The New York Times reported that an AI system known as Aristo had become the first to successfully pass a standardized eighth-grade science test. The achievement arrived four years after a competition in which 700-plus scientists all failed to build a system capable of accomplishing the same task despite the incentive of the contest's $80,000 prize. Aristo has been viewed as a significant breakthrough in the evolution of AI technology, with far-reaching implications for natural language processing, business intelligence and more. The system provides a vivid illustration of the differences between human and artificial intelligence. It shows why the most effective AI systems still incorporate help from human experts -- a fact that has big implications for AI in business and other applications. The Aristo system represents a major step toward imbuing AI with what one Wired article refers to as "common sense," the expansive and unconscious background knowledge that we apply when navigating new situations or engaging in conversation.
The implementation for the paper "TTNet: Real-time temporal and spatial video analysis of table tennis" An introduction of the project could be found here (from the authors) Training much faster, achieve 120 FPS in the inference phase on a single GPU (GTX1080Ti). The implementation could achieve comparative results with the reported results in the TTNet paper. There are several limitations of the TTNet Paper (hints: Loss function, input size, and 2 more). I have implemented the task with a new approach and a new model. By default (as the above command), there are 4 modules in the TTNet model: global stage, local stage, event spotting, segmentation.
When Nick Kyrgios met Rafael Nadal in a packed centre court at Wimbledon 2019, it was always going to be a tempestuous affair. The fiery Australian went into the match with his trademark, but possibly misplaced, swagger given he had been at a local pub until 11pm the previous night. In typical Kyrgios fashion, there were spats with the chair umpire, cheeky underhand serves and some sublime tennis, including a nail biting 23 shot rally in the second set that ended with a blistering down-the-line forehand winner that Nadal could only gaze at from the wrong side of the court. Given the celebrity and combustibility of the players in question, the point was a shoo-in for the highlights reel. Meanwhile, down on sparsely populated court 12, Elise Mertens pulled off a dazzling combination of base-line drives, lunging half-volleys, a smash and a final volley drop shot to take an early break point against Barbora Strycova. The rally also made the highlights reel, not because of some keen eyed television producer but because of an artificial intelligence algorithm.
Did you know Python is known as an all-rounder programming language? Yes, it is, though it shouldn't be used on every single project, You can use it to create desktop applications, games, mobile apps, websites, and system software. So, I spent the last few weeks collecting unique project ideas for any Python developer. These project ideas will hopefully bring back your interest in this amazing language. The best part is you can enhance your Python programming skills with these fun but challenging projects. Let's have a look at them one-by-one: These days, massive progress has been made in the field of desktop application development.
I watch as he toys with the small group of lifeforms named Agents that curiously ramble around a tiny planet. They're odd three-legged things that go from goldfishing their way from one side of their tennis ball-sized existence to another to staring at me in bemusement to accidentally – and then angrily – bumping into each other. When the call ends, Gagliano – perhaps unknowingly – leaves the stream going for another 10 or so minutes. I sit, slightly transfixed, continuing to observe the Agents that go on existing in the absence of their newfound virtual deity. Agence is a hard thing to pin down. Gagliano, the piece's director, and Oppenheim, the creative producer, label it as a'looping' and'dynamic film', something that starts right back up again the moment it ends.
Artificial Intelligence (AI) and Machine Learning (ML) are among the most sought after tech skills by companies around the world. There is also a surge in no-code AI platforms. As more and more businesses are looking to leverage the power of AI, companies are accelerating the adoption of these technologies. Building solutions with these technologies require a sound experience and expertise in programming languages, however, there are some no-code visual drag-and-drop tools available to build ML solutions. Now it is an independent macOS application that comes with a bunch of pre-trained model templates.
A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks -- post-hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.
International Business Machines Corporation IBM recently announced that it will be leveraging its artificial intelligence ("AI") capabilities of Watson as well as open hybrid cloud architecture, to provide tennis fans with enriched experiences. United States Tennis Association ("USTA") is conducting this year's US Open without fans present at the stadium due to the coronavirus outbreak. Markedly, the US Open 2020 will be held from Aug 31 to Sep 13. Nevertheless, USTA, with IBM's help, will provide fans with an interactive and engaging digital experience to enjoy the tournament. IBM has been USTA's technology partner for almost three decades.
New York (CNN Business)With spectators unable to fill stadiums, sports leagues have to get creative with new forms of digital engagement to keep fans entertained. During the US Open, which started Monday, the US Tennis Association is inviting fans to engage in online debates about some of the sport's most contested questions, with the help of artificial intelligence technology from IBM (IBM). Fans can discuss topics like the most influential players in history, and their arguments will be analyzed by IBM's Watson technology (using the same AI tool that helped a computer take on a top human debater last year). The Open typically draws around 850,000 fans over three weeks. When the USTA announced in June that the Open would be held for the first time with no fans on site, IBM, a longtime sponsor and tech partner of the Tennis Association, was tasked with finding ways to make sure all those people would still tune in.
Fans can become instant "experts" about the players and the tournament match-ups with new AI-powered insights. This year, IBM is partnering again with the United States Tennis Association (USTA) and has developed three new tennis-based digital experiences for fans of the US Open. Spectators won't be allowed at the Arthur Ashe Stadium at the Billie Jean King National Tennis Center in Flushing, NY when the Grand Slam event begins on Aug. 31, due to the COVID-19 pandemic, but they will be able to participate remotely with new fan experiences that use artificial intelligence (AI) underpinned by hybrid cloud technologies. IBM has partnered with the USTA for 29 years, but 2018 was the first year that AI-powered tools were used by players and coaches. Last year, IBM introduced the IBM Coach Advisor and IBM Watson OpenScale.