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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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#431828 This Self-Driving AI Is Learning to ...
I don’t have to open the doors of AImotive’s white 2015 Prius to see that it’s not your average car. This particular Prius has been christened El Capitan, the name written below the rear doors, and two small cameras are mounted on top of the car. Bundles of wire snake out from them, as well as from the two additional cameras on the car’s hood and trunk.
Inside is where things really get interesting, though. The trunk holds a computer the size of a microwave, and a large monitor covers the passenger glove compartment and dashboard. The center console has three switches labeled “Allowed,” “Error,” and “Active.”
Budapest-based AImotive is working to provide scalable self-driving technology alongside big players like Waymo and Uber in the autonomous vehicle world. On a highway test ride with CEO Laszlo Kishonti near the company’s office in Mountain View, California, I got a glimpse of just how complex that world is.
Camera-Based Feedback System
AImotive’s approach to autonomous driving is a little different from that of some of the best-known systems. For starters, they’re using cameras, not lidar, as primary sensors. “The traffic system is visual and the cost of cameras is low,” Kishonti said. “A lidar can recognize when there are people near the car, but a camera can differentiate between, say, an elderly person and a child. Lidar’s resolution isn’t high enough to recognize the subtle differences of urban driving.”
Image Credit: AImotive
The company’s aiDrive software uses data from the camera sensors to feed information to its algorithms for hierarchical decision-making, grouped under four concurrent activities: recognition, location, motion, and control.
Kishonti pointed out that lidar has already gotten more cost-efficient, and will only continue to do so.
“Ten years ago, lidar was best because there wasn’t enough processing power to do all the calculations by AI. But the cost of running AI is decreasing,” he said. “In our approach, computer vision and AI processing are key, and for safety, we’ll have fallback sensors like radar or lidar.”
aiDrive currently runs on Nvidia chips, which Kishonti noted were originally designed for graphics, and are not terribly efficient given how power-hungry they are. “We’re planning to substitute lower-cost, lower-energy chips in the next six months,” he said.
Testing in Virtual Reality
Waymo recently announced its fleet has now driven four million miles autonomously. That’s a lot of miles, and hard to compete with. But AImotive isn’t trying to compete, at least not by logging more real-life test miles. Instead, the company is doing 90 percent of its testing in virtual reality. “This is what truly differentiates us from competitors,” Kishonti said.
He outlined the three main benefits of VR testing: it can simulate scenarios too dangerous for the real world (such as hitting something), too costly (not every company has Waymo’s funds to run hundreds of cars on real roads), or too time-consuming (like waiting for rain, snow, or other weather conditions to occur naturally and repeatedly).
“Real-world traffic testing is very skewed towards the boring miles,” he said. “What we want to do is test all the cases that are hard to solve.”
On a screen that looked not unlike multiple games of Mario Kart, he showed me the simulator. Cartoon cars cruised down winding streets, outfitted with all the real-world surroundings: people, trees, signs, other cars. As I watched, a furry kangaroo suddenly hopped across one screen. “Volvo had an issue in Australia,” Kishonti explained. “A kangaroo’s movement is different than other animals since it hops instead of running.” Talk about cases that are hard to solve.
AImotive is currently testing around 1,000 simulated scenarios every night, with a steadily-rising curve of successful tests. These scenarios are broken down into features, and the car’s behavior around those features fed into a neural network. As the algorithms learn more features, the level of complexity the vehicles can handle goes up.
On the Road
After Kishonti and his colleagues filled me in on the details of their product, it was time to test it out. A safety driver sat in the driver’s seat, a computer operator in the passenger seat, and Kishonti and I in back. The driver maintained full control of the car until we merged onto the highway. Then he flicked the “Allowed” switch, his copilot pressed the “Active” switch, and he took his hands off the wheel.
What happened next, you ask?
A few things. El Capitan was going exactly the speed limit—65 miles per hour—which meant all the other cars were passing us. When a car merged in front of us or cut us off, El Cap braked accordingly (if a little abruptly). The monitor displayed the feed from each of the car’s cameras, plus multiple data fields and a simulation where a blue line marked the center of the lane, measured by the cameras tracking the lane markings on either side.
I noticed El Cap wobbling out of our lane a bit, but it wasn’t until two things happened in a row that I felt a little nervous: first we went under a bridge, then a truck pulled up next to us, both bridge and truck casting a complete shadow over our car. At that point El Cap lost it, and we swerved haphazardly to the right, narrowly missing the truck’s rear wheels. The safety driver grabbed the steering wheel and took back control of the car.
What happened, Kishonti explained, was that the shadows made it hard for the car’s cameras to see the lane markings. This was a new scenario the algorithm hadn’t previously encountered. If we’d only gone under a bridge or only been next to the truck for a second, El Cap may not have had so much trouble, but the two events happening in a row really threw the car for a loop—almost literally.
“This is a new scenario we’ll add to our testing,” Kishonti said. He added that another way for the algorithm to handle this type of scenario, rather than basing its speed and positioning on the lane markings, is to mimic nearby cars. “The human eye would see that other cars are still moving at the same speed, even if it can’t see details of the road,” he said.
After another brief—and thankfully uneventful—hands-off cruise down the highway, the safety driver took over, exited the highway, and drove us back to the office.
Driving into the Future
I climbed out of the car feeling amazed not only that self-driving cars are possible, but that driving is possible at all. I squint when driving into a tunnel, swerve to avoid hitting a stray squirrel, and brake gradually at stop signs—all without consciously thinking to do so. On top of learning to steer, brake, and accelerate, self-driving software has to incorporate our brains’ and bodies’ unconscious (but crucial) reactions, like our pupils dilating to let in more light so we can see in a tunnel.
Despite all the progress of machine learning, artificial intelligence, and computing power, I have a wholly renewed appreciation for the thing that’s been in charge of driving up till now: the human brain.
Kishonti seemed to feel similarly. “I don’t think autonomous vehicles in the near future will be better than the best drivers,” he said. “But they’ll be better than the average driver. What we want to achieve is safe, good-quality driving for everyone, with scalability.”
AImotive is currently working with American tech firms and with car and truck manufacturers in Europe, China, and Japan.
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