Although it might seem as though TVs are growing in size, good things still come in small packages. Plenty of manufacturers still support smaller TVs--models that range in size from 32-inches to 43-inches. These TVs can be the perfect fit for smaller living rooms or for bedrooms, guest rooms, and dorm rooms. While 65-inch TVs are more glamorous and 55-inch TVs are the most popular television size, the best 40-inch TVs can deliver great performance and desired features. There are plenty of cheap TVs out there that don't stand up, however.

If a song is created by artificial intelligence and listened to by a bot, was it even heard at all? It's a problem music-streaming companies now face as generative AI is rapidly making it easier for anyone to churn out songs with a few clicks, and then send bots to stream them for cash. "It's a floodgate," says Tony Rigg, a lecturer in music industry management at the University of Central Lancashire in the UK. And that torrent of new music amplifies the issue of fake listening, giving people a simple way to get streams on low-quality tracks. Some turn to third-party companies promising to boost streams, which then enlist bot-made accounts to listen to the same playlists on repeat.

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. Artificial intelligence already has a lengthy track record in the field of weather prediction, where it has helped prognosticators make faster, more accurate forecasts for nearly three decades. But now, AI has the potential to take the next step when it comes to predicting sun, rain, wind and snow by doing the work on its own, without using various models that human forecasters have relied on for generations. Hendrik Tolman, senior adviser for advanced modeling systems at the National Weather Service, told Fox News Digital this possibility is now on the horizon and is actively being explored.

AI image generators Midjourney and Stable Diffusion trained their models with the works of countless artists without their permission or compensation, artist says. AI-generated images that mimic an artist's style is a form of identity theft and compete with the very creatives whose work was used to train the models, a fine artist suing two artificial intelligence firms told Fox News. AI platforms like Midjourney and Stable Diffusion use text and images from across the internet and other sources to train their machines to create images for their consumers. "Somebody is able to mimic my work because a company let them," Ortiz told Fox News. "It feels like some sort of industrial-level identity theft."

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' With AI large language models like ChatGPT being developed around the globe, countries have raced to regulate AI. Some have drafted strict laws on the technology, while others lack regulatory oversight. China and the EU have received particular attention, as they have created detailed, yet divergent, AI regulations. In both, the government plays a large role.

"Gutfeld!" panelists weigh in on the rise of video and audio clips made using artificial intelligence tools to mimic the voice and the likeness of anyone you want. Images generated by artificial intelligence are becoming more convincing and prevalent, and they could lead to more complicated court cases if the synthetic media is submitted as evidence, legal experts say. "Deepfakes" often involve editing videos or photos of people to make them look like someone else by using deep-learning AI. The technology broadly hit the public's radar in 2017 after a Reddit user posted realistic-looking pornography of celebrities to the platform. The pornography was revealed to be doctored, but the revolutionary tech has only become more realistic and easier to make in the years since.

Based on the remarkable achievements of pre-trained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance. This work proposes PROM, a new PhRase-level cOpying Mechanism that enhances attention on n-grams, which can be applied to zero-shot summarization with pre-training. PROM adds an indicator layer to explicitly pick up tokens in n-gram that can be copied from the source, and calculates an auxiliary loss for the copying prediction. Empirical studies show that PROM makes significant improvements in fine-tuning on benchmarks. In zero-shot setting, PROM is utilized in the self-supervised pre-training on raw corpora and provides new general baselines on a wide range of summarization datasets. Further analysis shows that PROM performs more reasonable copying and contributes to faithfulness.

To avoid any terminological doubt, when we write "models of the language", we refer to any models that explain some linguistic phenomena, while "language models" refer to probabilistic language models as defined in Subsection 2.3 Probabilistic Language Models. While not long ago probabilistic language models were just models that assign probabilities to sequences of words [4], now they are the cornerstone of any task in computational linguistics through few-shot learning [6], prompt engineering [38] or fine-tuning [13]. On the other hand, current language models fail to catch long-range dependencies in the text consistently. For example, text generation with maximum likelihood target leads to rapid text degeneration, and consistent text generation requires probabilistic sampling and other tricks [22]. Large language models such as GPT-3 [6] push the boundary of "short text" rather far (specifically, to 2048 tokens), but do not remove the problem. Our contributions in this work are the following: We explain how the laws of autocorrelations decay in texts are related to applicability of language models to long texts; We pioneer the use of pretrained word vectors for autocorrelation computations that allows us to study a widest range of autocorrelation distances; We show that the autocorrelations in literary texts decay according to power laws for all these distances; We show that distributional semantics typically provides coherent autocorrelations decay exponents for texts translated to multiple languages, unlike earlier flawed approaches; We show that the behavior of autocorrelations decay in generated texts is quantitatively and often qualitatively different from the literary texts.

Representing human performance at high-fidelity is an essential building block in diverse applications, such as film production, computer games or videoconferencing. To close the gap to production-level quality, we introduce HumanRF, a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints. Our novel representation acts as a dynamic video encoding that captures fine details at high compression rates by factorizing space-time into a temporal matrix-vector decomposition. This allows us to obtain temporally coherent reconstructions of human actors for long sequences, while representing high-resolution details even in the context of challenging motion. While most research focuses on synthesizing at resolutions of 4MP or lower, we address the challenge of operating at 12MP. To this end, we introduce ActorsHQ, a novel multi-view dataset that provides 12MP footage from 160 cameras for 16 sequences with high-fidelity, per-frame mesh reconstructions. We demonstrate challenges that emerge from using such high-resolution data and show that our newly introduced HumanRF effectively leverages this data, making a significant step towards production-level quality novel view synthesis.

This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an inherently interpretable model with high performance. To this end, we propose a novel interpreter design that incorporates non-negative matrix factorization (NMF). In particular, an interpreter is trained to generate a regularized intermediate embedding from hidden layers of a target network, learnt as time-activations of a pre-learnt NMF dictionary. Our methodology allows us to generate intuitive audio-based interpretations that explicitly enhance parts of the input signal most relevant for a network's decision. We demonstrate our method's applicability on a variety of classification tasks, including multi-label data for real-world audio and music.