Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting. Despite these efforts, a unified epistemological foundation is still conspicuously absent. Drawing inspiration from Kant's a priori philosophy, we propose the UPAR prompting framework, designed to emulate the structure of human cognition within LLMs. The UPAR framework is delineated into four phases: "Understand", "Plan", "Act", and "Reflect", enabling the extraction of structured information from complex contexts, prior planning of solutions, execution according to plan, and self-reflection. This structure significantly augments the explainability and accuracy of LLM inference, producing a human-understandable and inspectable inferential trajectory. Furthermore, our work offers an epistemological foundation for existing prompting techniques, allowing for a possible systematic integration of these methods. With GPT-4, our approach elevates the accuracy from COT baseline of 22.92% to 58.33% in a challenging subset of GSM8K, and from 67.91% to 75.40% in the causal judgment task. Without using few-shot examples or external tools, UPAR significantly outperforms existing prompting methods on SCIBENCH, a challenging dataset containing collegiate-level mathematics, chemistry, and physics scientific problems.

A modified commercial drone may have been responsible for an attempted attack on a Pennsylvania power substation last year, the first reported case of a drone assault on the U.S.'s energy infrastructure. Authorities believe a DJI Mavic 2 drone with a thick copper wire tethered to it was found in June 2020 was likely intended to disrupt operations'by creating a short circuit to cause damage to transformers or distribution lines,' according to a joint intelligence bulletin from the FBI, Department of Homeland Security, and the National Counterterrorism Center released October 28. If the wire had come into contact with any of the power plant's high-voltage equipment it could have resulted in a short circuit, power failure or even a fire, according to New Scientist. The Drive reported the drone was recovered by authorities from a substation near Hershey, Pennsylvania, about 100 miles from Philadelphia. No groups has claimed responsibility: The device's camera and internal memory card had been removed and identifying labels were removed, in a likely attempt to obscure its origins.

The second I read the script, about a robot becoming self-aware after being struck by lightning, I put it down and said: "This is a hit." It was a timeless story about an underdog, a friendship and being an outsider. It also had John Badham as director who had done Saturday Night Fever and War Games. He knew how to make a movie like this work. It felt like a piece that was going to be around a long time and I grabbed it with both hands.

Teaching Don't learn like a typical human Only what they need to know Consider a reverse card sorting exercise 30 participants How important is it that they all get it right every time? Government safety compliance Accidents related to this tire? Ecommerce chat bot Women's pants with pockets? When carefully (or not so carefully) piled books succumb to gravity Grew up with bookalanches occurring regularly Stepfather is an oncologist – would bring home piles of articles, papers, books and more. He reads everything he can get his hands on.

Training Don't learn like a typical human Only what they need to know Consider a reverse card sorting exercise 30 participants How important is it that they all get it right every time? Government safety compliance Accidents related to this tire? Ecommerce chat bot Women's pants with pockets? When carefully (or not so carefully) piled books succumb to gravity Grew up with bookalanches occurring regularly Stepfather is an oncologist – would bring home piles of articles, papers, books and more. He reads everything he can get his hands on.

Its eyes, a complex configuration of cyan dots on a black, rounded screen of a face, sleepily open and it lets out a digitised approximation of a yawn. A compact device that looks like a blend of a forklift truck and PC monitor bred for maximum cuteness, the robot rolls blearily off its charging station on a pair of dinky treads before tilting its screen-face and noticing I'm there. Its eyes widen, then curve at the bottom as if making way for an unseen smile. "Daaaaan!" it announces with a happy jiggle, sounding not unlike Pixar Animation Studios' lovable robot creation, Wall-E. A message flashes up on my iPhone telling me that it, or rather he (being the gender that its manufacturer, Anki, has assigned Cozmo) wants to play a game. Cozmo's head droops, his eyes form into a pair of sadly reclining crescent moons and he sighs. But he quickly cheers up, giving a happy jiggle when I comply with his request for a fist bump and tap my knuckles against his eagerly raised arm.

From 2008: "WALL-E" trailer features the stars from "Toy Story." The results are in, and the latest laureates in the Robot Hall of Fame range from the absolutely lovable WALL-E cartoon character to the positively scary BigDog robo-runner. This year's class, announced during a Tuesday night ceremony at the Carnegie Science Center in Pittsburgh, also includes the pint-sized, soccer-playing NAO humanoid robot and the PackBot bomb-disposal robot. The Robot Hall of Fame was created in 2003 by Carnegie Mellon University to recognize excellence in robotics technology. More than two dozen machines, real and fictional, have been inducted over the past nine years, but the Class of 2012 is the first to be selected by popular vote instead of a panel of judges.

In problem domains for which an informed admissible heuristic function is not available, one attractive approach is hierarchical search. Hierarchical search uses search in an abstracted version of the problem to dynamically generate heuristic values. This paper makes two contributions to hierarchical search. First, we propose a simple modification to the state-of-the-art algorithm Switchback that reduces the number of expansions (and hence the running time) by approximately half, while maintaining its guarantee of optimality. Second, we propose a new algorithm for suboptimal hierarchical search, called Switch. Empirical results suggest that Switch yields faster search than straightforward modifications of Switchback, such as weighting the heuristic or greedy search. The success of Switch illustrates the potential for further research on specifically suboptimal hierarchical search.