and has no intention of devising its own AI plan, say those working there. They say there is no need for an AI moonshot, and that minimizing government interference is the best way to make sure the technology flourishes… That looks like a huge mistake. If it essentially ignores such a technological transformation, the US might never make the most of an opportunity to reboot its economy and kick-start both wage growth and job creation. Failure to plan could also cause the birthplace of AI to lose ground to international rivals.”

Underwater GPS Inspired by Shrimp Eyes
Jeremy Hsu | IEEE Spectrum
“A few years ago, U.S. and Australian researchers developed a special camera inspired by the eyes of mantis shrimp that can see the polarization patterns of light waves, which resemble those in a rope being waved up and down. That means the bio-inspired camera can detect how light polarization patterns change once the light enters the water and gets deflected or scattered.”

‘The Business of War’: Google Employees Protest Work for the Pentagon
Scott Shane and Daisuke Wakabayashi | The New York Times
“Thousands of Google employees, including dozens of senior engineers, have signed a letter protesting the company’s involvement in a Pentagon program that uses artificial intelligence to interpret video imagery and could be used to improve the targeting of drone strikes.

The letter, which is circulating inside Google and has garnered more than 3,100 signatures, reflects a culture clash between Silicon Valley and the federal government that is likely to intensify as cutting-edge artificial intelligence is increasingly employed for military purposes. ‘We believe that Google should not be in the business of war,’ says the letter, addressed to Sundar Pichai, the company’s chief executive. It asks that Google pull out of Project Maven, a Pentagon pilot program, and announce a policy that it will not ‘ever build warfare technology.’ (Read the text of the letter.)”

MIT’s New Headset Reads the ‘Words in Your Head’
Brian Heater | TechCrunch
“A team at MIT has been working on just such a device, though the hardware design, admittedly, doesn’t go too far toward removing that whole self-consciousness bit from the equation. AlterEgo is a headmounted—or, more properly, jaw-mounted—device that’s capable of reading neuromuscular signals through built-in electrodes. The hardware, as MIT puts it, is capable of reading ‘words in your head.’”

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#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.

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#432181 Putting AI in Your Pocket: MIT Chip Cuts ...

Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.

Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.

That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.

Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.

Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.

This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.

The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.

The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.

This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.

“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.

“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”

It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.

Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.

The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.

Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.

Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.

Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.

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