Tag Archives: scientist
#437982 Superintelligent AI May Be Impossible to ...
It may be theoretically impossible for humans to control a superintelligent AI, a new study finds. Worse still, the research also quashes any hope for detecting such an unstoppable AI when it’s on the verge of being created.
Slightly less grim is the timetable. By at least one estimate, many decades lie ahead before any such existential computational reckoning could be in the cards for humanity.
Alongside news of AI besting humans at games such as chess, Go and Jeopardy have come fears that superintelligent machines smarter than the best human minds might one day run amok. “The question about whether superintelligence could be controlled if created is quite old,” says study lead author Manuel Alfonseca, a computer scientist at the Autonomous University of Madrid. “It goes back at least to Asimov’s First Law of Robotics, in the 1940s.”
The Three Laws of Robotics, first introduced in Isaac Asimov's 1942 short story “Runaround,” are as follows:
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
In 2014, philosopher Nick Bostrom, director of the Future of Humanity Institute at the University of Oxford, not only explored ways in which a superintelligent AI could destroy us but also investigated potential control strategies for such a machine—and the reasons they might not work.
Bostrom outlined two possible types of solutions of this “control problem.” One is to control what the AI can do, such as keeping it from connecting to the Internet, and the other is to control what it wants to do, such as teaching it rules and values so it would act in the best interests of humanity. The problem with the former is that Bostrom thought a supersmart machine could probably break free from any bonds we could make. With the latter, he essentially feared that humans might not be smart enough to train a superintelligent AI.
Now Alfonseca and his colleagues suggest it may be impossible to control a superintelligent AI, due to fundamental limits inherent to computing itself. They detailed their findings this month in the Journal of Artificial Intelligence Research.
The researchers suggested that any algorithm that sought to ensure a superintelligent AI cannot harm people had to first simulate the machine’s behavior to predict the potential consequences of its actions. This containment algorithm then would need to halt the supersmart machine if it might indeed do harm.
However, the scientists said it was impossible for any containment algorithm to simulate the AI’s behavior and predict with absolute certainty whether its actions might lead to harm. The algorithm could fail to correctly simulate the AI’s behavior or accurately predict the consequences of the AI’s actions and not recognize such failures.
“Asimov’s first law of robotics has been proved to be incomputable,” Alfonseca says, “and therefore unfeasible.”
We may not even know if we have created a superintelligent machine, the researchers say. This is a consequence of Rice’s theorem, which essentially states that one cannot in general figure anything out about what a computer program might output just by looking at the program, Alfonseca explains.
On the other hand, there’s no need to spruce up the guest room for our future robot overlords quite yet. Three important caveats to the research still leave plenty of uncertainty to the group’s predictions.
First, Alfonseca estimates AI’s moment of truth remains, he says, “At least two centuries in the future.”
Second, he says researchers do not know if so-called artificial general intelligence, also known as strong AI, is theoretically even feasible. “That is, a machine as intelligent as we are in an ample variety of fields,” Alfonseca explains.
Last, Alfonseca says, “We have not proved that superintelligences can never be controlled—only that they can’t always be controlled.”
Although it may not be possible to control a superintelligent artificial general intelligence, it should be possible to control a superintelligent narrow AI—one specialized for certain functions instead of being capable of a broad range of tasks like humans. “We already have superintelligences of this type,” Alfonseca says. “For instance, we have machines that can compute mathematics much faster than we can. This is [narrow] superintelligence, isn’t it?” Continue reading
Posted in Human Robots
#437872 AlphaFold Proves That AI Can Crack ...
Any successful implementation of artificial intelligence hinges on asking the right questions in the right way. That’s what the British AI company DeepMind (a subsidiary of Alphabet) accomplished when it used its neural network to tackle one of biology’s grand challenges, the protein-folding problem. Its neural net, known as AlphaFold, was able to predict the 3D structures of proteins based on their amino acid sequences with unprecedented accuracy.
AlphaFold’s predictions at the 14th Critical Assessment of protein Structure Prediction (CASP14) were accurate to within an atom’s width for most of the proteins. The competition consisted of blindly predicting the structure of proteins that have only recently been experimentally determined—with some still awaiting determination.
Called the building blocks of life, proteins consist of 20 different amino acids in various combinations and sequences. A protein's biological function is tied to its 3D structure. Therefore, knowledge of the final folded shape is essential to understanding how a specific protein works—such as how they interact with other biomolecules, how they may be controlled or modified, and so on. “Being able to predict structure from sequence is the first real step towards protein design,” says Janet M. Thornton, director emeritus of the European Bioinformatics Institute. It also has enormous benefits in understanding disease-causing pathogens. For instance, at the moment only about 18 of the 26 proteins in the SARS-CoV-2 virus are known.
Predicting a protein’s 3D structure is a computational nightmare. In 1969 Cyrus Levinthal estimated that there are 10300 possible conformational combinations for a single protein, which would take longer than the age of the known universe to evaluate by brute force calculation. AlphaFold can do it in a few days.
As scientific breakthroughs go, AlphaFold’s discovery is right up there with the likes of James Watson and Francis Crick’s DNA double-helix model, or, more recently, Jennifer Doudna and Emmanuelle Charpentier’s CRISPR-Cas9 genome editing technique.
How did a team that just a few years ago was teaching an AI to master a 3,000-year-old game end up training one to answer a question plaguing biologists for five decades? That, says Briana Brownell, data scientist and founder of the AI company PureStrategy, is the beauty of artificial intelligence: The same kind of algorithm can be used for very different things.
“Whenever you have a problem that you want to solve with AI,” she says, “you need to figure out how to get the right data into the model—and then the right sort of output that you can translate back into the real world.”
DeepMind’s success, she says, wasn’t so much a function of picking the right neural nets but rather “how they set up the problem in a sophisticated enough way that the neural network-based modeling [could] actually answer the question.”
AlphaFold showed promise in 2018, when DeepMind introduced a previous iteration of their AI at CASP13, achieving the highest accuracy among all participants. The team had trained its to model target shapes from scratch, without using previously solved proteins as templates.
For 2020 they deployed new deep learning architectures into the AI, using an attention-based model that was trained end-to-end. Attention in a deep learning network refers to a component that manages and quantifies the interdependence between the input and output elements, as well as between the input elements themselves.
The system was trained on public datasets of the approximately 170,000 known experimental protein structures in addition to databases with protein sequences of unknown structures.
“If you look at the difference between their entry two years ago and this one, the structure of the AI system was different,” says Brownell. “This time, they’ve figured out how to translate the real world into data … [and] created an output that could be translated back into the real world.”
Like any AI system, AlphaFold may need to contend with biases in the training data. For instance, Brownell says, AlphaFold is using available information about protein structure that has been measured in other ways. However, there are also many proteins with as yet unknown 3D structures. Therefore, she says, a bias could conceivably creep in toward those kinds of proteins that we have more structural data for.
Thornton says it’s difficult to predict how long it will take for AlphaFold’s breakthrough to translate into real-world applications.
“We only have experimental structures for about 10 per cent of the 20,000 proteins [in] the human body,” she says. “A powerful AI model could unveil the structures of the other 90 per cent.”
Apart from increasing our understanding of human biology and health, she adds, “it is the first real step toward… building proteins that fulfill a specific function. From protein therapeutics to biofuels or enzymes that eat plastic, the possibilities are endless.” Continue reading
Posted in Human Robots
#437824 Video Friday: These Giant Robots Are ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):
ACRA 2020 – December 8-10, 2020 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.
“Who doesn’t love giant robots?”
Luma, is a towering 8 metre snail which transforms spaces with its otherworldly presence. Another piece, Triffid, stands at 6 metres and its flexible end sweeps high over audiences’ heads like an enchanted plant. The movement of the creatures is inspired by the flexible, wiggling and contorting motions of the animal kingdom and is designed to provoke instinctive reactions and emotions from the people that meet them. Air Giants is a new creative robotic studio founded in 2020. They are based in Bristol, UK, and comprise a small team of artists, roboticists and software engineers. The studio is passionate about creating emotionally effective motion at a scale which is thought-provoking and transporting, as well as expanding the notion of what large robots can be used for.
Here’s a behind the scenes and more on how the creatures work.
[ Air Giants ]
Thanks Emma!
If the idea of submerging a very expensive sensor payload being submerged in a lake makes you as uncomfortable as it makes me, this is not the video for you.
[ ANYbotics ]
As the pandemic continues on, the measures due to this health crisis are increasingly stringent, and working from home continues to be promoted and solicited by many companies, Pepper will allow you to keep in touch with your relatives or even your colleagues.
[ Softbank ]
Fairly impressive footwork from Tencent Robotics.
Although, LittleDog was doing that like a decade ago:
[ Tencent ]
It's been long enough since I've been able to go out for boba tea that a robotic boba tea kiosk seems like a reasonable thing to get for my living room.
[ Bobacino ] via [ Gizmodo ]
Road construction and maintenance is challenging and dangerous work. Pioneer Industrial Systems has spent over twenty years designing custom robotic systems for industrial manufacturers around the world. These robotic systems greatly improve safety and increase efficiency. Now they’re taking that expertise on the road, with the Robotic Maintenance Vehicle. This base unit can be mounted on a truck or trailer, and utilizes various modules to perform a variety of road maintenance tasks.
[ Pioneer ]
Extend Robotics arm uses cloud-based teleoperation software, featuring human-like dexterity and intelligence, with multiple applications in healthcare, utilities and energy
[ Extend Robotics ]
ARC, short for “AI, Robot, Cloud,” includes the latest algorithms and high precision data required for human-robot coexistence. Now with ultra-low latency networks, many robots can simultaneously become smarter, just by connecting to ARC. “ARC Eye” serves as the eyes for all robots, accurately determining the current location and route even indoors where there is no GPS access. “ARC Brain” is the computing system shared simultaneously by all robots, which plans and processes movement, localization, and task performance for the robot.
[ Naver Labs ]
How can we re-imagine urban infrastructures with cutting-edge technologies? Listen to this webinar from Ger Baron, Amsterdam’s CTO, and Senseable City Lab’s researchers, on how MIT and Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) are reimagining Amsterdam’s canals with the first fleet of autonomous boats.
[ MIT ]
Join Guy Burroughes in this webinar recording to hear about Spot, the robot dog created by Boston Dynamics, and how RACE plan to use it in nuclear decommissioning and beyond.
[ UKAEA ]
This GRASP on Robotics seminar comes from Marco Pavone at Stanford University, “On Safe and Efficient Human-robot interactions via Multimodal Intent Modeling and Reachability-based Safety Assurance.”
In this talk I will present a decision-making and control stack for human-robot interactions by using autonomous driving as a motivating example. Specifically, I will first discuss a data-driven approach for learning multimodal interaction dynamics between robot-driven and human-driven vehicles based on recent advances in deep generative modeling. Then, I will discuss how to incorporate such a learned interaction model into a real-time, interaction-aware decision-making framework. The framework is designed to be minimally interventional; in particular, by leveraging backward reachability analysis, it ensures safety even when other cars defy the robot's expectations without unduly sacrificing performance. I will present recent results from experiments on a full-scale steer-by-wire platform, validating the framework and providing practical insights. I will conclude the talk by providing an overview of related efforts from my group on infusing safety assurances in robot autonomy stacks equipped with learning-based components, with an emphasis on adding structure within robot learning via control-theoretical and formal methods.
[ UPenn ]
Autonomous Systems Failures: Who is Legally and Morally Responsible? Sponsored by Northwestern University’s Law and Technology Initiative and AI@NU, the event was moderated by Dan Linna and included Northwestern Engineering's Todd Murphey, University of Washington Law Professor Ryan Calo, and Google Senior Research Scientist Madeleine Clare Elish.
[ Northwestern ] Continue reading
Posted in Human Robots