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#432236 Why Hasn’t AI Mastered Language ...
In the myth about the Tower of Babel, people conspired to build a city and tower that would reach heaven. Their creator observed, “And now nothing will be restrained from them, which they have imagined to do.” According to the myth, God thwarted this effort by creating diverse languages so that they could no longer collaborate.
In our modern times, we’re experiencing a state of unprecedented connectivity thanks to technology. However, we’re still living under the shadow of the Tower of Babel. Language remains a barrier in business and marketing. Even though technological devices can quickly and easily connect, humans from different parts of the world often can’t.
Translation agencies step in, making presentations, contracts, outsourcing instructions, and advertisements comprehensible to all intended recipients. Some agencies also offer “localization” expertise. For instance, if a company is marketing in Quebec, the advertisements need to be in Québécois French, not European French. Risk-averse companies may be reluctant to invest in these translations. Consequently, these ventures haven’t achieved full market penetration.
Global markets are waiting, but AI-powered language translation isn’t ready yet, despite recent advancements in natural language processing and sentiment analysis. AI still has difficulties processing requests in one language, without the additional complications of translation. In November 2016, Google added a neural network to its translation tool. However, some of its translations are still socially and grammatically odd. I spoke to technologists and a language professor to find out why.
“To Google’s credit, they made a pretty massive improvement that appeared almost overnight. You know, I don’t use it as much. I will say this. Language is hard,” said Michael Housman, chief data science officer at RapportBoost.AI and faculty member of Singularity University.
He explained that the ideal scenario for machine learning and artificial intelligence is something with fixed rules and a clear-cut measure of success or failure. He named chess as an obvious example, and noted machines were able to beat the best human Go player. This happened faster than anyone anticipated because of the game’s very clear rules and limited set of moves.
Housman elaborated, “Language is almost the opposite of that. There aren’t as clearly-cut and defined rules. The conversation can go in an infinite number of different directions. And then of course, you need labeled data. You need to tell the machine to do it right or wrong.”
Housman noted that it’s inherently difficult to assign these informative labels. “Two translators won’t even agree on whether it was translated properly or not,” he said. “Language is kind of the wild west, in terms of data.”
Google’s technology is now able to consider the entirety of a sentence, as opposed to merely translating individual words. Still, the glitches linger. I asked Dr. Jorge Majfud, Associate Professor of Spanish, Latin American Literature, and International Studies at Jacksonville University, to explain why consistently accurate language translation has thus far eluded AI.
He replied, “The problem is that considering the ‘entire’ sentence is still not enough. The same way the meaning of a word depends on the rest of the sentence (more in English than in Spanish), the meaning of a sentence depends on the rest of the paragraph and the rest of the text, as the meaning of a text depends on a larger context called culture, speaker intentions, etc.”
He noted that sarcasm and irony only make sense within this widened context. Similarly, idioms can be problematic for automated translations.
“Google translation is a good tool if you use it as a tool, that is, not to substitute human learning or understanding,” he said, before offering examples of mistranslations that could occur.
“Months ago, I went to buy a drill at Home Depot and I read a sign under a machine: ‘Saw machine.’ Right below it, the Spanish translation: ‘La máquina vió,’ which means, ‘The machine did see it.’ Saw, not as a noun but as a verb in the preterit form,” he explained.
Dr. Majfud warned, “We should be aware of the fragility of their ‘interpretation.’ Because to translate is basically to interpret, not just an idea but a feeling. Human feelings and ideas that only humans can understand—and sometimes not even we, humans, understand other humans.”
He noted that cultures, gender, and even age can pose barriers to this understanding and also contended that an over-reliance on technology is leading to our cultural and political decline. Dr. Majfud mentioned that Argentinean writer Julio Cortázar used to refer to dictionaries as “cemeteries.” He suggested that automatic translators could be called “zombies.”
Erik Cambria is an academic AI researcher and assistant professor at Nanyang Technological University in Singapore. He mostly focuses on natural language processing, which is at the core of AI-powered language translation. Like Dr. Majfud, he sees the complexity and associated risks. “There are so many things that we unconsciously do when we read a piece of text,” he told me. Reading comprehension requires multiple interrelated tasks, which haven’t been accounted for in past attempts to automate translation.
Cambria continued, “The biggest issue with machine translation today is that we tend to go from the syntactic form of a sentence in the input language to the syntactic form of that sentence in the target language. That’s not what we humans do. We first decode the meaning of the sentence in the input language and then we encode that meaning into the target language.”
Additionally, there are cultural risks involved with these translations. Dr. Ramesh Srinivasan, Director of UCLA’s Digital Cultures Lab, said that new technological tools sometimes reflect underlying biases.
“There tend to be two parameters that shape how we design ‘intelligent systems.’ One is the values and you might say biases of those that create the systems. And the second is the world if you will that they learn from,” he told me. “If you build AI systems that reflect the biases of their creators and of the world more largely, you get some, occasionally, spectacular failures.”
Dr. Srinivasan said translation tools should be transparent about their capabilities and limitations. He said, “You know, the idea that a single system can take languages that I believe are very diverse semantically and syntactically from one another and claim to unite them or universalize them, or essentially make them sort of a singular entity, it’s a misnomer, right?”
Mary Cochran, co-founder of Launching Labs Marketing, sees the commercial upside. She mentioned that listings in online marketplaces such as Amazon could potentially be auto-translated and optimized for buyers in other countries.
She said, “I believe that we’re just at the tip of the iceberg, so to speak, with what AI can do with marketing. And with better translation, and more globalization around the world, AI can’t help but lead to exploding markets.”
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#432190 In the Future, There Will Be No Limit to ...
New planets found in distant corners of the galaxy. Climate models that may improve our understanding of sea level rise. The emergence of new antimalarial drugs. These scientific advances and discoveries have been in the news in recent months.
While representing wildly divergent disciplines, from astronomy to biotechnology, they all have one thing in common: Artificial intelligence played a key role in their scientific discovery.
One of the more recent and famous examples came out of NASA at the end of 2017. The US space agency had announced an eighth planet discovered in the Kepler-90 system. Scientists had trained a neural network—a computer with a “brain” modeled on the human mind—to re-examine data from Kepler, a space-borne telescope with a four-year mission to seek out new life and new civilizations. Or, more precisely, to find habitable planets where life might just exist.
The researchers trained the artificial neural network on a set of 15,000 previously vetted signals until it could identify true planets and false positives 96 percent of the time. It then went to work on weaker signals from nearly 700 star systems with known planets.
The machine detected Kepler 90i—a hot, rocky planet that orbits its sun about every two Earth weeks—through a nearly imperceptible change in brightness captured when a planet passes a star. It also found a sixth Earth-sized planet in the Kepler-80 system.
AI Handles Big Data
The application of AI to science is being driven by three great advances in technology, according to Ross King from the Manchester Institute of Biotechnology at the University of Manchester, leader of a team that developed an artificially intelligent “scientist” called Eve.
Those three advances include much faster computers, big datasets, and improved AI methods, King said. “These advances increasingly give AI superhuman reasoning abilities,” he told Singularity Hub by email.
AI systems can flawlessly remember vast numbers of facts and extract information effortlessly from millions of scientific papers, not to mention exhibit flawless logical reasoning and near-optimal probabilistic reasoning, King says.
AI systems also beat humans when it comes to dealing with huge, diverse amounts of data.
That’s partly what attracted a team of glaciologists to turn to machine learning to untangle the factors involved in how heat from Earth’s interior might influence the ice sheet that blankets Greenland.
Algorithms juggled 22 geologic variables—such as bedrock topography, crustal thickness, magnetic anomalies, rock types, and proximity to features like trenches, ridges, young rifts, and volcanoes—to predict geothermal heat flux under the ice sheet throughout Greenland.
The machine learning model, for example, predicts elevated heat flux upstream of Jakobshavn Glacier, the fastest-moving glacier in the world.
“The major advantage is that we can incorporate so many different types of data,” explains Leigh Stearns, associate professor of geology at Kansas University, whose research takes her to the polar regions to understand how and why Earth’s great ice sheets are changing, questions directly related to future sea level rise.
“All of the other models just rely on one parameter to determine heat flux, but the [machine learning] approach incorporates all of them,” Stearns told Singularity Hub in an email. “Interestingly, we found that there is not just one parameter…that determines the heat flux, but a combination of many factors.”
The research was published last month in Geophysical Research Letters.
Stearns says her team hopes to apply high-powered machine learning to characterize glacier behavior over both short and long-term timescales, thanks to the large amounts of data that she and others have collected over the last 20 years.
Emergence of Robot Scientists
While Stearns sees machine learning as another tool to augment her research, King believes artificial intelligence can play a much bigger role in scientific discoveries in the future.
“I am interested in developing AI systems that autonomously do science—robot scientists,” he said. Such systems, King explained, would automatically originate hypotheses to explain observations, devise experiments to test those hypotheses, physically run the experiments using laboratory robotics, and even interpret the results. The conclusions would then influence the next cycle of hypotheses and experiments.
His AI scientist Eve recently helped researchers discover that triclosan, an ingredient commonly found in toothpaste, could be used as an antimalarial drug against certain strains that have developed a resistance to other common drug therapies. The research was published in the journal Scientific Reports.
Automation using artificial intelligence for drug discovery has become a growing area of research, as the machines can work orders of magnitude faster than any human. AI is also being applied in related areas, such as synthetic biology for the rapid design and manufacture of microorganisms for industrial uses.
King argues that machines are better suited to unravel the complexities of biological systems, with even the most “simple” organisms are host to thousands of genes, proteins, and small molecules that interact in complicated ways.
“Robot scientists and semi-automated AI tools are essential for the future of biology, as there are simply not enough human biologists to do the necessary work,” he said.
Creating Shockwaves in Science
The use of machine learning, neural networks, and other AI methods can often get better results in a fraction of the time it would normally take to crunch data.
For instance, scientists at the National Center for Supercomputing Applications, located at the University of Illinois at Urbana-Champaign, have a deep learning system for the rapid detection and characterization of gravitational waves. Gravitational waves are disturbances in spacetime, emanating from big, high-energy cosmic events, such as the massive explosion of a star known as a supernova. The “Holy Grail” of this type of research is to detect gravitational waves from the Big Bang.
Dubbed Deep Filtering, the method allows real-time processing of data from LIGO, a gravitational wave observatory comprised of two enormous laser interferometers located thousands of miles apart in California and Louisiana. The research was published in Physics Letters B. You can watch a trippy visualization of the results below.
In a more down-to-earth example, scientists published a paper last month in Science Advances on the development of a neural network called ConvNetQuake to detect and locate minor earthquakes from ground motion measurements called seismograms.
ConvNetQuake uncovered 17 times more earthquakes than traditional methods. Scientists say the new method is particularly useful in monitoring small-scale seismic activity, which has become more frequent, possibly due to fracking activities that involve injecting wastewater deep underground. You can learn more about ConvNetQuake in this video:
King says he believes that in the long term there will be no limit to what AI can accomplish in science. He and his team, including Eve, are currently working on developing cancer therapies under a grant from DARPA.
“Robot scientists are getting smarter and smarter; human scientists are not,” he says. “Indeed, there is arguably a case that human scientists are less good. I don’t see any scientist alive today of the stature of a Newton or Einstein—despite the vast number of living scientists. The Physics Nobel [laureate] Frank Wilczek is on record as saying (10 years ago) that in 100 years’ time the best physicist will be a machine. I agree.”
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#431906 Low-Cost Soft Robot Muscles Can Lift 200 ...
Jerky mechanical robots are staples of science fiction, but to seamlessly integrate into everyday life they’ll need the precise yet powerful motor control of humans. Now scientists have created a new class of artificial muscles that could soon make that a reality.
The advance is the latest breakthrough in the field of soft robotics. Scientists are increasingly designing robots using soft materials that more closely resemble biological systems, which can be more adaptable and better suited to working in close proximity to humans.
One of the main challenges has been creating soft components that match the power and control of the rigid actuators that drive mechanical robots—things like motors and pistons. Now researchers at the University of Colorado Boulder have built a series of low-cost artificial muscles—as little as 10 cents per device—using soft plastic pouches filled with electrically insulating liquids that contract with the force and speed of mammalian skeletal muscles when a voltage is applied to them.
Three different designs of the so-called hydraulically amplified self-healing electrostatic (HASEL) actuators were detailed in two papers in the journals Science and Science Robotics last week. They could carry out a variety of tasks, from gently picking up delicate objects like eggs or raspberries to lifting objects many times their own weight, such as a gallon of water, at rapid repetition rates.
“We draw our inspiration from the astonishing capabilities of biological muscle,” Christoph Keplinger, an assistant professor at UC Boulder and senior author of both papers, said in a press release. “Just like biological muscle, HASEL actuators can reproduce the adaptability of an octopus arm, the speed of a hummingbird and the strength of an elephant.”
The artificial muscles work by applying a voltage to hydrogel electrodes on either side of pouches filled with liquid insulators, which can be as simple as canola oil. This creates an attraction between the two electrodes, pulling them together and displacing the liquid. This causes a change of shape that can push or pull levers, arms or any other articulated component.
The design is essentially a synthesis of two leading approaches to actuating soft robots. Pneumatic and hydraulic actuators that pump fluids around have been popular due to their high forces, easy fabrication and ability to mimic a variety of natural motions. But they tend to be bulky and relatively slow.
Dielectric elastomer actuators apply an electric field across a solid insulating layer to make it flex. These can mimic the responsiveness of biological muscle. But they are not very versatile and can also fail catastrophically, because the high voltages required can cause a bolt of electricity to blast through the insulator, destroying it. The likelihood of this happening increases in line with the size of their electrodes, which makes it hard to scale them up. By combining the two approaches, researchers get the best of both worlds, with the power, versatility and easy fabrication of a fluid-based system and the responsiveness of electrically-powered actuators.
One of the designs holds particular promise for robotics applications, as it behaves a lot like biological muscle. The so-called Peano-HASEL actuators are made up of multiple rectangular pouches connected in series, which allows them to contract linearly, just like real muscle. They can lift more than 200 times their weight, but being electrically powered, they exceed the flexing speed of human muscle.
As the name suggests, the HASEL actuators are also self-healing. They are still prone to the same kind of electrical damage as dielectric elastomer actuators, but the liquid insulator is able to immediately self-heal by redistributing itself and regaining its insulating properties.
The muscles can even monitor the amount of strain they’re under to provide the same kind of feedback biological systems would. The muscle’s capacitance—its ability to store an electric charge—changes as the device stretches, which makes it possible to power the arm while simultaneously measuring what position it’s in.
The researchers say this could imbue robots with a similar sense of proprioception or body-awareness to that found in plants and animals. “Self-sensing allows for the development of closed-loop feedback controllers to design highly advanced and precise robots for diverse applications,” Shane Mitchell, a PhD student in Keplinger’s lab and an author on both papers, said in an email.
The researchers say the high voltages required are an ongoing challenge, though they’ve already designed devices in the lab that use a fifth of the voltage of those features in the recent papers.
In most of their demonstrations, these soft actuators were being used to power rigid arms and levers, pointing to the fact that future robots are likely to combine both rigid and soft components, much like animals do. The potential applications for the technology range from more realistic prosthetics to much more dextrous robots that can work easily alongside humans.
It will take some work before these devices appear in commercial robots. But the combination of high-performance with simple and inexpensive fabrication methods mean other researchers are likely to jump in, so innovation could be rapid.
Image Credit: Keplinger Research Group/University of Colorado Continue reading
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#431900 Artificial muscles power up with new ...
Scientists are one step closer to artificial muscles. Orthotics have come a long way since their initial wood and strap designs, yet innovation lapsed when it came to compensating for muscle power—until now. A collaborative research team has designed a wearable robot to support a person's hip joint while walking. The team, led by Minoru Hashimoto, a professor of textile science and technology at Shinshu University in Japan, published the details of their prototype in Smart Materials and Structures, a journal published by the Institute of Physics. Continue reading
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