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Category: robotics/AI – Page 1246

DeepMind has predicted the structure of almost every protein so far catalogued by science, cracking one of the grand challenges of biology in just 18 months thanks to an artificial intelligence called AlphaFold. Researchers say that the work has already led to advances in combating malaria, antibiotic resistance and plastic waste, and could speed up the discovery of new drugs.

Determining the crumpled shapes of proteins based on their sequences of constituent amino acids has been a persistent problem for decades in biology. Some of these amino acids are attracted to others, some are repelled by water, and the chains form intricate shapes that are hard to accurately determine.

Thinking long-term to save the world Martin Rees at New Scientist Live this October.

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A team of researchers at Istituto Italiano di Tecnologia’s Bioinspired Soft Robotics Laboratory has developed a new pleat-based soft robotic actuator that can be used in a variety of sizes, down to just 1 centimeter. In their paper published in the journal Science Robotics, the group describes the technology behind their new actuator and how well it worked when they tested it under varied circumstances.

Engineers working on soft robotics projects have often found themselves constrained by standard pneumatic artificial muscle actuators, which tend to only work well at a given size due to the large number of complex parts. In this new effort, the researchers have added a new feature to such actuators that requires fewer parts, resulting in a smaller actuator.

Pneumatic artificial muscle actuators work by pumping air in and out of small balloon-like sacs, simulating activity. Not only do they expand and contract, but they are also bendable because they are made using resins. When used in conjunction with other parts, such as hands, the artificial muscles allow for gripping and twisting. To reduce the number of complex parts, the researchers adjusted the sacs by added pleats. This reduces the size of the sacs as air is withdrawn without having to add other parts, making them useful in much smaller devices. The researchers also used a resin that was more flexible than those typically used in such work.

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Training robots to complete tasks in the real-world can be a very time-consuming process, which involves building a fast and efficient simulator, performing numerous trials on it, and then transferring the behaviors learned during these trials to the real world. In many cases, however, the performance achieved in simulations does not match the one attained in the real-world, due to unpredictable changes in the environment or task.

Researchers at the University of California, Berkeley (UC Berkeley) have recently developed DayDreamer, a tool that could be used to train robots to complete tasks more effectively. Their approach, introduced in a paper pre-published on arXiv, is based on learning models of the world that allow robots to predict the outcomes of their movements and actions, reducing the need for extensive trial and error training in the real-world.

“We wanted to build robots that continuously learn directly in the real world, without having to create a simulation environment,” Danijar Hafner, one of the researchers who carried out the study, told TechXplore. “We had only learned world models of video games before, so it was super exciting to see that the same algorithm allows robots to quickly learn in the real world, too!”

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Circa 2015

New software being developed at MIT is proving able to autonomously repair software bugs by borrowing from other programs and across different programming languages, without requiring access to the source code. This could save developers thousands of hours of programming time and lead to much more stable software.

Bugs are the bane of the software developer’s life. The changes that must be made to fix them are often trivial, typically involving changing only a few lines of code, but the process of identifying exactly which lines need to be fixed can be a very time-consuming and often very frustrating process, particularly in larger projects.

But now, new software from MIT could take care of this, and more. The system, dubbed CodePhage, can fix bugs which have to do with variable checks, and could soon be expanded to fix many more types of mistakes. Remarkably, according to MIT researcher Stelios Sidiroglou-Douskos, the software can do this kind of dynamic code translation and transplant (dubbed “horizontal code transplant,” from the analogous process in genetics) without needing access to the source code and across different programming languages, by analyzing the executable file directly.

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What does the future of AI look like? Let’s try out some AI software that’s readily available for consumers and see how it holds up against the human brain.

🦾 AI can outperform humans. But at what cost? 👉 👉 https://cybernews.com/editorial/ai-can-outperform-humans-but-at-what-cost/

Whether you welcome our new AI overlords with open arms, or you’re a little terrified about what an AI future may look like, many say it’s not really a question of ‘if,’ but more of a question of ‘when.’

Okay, you’ve got AI technologies on a small scale to a grand scale. From Siri — self-driving cars, text generators — humanoid robots, but what really is the real threat? As far back as 2013, Oxford University (ironically) used a machine-learning algorithm to determine whether 702 different jobs throughout America could turn automated, this found that a whopping 47% could in fact be replaced by machines.

Continue reading “Watch: 🤖 🤖 Will AI become an “existential threat?”” | >