Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much appreciated in chatbots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Chgk (che ge ka). The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! - "Own Game". We observe its linguistic features and the related QA-task. We conclude about perspectives of a QA competition based on the data set collected from this database.

Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current efficient answer correctness (AC) metrics do not align with human judgments, particularly verbose, free-form answers from large language models (LLMs). There are two challenges: a lack of diverse evaluation data and that models are too big and non-transparent; LLM-based scorers correlate better with humans, but this expensive task has only been tested on limited QA datasets. We rectify these issues by providing guidelines and datasets for evaluating machine QA adopted from human QA community. We also propose an efficient, low-resource, and interpretable QA evaluation method more stable than an exact match and neural methods.

Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current evaluation metrics to determine answer equivalence (AE) often do not align with human judgments, particularly more verbose, free-form answers from large language models (LLM). There are two challenges: a lack of data and that models are too big: LLM-based scorers can correlate better with human judges, but this task has only been tested on limited QA datasets, and even when available, update of the model is limited because LLMs are large and often expensive. We rectify both of these issues by providing clear and consistent guidelines for evaluating AE in machine QA adopted from professional human QA contests. We also introduce a combination of standard evaluation and a more efficient, robust, and lightweight discriminate AE classifier-based matching method (CFMatch, smaller than 1 MB), trained and validated to more accurately evaluate answer correctness in accordance with adopted expert AE rules that are more aligned with human judgments.

'Gutfeld!' guests discuss a Jeopardy question that used alleged murderer Brian Laundrie as the clue. A "Jeopardy!" contestant is going viral this week after making what many fans are considering one of the biggest blunders in the show's history. On Wednesday's episode, a woman named Karen had a huge lead over the other two contestants as they neared the end of the second round – she had earned $21,800, while her competitors had earned $7,100 and $6,400. When there were only a few clues left on the Double Jeopardy board, Karen found a Daily Double in the "Hans, Solo" category. If she had made a modest bet, she would have been sure to win the entire game after Final Jeopardy, as the other players couldn't possibly catch up to her lead.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Mattea Roach, a tutor from Toronto, Canada, had won $560,983 over the course of her winning streak. This image released by Sony Pictures Television shows Mattea Roach, a 23-year-old Canadian contestant on the game show "Jeopardy!" Heading into the final round of Friday's match, Roach was leading with $19,200 and wagered $3,001 on the Final Jeopardy question.

Remember IBM's Watson, the AI Jeopardy! A 2010 promotion proclaimed, "Watson understands natural language with all its ambiguity and complexity." However, as we saw when Watson subsequently failed spectacularly in its quest to "revolutionize medicine with artificial intelligence," a veneer of linguistic facility is not the same as actually comprehending human language. Natural language understanding has long been a major goal of AI research. At first, researchers tried to manually program everything a machine would need to make sense of news stories, fiction or anything else humans might write.

From chess to Jeopardy to e-sports, AI is increasingly beating humans at their own games. But that was never the ultimate goal. We meet the big players in the space, and we take a trip to an arcade. To make this episode, we also spoke to Natasha Regan, Actuary at RPC Tyche, Chess WIM and co-author of "Game Changer". This episode was reported by Jennifer Strong and Will Douglas Heaven and produced by Anthony Green, Emma Cillekens and Karen Hao. Our mix engineer is Garret Lang. Trebeck: Today we're announcing a Jeopardy competition unlike anything we have ever presented before. Jennifer: Ten years ago, the television quiz show Jeopardy unveiled a new player... Well, his name is Watson. Documentary Announcer: [music] Watson is an IBM computer designed to play Jeopardy. Watson understands natural language with all its ambiguity and complexity." Jennifer: And perhaps not surprisingly... given that playing Jeopardy is the thing it was designed to do… Watson was good.

From AI to 5G, tech that once seemed as if it belonged in the realm of science fiction is starting to impact our everyday lives. The next sci-fi crossover may well be quantum computers. Headlines on the accomplishments of supercomputers have popped up regularly in the past decade or so, with stories touting their help with issues ranging from predicting climate change and mapping the human bloodstream to defeating Jeopardy! Through the use of multidimensional representation, quantum computers leave supercomputers in the dust. In 2019, Google's quantum computer, Sycamore, took 200 seconds to perform a mathematical computation that would have taken IBM's Summit supercomputer 10,000 years.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. "Jeopardy!" is once again looking for a host. A nearly-exhaustive search for late host Alex Trebek's replacement began not long after his passing with a slew of guest hosts taking a swing at the gig. After months of consideration, executive producer Mike Richards was offered the reigns with actress Mayim Bialik taking over the show's spin-off events.

Artificial Intelligence or simply AI is the science of designing intelligent computer programs or machines. AI will change the world as we know it by making everyday tasks easier and more efficient. AI is already created by major developers like IBM but has not nearly reached its full potential. Regardless of the benefits of AI there are many concerns with what the creation of AI can lead to, some as drastic as humanity creating their own uncontrollable superiors to even a third World War. Artificial Intelligence has been an enduring concept since the fifties when Arthur Samuel created the first computer program that taught itself how to play checkers in 1952.