People in Pensacola, Florida shared their thoughts on Cracker Barrel's new logo with Fox News Digital. Few things in American life have felt as trapped in the amber of history as Cracker Barrel restaurants, with their recipe of comfort food served up in cozy confines that evoke a bygone era. It's little wonder Americans routinely wait for an hour to get a table after church, or welcome a road-trip diversion when they see the classic logo on a highway sign. Now, the cracker-jack whiz-kid marketing team at the iconic eatery's corprate headquarters has decided to forgo all of this, including possibly, based on public reaction to their changes, the long lines. CRACKER BARREL UNVEILS NEW SIMPLIFIED LOGO: 'OUR STORY HASN'T CHANGED' This may not exactly be wokeness at work, as we have seen with so many brands such as Target and Bud Light, but it is something similarly lifeless and cold.

Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of inductive bias on HTML. We introduce WebAgent, an LLM-driven agent that learns from self-experience to complete tasks on real websites following natural language instructions. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites via Python programs generated from those. We design WebAgent with Flan-U-PaLM, for grounded code generation, and HTML-T5, new pre-trained LLMs for long HTML documents using local and global attention mechanisms and a mixture of long-span denoising objectives, for planning and summarization. We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks; achieving 18.7% higher success rate than the prior method on MiniWoB web automation benchmark, and SoTA performance on Mind2Web, an offline task planning evaluation.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Warner Bros. has once again delayed the release of the Christopher Nolan-directed film "Tenet" during the coronavirus pandemic. The studio said Monday that the $200 million thriller will not make its August release date. However, unlike past announcements, Warner Bros. did not announce a new target date this time. The sci-fi thriller, which stars John David Washington and Robert Pattinson, was set to be released on Wednesday, Aug. 12.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Warner Bros. has once again delayed the release of the Christopher Nolan-directed film "Tenet" amid reported cases of the novel coronavirus surging. The studio announced the decision on Thursday, stressing the need for flexibility. The sci-fi thriller, which stars John David Washington and Robert Pattinson, will now be released on Wednesday, Aug. 12.

Twelve years later, even some of his former Carnegie Mellon University teammates have become business competitors of Salesky, who with CMU alumnus and faculty adviser Peter Rander founded Argo AI and went on to attract substantial investments from Ford Motor Co and Volkswagen AG (VOWG_p.DE). At the 2007 self-driving competition staged by DoD's Defense Advanced Research Projects Agency (DARPA) in remote Victorville, California, Salesky's CMU team and one from rival Stanford University included the future founders of at least four self-driving startups. Those competitors were Chris Urmson and Drew Bagnell of self-driving vehicle startup Aurora, Dave Ferguson of Nuro, Apex.ai's Jan Becker and Anthony Levandowski of Pronto.ai. Sebastian Thrun, who with Levandowski and Urmson helped build Google's self-driving business, also participated in the 2007 DARPA Urban Challenge, as did Dmitri Dolgov, who now heads engineering at Google's self-driving spinout Waymo.

Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle-free volume in spacetime is very small in these scenarios for the vehicle to drive through. However, that does not mean the task is infeasible since human drivers are known to be able to drive amongst dense traffic by leveraging the cooperativeness of other drivers to open a gap. The traditional methods fail to take into account the fact that the actions taken by an agent affect the behaviour of other vehicles on the road. In this work, we rely on the ability of deep reinforcement learning to implicitly model such interactions and learn a continuous control policy over the action space of an autonomous vehicle. The application we consider requires our agent to negotiate and open a gap in the road in order to successfully merge or change lanes. Our policy learns to repeatedly probe into the target road lane while trying to find a safe spot to move in to. We compare against two model-predictive control-based algorithms and show that our policy outperforms them in simulation.

Nasa has flown a large, remotely piloted predator drone equipped with detect-and-avoid technologies through the national airspace system for the first time without a safety chase plane following it. The space agency says the'milestone' flight over California moves the US closer to normalising unmanned aircraft operations in airspace used by commercial and private pilots. The test used a non-military version of the Air Force's MQ-9 Predator B called Ikhana that is 36 feet (11 meters) long and has a 66-foot (20-meter) wingspan. It paves the way for large remotely-piloted aircraft to be used in all kinds of services, from fighting forest fires to providing emergency search and rescue operations, according to Nasa. The flight took off from Edwards Air Force Base in California and entered controlled air space almost immediately.

In an airplane hanger on MIT's campus in Cambridge last week, a team of engineering students and researchers put the finishing touches on Talos, a Land Rover that drives itself. Talos is MIT's entry in the Defense Advanced Research Project Agency's (DARPA) robotic car race, which will take place on November 3, in Victorville, CA. Known as the Urban Challenge, the race will test the ability of robotic cars from 35 different teams to obey traffic laws and drive safely in a city-like environment without human assistance. The vehicles will need to find their way to a preprogrammed destination while paying attention to lane markers, other cars, and unexpected obstacles, such as potholes in the road. The Urban Challenge is a follow-up to DARPA's Grand Challenge race, held in 2004 and 2005, in which cars navigated an empty desert road.