If you have a dog, you might think you have a strong connection with them. But according to a new study, you've probably been reading your pet's emotions all wrong. Although humans and dogs have a unique bond, scientists from Arizona State University say that we are terrible at understanding canine emotions. Participants were shown videos of a dog reacting to positive situations, such as seeing their lead, or negative situations such as being presented with the dreaded vacuum cleaner. Instead of actually trying to understand what the dog is feeling, the researchers found that people tend to'project human emotions onto their pets'.

Accurate estimation of a user's biological joint moment from wearable sensor data is vital for improving exoskeleton control during real-world locomotor tasks. However, most state-of-the-art methods rely on deep learning techniques that necessitate extensive in-lab data collection, posing challenges in acquiring sufficient data to develop robust models. To address this challenge, we introduce a locomotor task set optimization strategy designed to identify a minimal, yet representative, set of tasks that preserves model performance while significantly reducing the data collection burden. In this optimization, we performed a cluster analysis on the dimensionally reduced biomechanical features of various cyclic and non-cyclic tasks. We identified the minimal viable clusters (i.e., tasks) to train a neural network for estimating hip joint moments and evaluated its performance. Our cross-validation analysis across subjects showed that the optimized task set-based model achieved a root mean squared error of 0.30$\pm$0.05 Nm/kg. This performance was significantly better than using only cyclic tasks (p<0.05) and was comparable to using the full set of tasks. Our results demonstrate the ability to maintain model accuracy while significantly reducing the cost associated with data collection and model training. This highlights the potential for future exoskeleton designers to leverage this strategy to minimize the data requirements for deep learning-based models in wearable robot control.

"Oppenheimer" director Christopher Nolan spoke of the historical significance in artificial intelligence and compared it to the creation of the atomic bomb. 'OPPENHEIMER MOMENT': Hollywood director Christopher Nolan spoke with Fox News Digital on artificial intelligence's "Oppenheimer moment." Nolan compared AI to the creation of the atomic bomb and stated, "It's really the looking back through Oppenheimer's story and saying, 'Okay, what could have been done differently? What are the responsibilities of people who create technology that can go out and have unintended impacts?'" Continue reading… 'MINING OUR PERSONHOODS': Companies like OpenAI and Google have taken your data to train AI systems, attorney Ryan J. Clarkson writes in an op-ed. If you posted it, its most likely been taken.

AGI, while powerful, could have negative consequences, warned Diveplane CEO Mike Capps and Liberty Blockchain CCO Christopher Alexander. The House last week passed a defense policy bill that strongly encourages the Pentagon to use artificial intelligence to its advantage, but also requires defense officials to examine how America's national security infrastructure may be vulnerable to AI systems deployed by China, Russia and other adversaries. Rep. Marc Molinaro, R-N.Y., pushed to include language in the bill requiring an assessment of AI vulnerabilities, and watched it pass easily on the House floor. That's a strong sign the language will remain in the final bill even after a negotiation with the Senate, and Molinaro told Fox News Digital that this assessment is needed in the face of ever-evolving AI capabilities. "The average person knows at least the rudimentary use of AI. China, terrorists, Russia are using AI in a much more sophisticated way, certainly as aggressors," he told Fox news Digital.

A logic program with function symbols is called finitely ground if there is a finite propositional logic program whose stable models are exactly the same as the stable models of this program. Finite groundability is an important property for logic programs with function symbols because it makes feasible to compute such program’s stable models using traditional ASP solvers. In this paper, we introduce a new decidable class of finitely ground programs called POLY-bounded programs, which, to the best of our knowledge, strictly contains all decidable classes of finitely ground programs discovered so far in the literature. We also study the related complexity property for this class of programs. We prove that deciding whether a program is POLY-bounded is EXPTIMEcomplete.