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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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Shopping is becoming less and less of a consumer experience—or, for many, less of a chore—as the list of things that can be bought online and delivered to our homes grows to include, well, almost anything you can think of. An Israeli startup is working to make shopping and deliveries even faster and cheaper—and they’re succeeding.
Last week, CommonSense Robotics announced the launch of its first autonomous micro-fulfillment center in Tel Aviv. The company claims the facility is the smallest of its type in the world at 6,000 square feet. For comparison’s sake—most fulfillment hubs that incorporate robotics are at least 120,000 square feet. Amazon’s upcoming facility in Bessemer, Alabama will be a massive 855,000 square feet.
The thing about a building whose square footage is in the hundred-thousands is, you can fit a lot of stuff inside it, but there aren’t many places you can fit the building itself, especially not in major urban areas. So most fulfillment centers are outside cities, which means more time and more money to get your Moroccan oil shampoo, or your vegetable garden starter kit, or your 100-pack of organic protein bars from that fulfillment center to your front door.
CommonSense Robotics built the Tel Aviv center in an area that was previously thought too small for warehouse infrastructure. “In order to fit our site into small, tight urban spaces, we’ve designed every single element of it to optimize for space efficiency,” said Avital Sterngold, VP of operations. Using a robotic sorting system that includes hundreds of robots, plus AI software that assigns them specific tasks, the facility can prepare orders in less than five minutes end-to-end.
It’s not all automated, though—there’s still some human labor in the mix. The robots fetch goods and bring them to a team of people, who then pack the individual orders.
CommonSense raised $20 million this year in a funding round led by Palo Alto-based Playground Global. The company hopes to expand its operations to the US and UK in 2019. Its business model is to charge retailers a fee for each order fulfilled, while maintaining ownership and operation of the fulfillment centers. The first retailers to jump on the bandwagon were Super-Pharm, a drugstore chain, and Rami Levy, a retail supermarket chain.
“Staying competitive in today’s market is anchored by delivering orders quickly and determining how to fulfill and deliver orders efficiently, which are always the most complex aspects of any ecommerce operation. With robotics, we will be able to fulfill and deliver orders in under one hour, all while saving costs on said fulfillment and delivery,” said Super-Pharm VP Yossi Cohen. “Before CommonSense Robotics, we offered our customers next-day home delivery. With this partnership, we are now able to offer our customers same-day delivery and will very soon be offering them one-hour delivery.”
Long live the instant gratification economy—and the increasingly sophisticated technology that’s enabling it.
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Artificial intelligence in space exploration is gathering momentum. Over the coming years, new missions look likely to be turbo-charged by AI as we voyage to comets, moons, and planets and explore the possibilities of mining asteroids.
“AI is already a game-changer that has made scientific research and exploration much more efficient. We are not just talking about a doubling but about a multiple of ten,” Leopold Summerer, Head of the Advanced Concepts and Studies Office at ESA, said in an interview with Singularity Hub.
The history of AI and space exploration is older than many probably think. It has already played a significant role in research into our planet, the solar system, and the universe. As computer systems and software have developed, so have AI’s potential use cases.
The Earth Observer 1 (EO-1) satellite is a good example. Since its launch in the early 2000s, its onboard AI systems helped optimize analysis of and response to natural occurrences, like floods and volcanic eruptions. In some cases, the AI was able to tell EO-1 to start capturing images before the ground crew were even aware that the occurrence had taken place.
Other satellite and astronomy examples abound. Sky Image Cataloging and Analysis Tool (SKICAT) has assisted with the classification of objects discovered during the second Palomar Sky Survey, classifying thousands more objects caught in low resolution than a human would be able to. Similar AI systems have helped astronomers to identify 56 new possible gravitational lenses that play a crucial role in connection with research into dark matter.
AI’s ability to trawl through vast amounts of data and find correlations will become increasingly important in relation to getting the most out of the available data. ESA’s ENVISAT produces around 400 terabytes of new data every year—but will be dwarfed by the Square Kilometre Array, which will produce around the same amount of data that is currently on the internet in a day.
AI Readying For Mars
AI is also being used for trajectory and payload optimization. Both are important preliminary steps to NASA’s next rover mission to Mars, the Mars 2020 Rover, which is, slightly ironically, set to land on the red planet in early 2021.
An AI known as AEGIS is already on the red planet onboard NASA’s current rovers. The system can handle autonomous targeting of cameras and choose what to investigate. However, the next generation of AIs will be able to control vehicles, autonomously assist with study selection, and dynamically schedule and perform scientific tasks.
Throughout his career, John Leif Jørgensen from DTU Space in Denmark has designed equipment and systems that have been on board about 100 satellites—and counting. He is part of the team behind the Mars 2020 Rover’s autonomous scientific instrument PIXL, which makes extensive use of AI. Its purpose is to investigate whether there have been lifeforms like stromatolites on Mars.
“PIXL’s microscope is situated on the rover’s arm and needs to be placed 14 millimetres from what we want it to study. That happens thanks to several cameras placed on the rover. It may sound simple, but the handover process and finding out exactly where to place the arm can be likened to identifying a building from the street from a picture taken from the roof. This is something that AI is eminently suited for,” he said in an interview with Singularity Hub.
AI also helps PIXL operate autonomously throughout the night and continuously adjust as the environment changes—the temperature changes between day and night can be more than 100 degrees Celsius, meaning that the ground beneath the rover, the cameras, the robotic arm, and the rock being studied all keep changing distance.
“AI is at the core of all of this work, and helps almost double productivity,” Jørgensen said.
First Mars, Then Moons
Mars is likely far from the final destination for AIs in space. Jupiter’s moons have long fascinated scientists. Especially Europa, which could house a subsurface ocean, buried beneath an approximately 10 km thick ice crust. It is one of the most likely candidates for finding life elsewhere in the solar system.
While that mission may be some time in the future, NASA is currently planning to launch the James Webb Space Telescope into an orbit of around 1.5 million kilometers from Earth in 2020. Part of the mission will involve AI-empowered autonomous systems overseeing the full deployment of the telescope’s 705-kilo mirror.
The distances between Earth and Europa, or Earth and the James Webb telescope, means a delay in communications. That, in turn, makes it imperative for the crafts to be able to make their own decisions. Examples from the Mars Rover project show that communication between a rover and Earth can take 20 minutes because of the vast distance. A Europa mission would see much longer communication times.
Both missions, to varying degrees, illustrate one of the most significant challenges currently facing the use of AI in space exploration. There tends to be a direct correlation between how well AI systems perform and how much data they have been fed. The more, the better, as it were. But we simply don’t have very much data to feed such a system about what it’s likely to encounter on a mission to a place like Europa.
Computing power presents a second challenge. A strenuous, time-consuming approval process and the risk of radiation mean that your computer at home would likely be more powerful than anything going into space in the near future. A 200 GHz processor, 256 megabytes of ram, and 2 gigabytes of memory sounds a lot more like a Nokia 3210 (the one you could use as an ice hockey puck without it noticing) than an iPhone X—but it’s actually the ‘brain’ that will be onboard the next rover.
Private Companies Taking Off
Private companies are helping to push those limitations. CB Insights charts 57 startups in the space-space, covering areas as diverse as natural resources, consumer tourism, R&D, satellites, spacecraft design and launch, and data analytics.
David Chew works as an engineer for the Japanese satellite company Axelspace. He explained how private companies are pushing the speed of exploration and lowering costs.
“Many private space companies are taking advantage of fall-back systems and finding ways of using parts and systems that traditional companies have thought of as non-space-grade. By implementing fall-backs, and using AI, it is possible to integrate and use parts that lower costs without adding risk of failure,” he said in an interview with Singularity Hub.
Terraforming Our Future Home
Further into the future, moonshots like terraforming Mars await. Without AI, these kinds of projects to adapt other planets to Earth-like conditions would be impossible.
Autonomous crafts are already terraforming here on Earth. BioCarbon Engineering uses drones to plant up to 100,000 trees in a single day. Drones first survey and map an area, then an algorithm decides the optimal locations for the trees before a second wave of drones carry out the actual planting.
As is often the case with exponential technologies, there is a great potential for synergies and convergence. For example with AI and robotics, or quantum computing and machine learning. Why not send an AI-driven robot to Mars and use it as a telepresence for scientists on Earth? It could be argued that we are already in the early stages of doing just that by using VR and AR systems that take data from the Mars rovers and create a virtual landscape scientists can walk around in and make decisions on what the rovers should explore next.
One of the biggest benefits of AI in space exploration may not have that much to do with its actual functions. Chew believes that within as little as ten years, we could see the first mining of asteroids in the Kuiper Belt with the help of AI.
“I think one of the things that AI does to space exploration is that it opens up a whole range of new possible industries and services that have a more immediate effect on the lives of people on Earth,” he said. “It becomes a relatable industry that has a real effect on people’s daily lives. In a way, space exploration becomes part of people’s mindset, and the border between our planet and the solar system becomes less important.”
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