A downed drone employing AI raises the question of what the international community needs to do to ban such weapons.
Category: robotics/AI – Page 1,366
Humans are usually pretty good at recognizing when they get things wrong, but artificial intelligence systems are not. According to a new study, AI generally suffers from inherent limitations due to a century-old mathematical paradox.
Like some people, AI systems often have a degree of confidence that far exceeds their actual abilities. And like an overconfident person, many AI systems don’t know when they’re making mistakes. Sometimes it’s even more difficult for an AI system to realize when it’s making a mistake than to produce a correct result.
Researchers from the University of Cambridge and the University of Oslo say that instability is the Achilles’ heel of modern AI and that a mathematical paradox shows AI’s limitations. Neural networks, the state of the art tool in AI, roughly mimic the links between neurons in the brain. The researchers show that there are problems where stable and accurate neural networks exist, yet no algorithm can produce such a network. Only in specific cases can algorithms compute stable and accurate neural networks.
Scientists are increasingly concerned that the lack of reproducibility in research may lead to, among other things, inaccuracies that slow scientific output and diminished public trust in science. Now, a team of researchers reports that creating a prediction market, where artificially intelligent—AI—agents make predictions—or bet—on hypothetical replication studies, could lead to an explainable, scalable approach to estimate confidence in published scholarly work.
Replication of experiments and studies, a critical step in the scientific process, helps provide confidence in the results and indicates whether they can generalize across contexts, according to Sarah Rajtmajer, assistant professor in information sciences and technology, Penn State. As experiments have become more complex, costly and time consuming, scientists increasingly lack the resources for robust replication efforts—what is often referred to now by them as the “replication crisis.”
“As scientists, we want to do work, and we want to know that our work is good,” said Rajtmajer. “Our approach to help address the replication crisis is to use AI to help predict whether a finding would replicate if repeated and why.”
The MIT engineers behind the robot mini cheetah released a video showcasing its new running and terrain-traversing skills on Thursday.
Dude, what if everything around us was just … a hologram?
The thing is, it could be—and a University of Michigan physicist is using quantum computing and machine learning to better understand the idea, called holographic duality.
Holographic duality is a mathematical conjecture that connects theories of particles and their interactions with the theory of gravity. This conjecture suggests that the theory of gravity and the theory of particles are mathematically equivalent: what happens mathematically in the theory of gravity happens in the theory of particles, and vice versa.
Engineers at Ford’s Advanced Manufacturing Center have tasked the innovative robot on wheels – called Javier – with operating the 3D printers completely on its own. The autonomous process enables the 3D printer to run continuously with no human interaction needed, increasing throughput and reducing the cost of custom-printed products.
Ford says Javier is always on time, very precise in its movements, mostly spends its day doing nothing but 3D printing, only taking a “short break” to recharge the batteries. The company has achieved great accuracy with Javier, using its feedback to significantly reduce margins of error. The method can also be applied to a vast array of robots already working at the company to increase efficiency and reduce cost.
Ford has filed several patents for the technology in its drive to innovate. Javier can communicate with Ford’s 3D printer, something that isn’t necessarily as easy to pull off as it sounds. The robot does not require the use of a camera vision system to “see.”
