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Making sure artificial intelligence does what we want and behaves in predictable ways will be crucial as the technology becomes increasingly ubiquitous. It’s an area frequently neglected in the race to develop products, but DeepMind has now outlined its research agenda to tackle the problem.
AI safety, as the field is known, has been gaining prominence in recent years. That’s probably at least partly down to the overzealous warnings of a coming AI apocalypse from well-meaning, but underqualified pundits like Elon Musk and Stephen Hawking. But it’s also recognition of the fact that AI technology is quickly pervading all aspects of our lives, making decisions on everything from what movies we watch to whether we get a mortgage.
That’s why DeepMind hired a bevy of researchers who specialize in foreseeing the unforeseen consequences of the way we built AI back in 2016. And now the team has spelled out the three key domains they think require research if we’re going to build autonomous machines that do what we want.
In a new blog designed to provide updates on the team’s work, they introduce the ideas of specification, robustness, and assurance, which they say will act as the cornerstones of their future research. Specification involves making sure AI systems do what their operator intends; robustness means a system can cope with changes to its environment and attempts to throw it off course; and assurance involves our ability to understand what systems are doing and how to control them.
A classic thought experiment designed to illustrate how we could lose control of an AI system can help illustrate the problem of specification. Philosopher Nick Bostrom’s posited a hypothetical machine charged with making as many paperclips as possible. Because the creators fail to add what they might assume are obvious additional goals like not harming people, the AI wipes out humanity so we can’t switch it off before turning all matter in the universe into paperclips.
Obviously the example is extreme, but it shows how a poorly-specified goal can lead to unexpected and disastrous outcomes. Properly codifying the desires of the designer is no easy feat, though; often there are not neat ways to encompass both the explicit and implicit goals in ways that are understandable to the machine and don’t leave room for ambiguities, meaning we often rely on incomplete approximations.
The researchers note recent research by OpenAI in which an AI was trained to play a boat-racing game called CoastRunners. The game rewards players for hitting targets laid out along the race route. The AI worked out that it could get a higher score by repeatedly knocking over regenerating targets rather than actually completing the course. The blog post includes a link to a spreadsheet detailing scores of such examples.
Another key concern for AI designers is making their creation robust to the unpredictability of the real world. Despite their superhuman abilities on certain tasks, most cutting-edge AI systems are remarkably brittle. They tend to be trained on highly-curated datasets and so can fail when faced with unfamiliar input. This can happen by accident or by design—researchers have come up with numerous ways to trick image recognition algorithms into misclassifying things, including thinking a 3D printed tortoise was actually a gun.
Building systems that can deal with every possible encounter may not be feasible, so a big part of making AIs more robust may be getting them to avoid risks and ensuring they can recover from errors, or that they have failsafes to ensure errors don’t lead to catastrophic failure.
And finally, we need to have ways to make sure we can tell whether an AI is performing the way we expect it to. A key part of assurance is being able to effectively monitor systems and interpret what they’re doing—if we’re basing medical treatments or sentencing decisions on the output of an AI, we’d like to see the reasoning. That’s a major outstanding problem for popular deep learning approaches, which are largely indecipherable black boxes.
The other half of assurance is the ability to intervene if a machine isn’t behaving the way we’d like. But designing a reliable off switch is tough, because most learning systems have a strong incentive to prevent anyone from interfering with their goals.
The authors don’t pretend to have all the answers, but they hope the framework they’ve come up with can help guide others working on AI safety. While it may be some time before AI is truly in a position to do us harm, hopefully early efforts like these will mean it’s built on a solid foundation that ensures it is aligned with our goals.
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Earth’s oceans are having a rough go of it these days. On top of being the repository for millions of tons of plastic waste, global warming is affecting the oceans and upsetting marine ecosystems in potentially irreversible ways.
Coral bleaching, for example, occurs when warming water temperatures or other stress factors cause coral to cast off the algae that live on them. The coral goes from lush and colorful to white and bare, and sometimes dies off altogether. This has a ripple effect on the surrounding ecosystem.
Warmer water temperatures have also prompted many species of fish to move closer to the north or south poles, disrupting fisheries and altering undersea environments.
To keep these issues in check or, better yet, try to address and improve them, it’s crucial for scientists to monitor what’s going on in the water. A paper released last week by a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled a new tool for studying marine life: a biomimetic soft robotic fish, dubbed SoFi, that can swim with, observe, and interact with real fish.
SoFi isn’t the first robotic fish to hit the water, but it is the most advanced robot of its kind. Here’s what sets it apart.
It swims in three dimensions
Up until now, most robotic fish could only swim forward at a given water depth, advancing at a steady speed. SoFi blows older models out of the water. It’s equipped with side fins called dive planes, which move to adjust its angle and allow it to turn, dive downward, or head closer to the surface. Its density and thus its buoyancy can also be adjusted by compressing or decompressing air in an inner compartment.
“To our knowledge, this is the first robotic fish that can swim untethered in three dimensions for extended periods of time,” said CSAIL PhD candidate Robert Katzschmann, lead author of the study. “We are excited about the possibility of being able to use a system like this to get closer to marine life than humans can get on their own.”
The team took SoFi to the Rainbow Reef in Fiji to test out its swimming skills, and the robo fish didn’t disappoint—it was able to swim at depths of over 50 feet for 40 continuous minutes. What keeps it swimming? A lithium polymer battery just like the one that powers our smartphones.
It’s remote-controlled… by Super Nintendo
SoFi has sensors to help it see what’s around it, but it doesn’t have a mind of its own yet. Rather, it’s controlled by a nearby scuba-diving human, who can send it commands related to speed, diving, and turning. The best part? The commands come from an actual repurposed (and waterproofed) Super Nintendo controller. What’s not to love?
Image Credit: MIT CSAIL
Previous robotic fish built by this team had to be tethered to a boat, so the fact that SoFi can swim independently is a pretty big deal. Communication between the fish and the diver was most successful when the two were less than 10 meters apart.
It looks real, sort of
SoFi’s side fins are a bit stiff, and its camera may not pass for natural—but otherwise, it looks a lot like a real fish. This is mostly thanks to the way its tail moves; a motor pumps water between two chambers in the tail, and as one chamber fills, the tail bends towards that side, then towards the other side as water is pumped into the other chamber. The result is a motion that closely mimics the way fish swim. Not only that, the hydraulic system can change the water flow to get different tail movements that let SoFi swim at varying speeds; its average speed is around half a body length (21.7 centimeters) per second.
Besides looking neat, it’s important SoFi look lifelike so it can blend in with marine life and not scare real fish away, so it can get close to them and observe them.
“A robot like this can help explore the reef more closely than current robots, both because it can get closer more safely for the reef and because it can be better accepted by the marine species.” said Cecilia Laschi, a biorobotics professor at the Sant’Anna School of Advanced Studies in Pisa, Italy.
Just keep swimming
It sounds like this fish is nothing short of a regular Nemo. But its creators aren’t quite finished yet.
They’d like SoFi to be able to swim faster, so they’ll work on improving the robo fish’s pump system and streamlining its body and tail design. They also plan to tweak SoFi’s camera to help it follow real fish.
“We view SoFi as a first step toward developing almost an underwater observatory of sorts,” said CSAIL director Daniela Rus. “It has the potential to be a new type of tool for ocean exploration and to open up new avenues for uncovering the mysteries of marine life.”
The CSAIL team plans to make a whole school of SoFis to help biologists learn more about how marine life is reacting to environmental changes.
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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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