Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Why catch a fly ball with your hands when you can catch one with your leg? TikTok user Shannon Frandreis amazed her fellow fans when she caught a fly ball at a recent Chicago White Sox game with her prosthetic leg. The surrounding crowd stood up and cheered for Frandreis as she hoisted the leg up in the air, celebrating with a big smile on her face. Chicago White Sox's Yoan Moncada, left, celebrates with third base coach Joe McEwing after hitting a two-run home run during the eighth inning of a baseball game against the Detroit Tigers in Chicago, Saturday, Oct. 2, 2021.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Jose Siri and Chas McCormick hit back-to-back home runs in the eighth inning, rallying the AL West-leading Houston Astros over the Arizona Diamondbacks 7-6 on Sunday. Carlos Correa also homered as the Astros held their comfortable division lead over Oakland. Houston won for the fourth time in five games and cut Tampa Bay's lead for the best record in the AL to 3 ½ games.
Large pre-trained language models have repeatedly shown their ability to produce fluent text. Yet even when starting from a prompt, generation can continue in many plausible directions. Current decoding methods with the goal of controlling generation, e.g., to ensure specific words are included, either require additional models or fine-tuning, or work poorly when the task at hand is semantically unconstrained, e.g., story generation. In this work, we present a plug-and-play decoding method for controlled language generation that is so simple and intuitive, it can be described in a single sentence: given a topic or keyword, we add a shift to the probability distribution over our vocabulary towards semantically similar words. We show how annealing this distribution can be used to impose hard constraints on language generation, something no other plug-and-play method is currently able to do with SOTA language generators. Despite the simplicity of this approach, we see it works incredibly well in practice: decoding from GPT-2 leads to diverse and fluent sentences while guaranteeing the appearance of given guide words. We perform two user studies, revealing that (1) our method outperforms competing methods in human evaluations; and (2) forcing the guide words to appear in the generated text has no impact on the fluency of the generated text.
Can machines learn to use a search engine as an interactive tool for finding information? That would have far reaching consequences for making the world's knowledge more accessible. This paper presents first steps in designing agents that learn meta-strategies for contextual query refinements. Our approach uses machine reading to guide the selection of refinement terms from aggregated search results. Agents are then empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results. We develop a novel way of generating synthetic search sessions, which leverages the power of transformer-based generative language models through (self-)supervised learning. We also present a reinforcement learning agent with dynamically constrained actions that can learn interactive search strategies completely from scratch. In both cases, we obtain significant improvements over one-shot search with a strong information retrieval baseline. Finally, we provide an in-depth analysis of the learned search policies.
Grown men wearing tights like to yell terrible things at Fred DeJesus. DeJesus is an umpire in the outer constellations of professional baseball, where he's been spat on and, once, challenged to a postgame fight in a parking lot. He was born in Bushwick, Brooklyn, to Puerto Rican parents, stands five feet three, and is shaped, in his chest protector, like a fire hydrant; he once ejected a player for saying that he suffered from "little-man syndrome." Two years ago, DeJesus became the first umpire in a regular-season game anywhere to use something called the Automated Ball-Strike System. Most players refer to it as the "robo-umpire."
People are increasingly getting onto those banned no-fly types of lists, which could happen with ... [ ] self-driving cars too. People keep getting banned for doing the darndest and seemingly dumbest of acts. Oftentimes getting banned for the rest of their entire life. You might have heard or seen the recent brouhaha in major league baseball when a spectator in Yankee Stadium seated above leftfield opted to throw a baseball down onto the field that then struck the Boston Red Sox player Alex Verdugo in the back. He was not hurt, but you can imagine the personal dismay and shock at suddenly and unexpectedly having a projectile strike him from behind, seemingly out of nowhere. Turns out that Alex had earlier tossed the same baseball up into the stands as a memento for a young Red Sox cheering attendee. By some boorish grabbing, it had ended up in the hands of a New York Yankees fan. Next, after some hysterical urging by other frenetic Yankees to toss it back, the young man did so. Whether this act of defiance was intentionally devised to smack the left-fielder is still unclear and it could have been a happenstance rather than a purposeful aim.
'Gutfeld!' panel debates whether CNN will change their coverage This is a rush transcript from "Gutfeld!," This copy may not be in its final form and may be updated. I want to protect free speech. No, we want people to be protected from disinformation, to be protected from dying in this country, to be protected from people like Donald Trump who spread this information for -- who love to make sure that the division and the death continues. That was a rough weekend, and not just for Kat. But at least she kept her clothes on unlike our other guests, Jimmy Failla. But it was a far worse weekend for CNN. First let's go to our roly-poly guacamole gossip goalie. See how bad it got unreliable fart noises. Here's Michael Wolff delivering that smack to the hack. You know, you become part of -- one of the parts of the problem of the media. You know, you come on here and you -- and you have a, you know, a monopoly on truth. You know, you know exactly how things are supposed to be done. You know, you are why one of the reasons people can't stand the media. You should see the rest of the world, buddy. Can I hear that chuckle again? But if that was a heavyweight fight, and it is because, you know, Stelter, it would have been stopped in the first 25 seconds. It got worse, meaning better, lots better. STELTER: It's -- how -- so what should I do differently, Michael? WOLFF: You know, don't talk so much. Listen more, you know, people have genuine problems with the media. The media doesn't get the story right.
In the 2003 Major League Baseball season, Oreo Queefs stood five-foot-zero, weighed 385 pounds, and, impossibly, stole 214 bases, obliterating the century-old single-season record of 138. A walrus with the legs of a cheetah, the purple goateed Queefs also regularly blasted the ball 500 feet to opposite field--steroid-free beefiness never seen before or since. Over just two seasons with the Florida Marlins, he batted .680, Then, before even reaching his super alien prime, Queefs vanished into thin air. A few weeks ago, I received a text from the Marlins manager about what happened to the former Golden Glove winner.
A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound). We propose a Bayesian kernelised correlation test of (in)dependence using a Dirichlet process model. The new measure of (in)dependence allows us to answer some fundamental questions: Based on data, are (mixed-type) variables independent? How likely is dependence/independence to hold? How high is the probability that two mixed-type variables are more than just weakly dependent? We theoretically show the properties of the approach, as well as algorithms for fast computation with it. We empirically demonstrate the effectiveness of the proposed method by analysing its performance and by comparing it with other frequentist and Bayesian approaches on a range of datasets and tasks with mixed-type variables.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. It took just four batters at George Steinbrenner Field before a fan yelled "C'mon, blue!" toward home plate umpire Kaleb Devier after two consecutive close pitches were called balls. Never mind that a computer was making the calls. Didn't matter on Tuesday night as the Tampa Tarpons took on the Dunedin Blue Jays.