Benjamin Franklin and other citizen scientists are core parts of the American experiment. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Benjamin Franklin's kite experiment in 1752 was a pivotal scientific event, which demonstrated the connection between lightning and electricity. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. "We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness. At the Second Continental Congress of the American colonies, meeting in Philadelphia, Richard Henry Lee of Virginia proposed a motion on June 7, 1776, that proclaimed, "These United Colonies are, and of right ought to be, free and independent states."

Andrew Jackson's White House once hosted a cheese feeding frenzy The seventh president's farewell party featured 1,400 pounds of cheddar. In 1835, a New York dairy farmer sent President Andrew Jackson a 1,400-pound cheddar cheese to celebrate the president's second inauguration. Two years later, it was finally eaten. Breakthroughs, discoveries, and DIY tips sent every weekday. It's February 1837, and the White House is about to bear witness to one of the greatest feeding frenzies in this nation's proud history of competitive consumption.

Although Large Language Models (LLMs) show exceptional fluency, efforts persist to extract stronger reasoning capabilities from them. Drawing on search-based interpretations of LLM computation, this paper advances a systematic framework for understanding LLM reasoning and optimization. Namely, that enhancing reasoning is best achieved by structuring a multi-agent pipeline to ensure a traversal of the search space in a gradual, incremental, and sequential (GIS) manner. Stated succinctly, high-quality reasoning is a controlled, incremental search. To test this framework, we investigate the efficacy of recursive refinement (RR)--an iterative process of self-criticism, adversarial stress-testing, and integrating critical feedback--as a practical method for implementing GIS search. We designed an experiment comparing a simple, linear pipeline against a complex, explicitly structured pipeline leveraging a recursive refinement layer. The multi-agent models were constructed to reflect the historical personas of three US Founding Fathers (Hamilton, Jefferson, and Madison) using RAG-powered corpora and were prompted to generate responses to three contemporary political issues. Model performance was evaluated using a two-tiered approach: a quantitative score from an LLM arbiter agent and qualitative human judgment. Our results revealed that the complex model consistently outperformed the simple model across all nine test cases with an average arbiter-outputted score of 88.3 versus 71.7. The complex model's arguments were superior in analytical depth, structural nuance, and strategic framing. We conclude that recursive refinement is a robust architectural feature for enhancing LLM reasoning via GIS search.

The performance of a constraint model can often be improved by converting a subproblem into a single table constraint (referred to as tabulation). Finding subproblems to tabulate is traditionally a manual and time-intensive process, even for expert modellers. This paper presents TabID, an entirely automated method to identify promising subproblems for tabulation in constraint programming. We introduce a diverse set of heuristics designed to identify promising candidates for tabulation, aiming to improve solver performance. These heuristics are intended to encapsulate various factors that contribute to useful tabulation. We also present additional checks to limit the potential drawbacks of suboptimal tabulation. We comprehensively evaluate our approach using benchmark problems from existing literature that previously relied on manual identification by constraint programming experts of constraints to tabulate. We demonstrate that our automated identification and tabulation process achieves comparable, and in some cases improved results. We empirically evaluate the efficacy of our approach on a variety of solvers, including standard CP (Minion and Gecode), clause-learning CP (Chuffed and OR-Tools) and SAT solvers (Kissat). Our findings highlight the substantial potential of fully automated tabulation, suggesting its integration into automated model reformulation tools.

Conversational AI agents use Retrieval Augmented Generation (RAG) to provide verifiable document-grounded responses to user inquiries. However, many natural questions do not have good answers: about 25\% contain false assumptions~\cite{Yu2023:CREPE}, and over 50\% are ambiguous~\cite{DBLP:conf/emnlp/MinMHZ20}. RAG agents need high-quality data to improve their responses to confusing questions. This paper presents a novel guided hallucination-based method to efficiently generate a diverse set of borderline out-of-scope confusing questions for a given document corpus. We conduct an empirical comparative evaluation of several large language models as RAG agents to measure the accuracy of confusion detection and appropriate response generation. We contribute a benchmark dataset to the public domain.

In the wake of large language models, there has been a resurgence of claims and questions about the Turing test and its value for AI, which are reminiscent of decades of practical "Turing" tests. If AI were quantum physics, by now several "Schr\"odinger's" cats could have been killed. Better late than never, it is time for a historical reconstruction of Turing's beautiful thought experiment. In this paper I present a wealth of evidence, including new archival sources, give original answers to several open questions about Turing's 1950 paper, and address the core question of the value of Turing's test.

This week, the panel is joined by Slate writer and senior editor Sam Adams to dissect Poor Things, director Yorgos Lanthimos horny, steampunk Frankenstein tale about Bella Baxter (played by Emma Stone), a pregnant woman who commits suicide then is brought back to life by a brilliant scientist (Willem Dafoe), with an eccentric caveat: She now has the brain of her unborn fetus. Then, the three remember Norman Lear, the late television pioneer and American icon who died at the age of 101 and who was responsible for ushering in a new era of character-driven, comedic, topical, and morally serious TV with hit sitcoms like All in the Family, The Jeffersons, Maude, and One Day at a Time. Finally, they are joined by Slate's books and culture columnist, Laura Miller, who shares her top ten books of the year, and along with Dana, discusses the joys and challenges of year-end listmaking. In the exclusive Slate Plus segment, the panel reunites with Sam Adams to spoil Poor Things, detailing what is arguably the film's weakest portion: the final ten minutes. The deadline to submit is Wednesday, December 13.

We study a planning problem based on Plotting, a tile-matching puzzle video game published by Taito in 1989. The objective of this game is to remove a target number of coloured blocks from a grid by sequentially shooting blocks into the grid. Plotting features complex transitions after every shot: various blocks are affected directly, while others can be indirectly affected by gravity. We highlight the challenges of modelling Plotting with PDDL and of solving it with a grounding-based state-of-the-art planner.