Did Texas use artificial intelligence to call fourth-down play against Florida? LeBron James doubles down with sneaky golf hip turns during national anthem after Trump's'total disgrace' dig Drake Maye's end-zone interception proves costly as Patriots fall 30-24 to Bears in primetime Rhamondre Stevenson's miraculous 48-yard touchdown stuns Bears as clock expires in first half Todd Benzinger, who caught the final out of the Reds' 1990 World Series sweep, dead at 63 Super Bowl champion Marshall Faulk says he is living with Parkinson's disease NBA legend Charles Barkley says he won't'burn every bridge' like Aaron Rodgers as he considers retirement Bears fans don Mike Ditka's famous mustache as franchise legend honored at first home game since death Data shows Iran's shadow tanker fleet has run out of oil Court documents reportedly list Rep. Ilhan Omar under different name Jonathan Fahey on Ilhan Omar: 'This is a massive problem for her' OutKick Sports AOC says she supports men in women's sports because she's a girl's girl The New York congresswoman defended transgender students in women's sports at a More Perfect University campus event A cross country runner from Camas High (WA) who switched to the girls team midseason, takes off at the start line against 25 girls from four different schools. Alexandria Ocasio-Cortez (AOC), the U.S. representative for New York's 14th congressional district, just said one of the most asinine things you'll ever hear from a politician. She, as the vast majority of Democrats have, came out in support of trans rights and men in women's sports. That shouldn't shock anyone, but her reasoning behind that support is so absurdly ridiculous and nonsensical, it'll inevitably cause your hand to hit your face.

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We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high

Orthogonal convolutional layers are the workhorse of multiple areas in machine learning, such as adversarial robustness, normalizing flows, GANs, and Lipschitzconstrained models. Their ability to preserve norms and ensure stable gradient propagation makes them valuable for a large range of problems. Despite their promise, the deployment of orthogonal convolution in large-scale applications is a significant challenge due to computational overhead and limited support for modern features like strides, dilations, group convolutions, and transposed convolutions.In this paper, we introduce AOC (Adaptative Orthogonal Convolution), a scalable method for constructing orthogonal convolutions, effectively overcoming these limitations. This advancement unlocks the construction of architectures that were previously considered impractical. We demonstrate through our experiments that our method produces expressive models that become increasingly efficient as they scale. To foster further advancement, we provide an open-source library implementing this method, available at https://github.com/thib-s/orthogonium.

More than two dozen prostitutes line a Queens New York City street soliciting sex. At the exact time Rep. Alexandria Ocasio-Cortez, D-N.Y., was live-streaming her "Madden" NFL video game session with vice presidential candidate Tim Walz, on Twitch, her constituents were taking to the streets to protest rampant illegal prostitution and crime in the neighborhood she represents. The progressive "Squad" member was slammed by fellow Democrat politician Hiram Monserrate for playing the video game on the streaming service Sunday afternoon while residents from her district held a rally calling for their community to be cleaned up. "We need advocates not gamers," Monserrate, a former New York state senator who is running for State Assembly, told Fox News Digital. The Queens neighborhood is well known as a "Red Light" district, with some residents comparing the unsanitary and seedy conditions to a "Third World" country.

Recent advancements in large language models (LLMs) have showcased their exceptional abilities across various tasks, such as code generation, problem-solving and reasoning. Existing benchmarks evaluate tasks in isolation, yet the extent to which LLMs can understand prose-style tasks, identify the underlying problems, and then generate appropriate code solutions is still unexplored. Addressing this gap, we introduce PECC, a novel benchmark derived from Advent Of Code (AoC) challenges and Project Euler, including 2396 problems. Unlike conventional benchmarks, PECC requires LLMs to interpret narrative-embedded problems, extract requirements, and generate executable code. A key feature of our dataset is the complexity added by natural language prompting in chat-based evaluations, mirroring real-world instruction ambiguities. Results show varying model performance between narrative and neutral problems, with specific challenges in the Euler math-based subset with GPT-3.5-Turbo passing 50% of the AoC challenges and only 8% on the Euler problems. By probing the limits of LLMs' capabilities, our benchmark provides a framework to monitor and assess the subsequent progress of LLMs as a universal problem solver.

Citations are a key ingredient of scientific research to relate a paper to others published in the community. Recently, it has been noted that there is a citation age bias in the Natural Language Processing (NLP) community, one of the currently fastest growing AI subfields, in that the mean age of the bibliography of NLP papers has become ever younger in the last few years, leading to `citation amnesia' in which older knowledge is increasingly forgotten. In this work, we put such claims into perspective by analyzing the bibliography of $\sim$300k papers across 15 different scientific fields submitted to the popular preprint server Arxiv in the time period from 2013 to 2022. We find that all AI subfields (in particular: cs.AI, cs.CL, cs.CV, cs.LG) have similar trends of citation amnesia, in which the age of the bibliography has roughly halved in the last 10 years (from above 12 in 2013 to below 7 in 2022), on average. Rather than diagnosing this as a citation age bias in the NLP community, we believe this pattern is an artefact of the dynamics of these research fields, in which new knowledge is produced in ever shorter time intervals.

Learning controllers with offline data in decision-making systems is an essential area of research due to its potential to reduce the risk of applications in real-world systems. However, in responsibility-sensitive settings such as healthcare, decision accountability is of paramount importance, yet has not been adequately addressed by the literature. This paper introduces the Accountable Offline Controller (AOC) that employs the offline dataset as the Decision Corpus and performs accountable control based on a tailored selection of examples, referred to as the Corpus Subset. AOC operates effectively in low-data scenarios, can be extended to the strictly offline imitation setting, and displays qualities of both conservation and adaptability. We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high levels of performance while maintaining accountability.

Temporal abstraction in reinforcement learning is the ability of an agent to learn and use high-level behaviors, called options. The option-critic architecture provides a gradient-based end-to-end learning method to construct options. We propose an attention-based extension to this framework, which enables the agent to learn to focus different options on different aspects of the observation space. We show that this leads to behaviorally diverse options which are also capable of state abstraction, and prevents the degeneracy problems of option domination and frequent option switching that occur in option-critic, while achieving a similar sample complexity. We also demonstrate the more efficient, interpretable, and reusable nature of the learned options in comparison with option-critic, through different transfer learning tasks. Experimental results in a relatively simple four-rooms environment and the more complex ALE (Arcade Learning Environment) showcase the efficacy of our approach.