Tag Archives: environment
#433758 DeepMind’s New Research Plan to Make ...
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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#433728 AI Is Kicking Space Exploration into ...
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.
Examples Abound
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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#433634 This Robotic Skin Makes Inanimate ...
In Goethe’s poem “The Sorcerer’s Apprentice,” made world-famous by its adaptation in Disney’s Fantasia, a lazy apprentice, left to fetch water, uses magic to bewitch a broom into performing his chores for him. Now, new research from Yale has opened up the possibility of being able to animate—and automate—household objects by fitting them with a robotic skin.
Yale’s Soft Robotics lab, the Faboratory, is led by Professor Rebecca Kramer-Bottiglio, and has long investigated the possibilities associated with new kinds of manufacturing. While the typical image of a robot is hard, cold steel and rigid movements, soft robotics aims to create something more flexible and versatile. After all, the human body is made up of soft, flexible surfaces, and the world is designed for us. Soft, deformable robots could change shape to adapt to different tasks.
When designing a robot, key components are the robot’s sensors, which allow it to perceive its environment, and its actuators, the electrical or pneumatic motors that allow the robot to move and interact with its environment.
Consider your hand, which has temperature and pressure sensors, but also muscles as actuators. The omni-skins, as the Science Robotics paper dubs them, combine sensors and actuators, embedding them into an elastic sheet. The robotic skins are moved by pneumatic actuators or memory alloy that can bounce back into shape. If this is then wrapped around a soft, deformable object, moving the skin with the actuators can allow the object to crawl along a surface.
The key to the design here is flexibility: rather than adding chips, sensors, and motors into every household object to turn them into individual automatons, the same skin can be used for many purposes. “We can take the skins and wrap them around one object to perform a task—locomotion, for example—and then take them off and put them on a different object to perform a different task, such as grasping and moving an object,” said Kramer-Bottiglio. “We can then take those same skins off that object and put them on a shirt to make an active wearable device.”
The task is then to dream up applications for the omni-skins. Initially, you might imagine demanding a stuffed toy to fetch the remote control for you, or animating a sponge to wipe down kitchen surfaces—but this is just the beginning. The scientists attached the skins to a soft tube and camera, creating a worm-like robot that could compress itself and crawl into small spaces for rescue missions. The same skins could then be worn by a person to sense their posture. One could easily imagine this being adapted into a soft exoskeleton for medical or industrial purposes: for example, helping with rehabilitation after an accident or injury.
The initial motivating factor for creating the robots was in an environment where space and weight are at a premium, and humans are forced to improvise with whatever’s at hand: outer space. Kramer-Bottoglio originally began the work after NASA called out for soft robotics systems for use by astronauts. Instead of wasting valuable rocket payload by sending up a heavy metal droid like ATLAS to fetch items or perform repairs, soft robotic skins with modular sensors could be adapted for a range of different uses spontaneously.
By reassembling components in the soft robotic skin, a crumpled ball of paper could provide the chassis for a robot that performs repairs on the spaceship, or explores the lunar surface. The dynamic compression provided by the robotic skin could be used for g-suits to protect astronauts when they rapidly accelerate or decelerate.
“One of the main things I considered was the importance of multi-functionality, especially for deep space exploration where the environment is unpredictable. The question is: How do you prepare for the unknown unknowns? … Given the design-on-the-fly nature of this approach, it’s unlikely that a robot created using robotic skins will perform any one task optimally,” Kramer-Bottiglio said. “However, the goal is not optimization, but rather diversity of applications.”
There are still problems to resolve. Many of the videos of the skins indicate that they can rely on an external power supply. Creating new, smaller batteries that can power wearable devices has been a focus of cutting-edge materials science research for some time. Much of the lab’s expertise is in creating flexible, stretchable electronics that can be deformed by the actuators without breaking the circuitry. In the future, the team hopes to work on streamlining the production process; if the components could be 3D printed, then the skins could be created when needed.
In addition, robotic hardware that’s capable of performing an impressive range of precise motions is quite an advanced technology. The software to control those robots, and enable them to perform a variety of tasks, is quite another challenge. With soft robots, it can become even more complex to design that control software, because the body itself can change shape and deform as the robot moves. The same set of programmed motions, then, can produce different results depending on the environment.
“Let’s say I have a soft robot with four legs that crawls along the ground, and I make it walk up a hard slope,” Dr. David Howard, who works on robotics at CSIRO in Australia, explained to ABC.
“If I make that slope out of gravel and I give it the same control commands, the actual body is going to deform in a different way, and I’m not necessarily going to know what that is.”
Despite these and other challenges, research like that at the Faboratory still hopes to redefine how we think of robots and robotics. Instead of a robot that imitates a human and manipulates objects, the objects themselves will become programmable matter, capable of moving autonomously and carrying out a range of tasks. Futurists speculate about a world where most objects are automated to some degree and can assemble and repair themselves, or are even built entirely of tiny robots.
The tale of the Sorcerer’s Apprentice was first written in 1797, at the dawn of the industrial revolution, over a century before the word “robot” was even coined. Yet more and more roboticists aim to prove Arthur C Clarke’s maxim: any sufficiently advanced technology is indistinguishable from magic.
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