YouTube is rolling out new rules for AI content, including a requirement that creators reveal whether they've used generative artificial intelligence to make realistic looking videos. In a blog post Tuesday outlining a number of AI-related policy updates, YouTube said creators that don't disclose whether they've used AI tools to make "altered or synthetic" videos face penalties including having their content removed or suspension from the platform's revenue sharing program. "Generative AI has the potential to unlock creativity on YouTube and transform the experience for viewers and creators on our platform," Jennifer Flannery O'Connor and Emily Moxley, vice presidents for product management, wrote in the blog post. "But just as important, these opportunities must be balanced with our responsibility to protect the YouTube community." The restrictions expand on rules that YouTube's parent company, Google, unveiled in September requiring that political ads on YouTube and other Google platforms using artificial intelligence come with a prominent warning label.

Two days later on another stage, in another venue, at another developers' conference, Nadella made his second unannounced appearance of the week--this time at GitHub Universe. There Thomas Dohmke, GitHub's CEO, was showing off a new version of the company's AI programming tool, Copilot, that can generate computer code from natural language. Nadella was effusive: "I can code again!" he exclaimed. Today, Nadella will be onstage speaking to developers at Microsoft Ignite, where the company is announcing even more AI-based developer tools, including an Azure AI Studio that will let devs choose between model catalogs from not only Microsoft, but also the likes of Meta, OpenAI, and Hugging Face, as well as new tools for customizing Copilot for Microsoft 365. If it seems like Nadella is obsessed with developers, you're not wrong.

Microsoft has announced a project it has been "refining in secret for years;" Its own custom silicon in the form of two new server chips. The company unveiled the fruits of its labor at Microsoft Ignite, showing off the Azure Maia AI Accelerator and the Azure Cobalt CPU. The latter of which, at least, the company is happy to admit is ARM-based, which can still feel unthinkable to eyes so used to Microsoft and Intel's hand-in-glove dominance of the computing market. The company turned to OpenAI to receive feedback on Azure Maia and to use the company's models for testing. OpenAI CEO Sam Altman said the updated Microsoft's Azure will also provide the opportunity for training improved models and making them more affordable for customers.

Microsoft is rebranding Bing Chat and is now simply calling it "Copilot," giving its generative AI assistant a consistent identity across its products. Similarly, Bing Chat Enterprise will be known "Copilot Pro," and it will be generally available starting on December 1. It will still be free for specific Microsoft 365 licenses, which will include F3 accounts for frontline workers, though the $5-a-month standalone subscription will be available that day, as well. The Copilot Pro is based on OpenAI's latest models, GPT-4 and DALL-E 3, and the company says it will not save prompts and responses. Microsoft will not see interactions happening within Copilot Pro at all, and it will not use customers' chats to further train the underlying models.

In May, the company began a hiring spree, poaching executives from Meta, Apple and Amazon Web Services. Last month, the company expanded its footprint in San Francisco, subleasing nearly 445,000 square feet of office space from Uber, purchased when then-CEO Travis Kalanick was still the most envied founder in the Valley.

Chest X-rays (CXR) are the most common medical imaging study and are used to diagnose multiple medical conditions. This study examines the impact of synthetic data supplementation, using diffusion models, on the performance of deep learning (DL) classifiers for CXR analysis. We employed three datasets: CheXpert, MIMIC-CXR, and Emory Chest X-ray, training conditional denoising diffusion probabilistic models (DDPMs) to generate synthetic frontal radiographs. Our approach ensured that synthetic images mirrored the demographic and pathological traits of the original data. Evaluating the classifiers' performance on internal and external datasets revealed that synthetic data supplementation enhances model accuracy, particularly in detecting less prevalent pathologies. Furthermore, models trained on synthetic data alone approached the performance of those trained on real data. This suggests that synthetic data can potentially compensate for real data shortages in training robust DL models. However, despite promising outcomes, the superiority of real data persists.

Diffusion models are at the vanguard of generative AI research with renowned solutions such as ImageGen by Google Brain and DALL.E 3 by OpenAI. Nevertheless, the potential merits of diffusion models for communication engineering applications are not fully understood yet. In this paper, we aim to unleash the power of generative AI for PHY design of constellation symbols in communication systems. Although the geometry of constellations is predetermined according to networking standards, e.g., quadrature amplitude modulation (QAM), probabilistic shaping can design the probability of occurrence (generation) of constellation symbols. This can help improve the information rate and decoding performance of communication systems. We exploit the ``denoise-and-generate'' characteristics of denoising diffusion probabilistic models (DDPM) for probabilistic constellation shaping. The key idea is to learn generating constellation symbols out of noise, ``mimicking'' the way the receiver performs symbol reconstruction. This way, we make the constellation symbols sent by the transmitter, and what is inferred (reconstructed) at the receiver become as similar as possible, resulting in as few mismatches as possible. Our results show that the generative AI-based scheme outperforms deep neural network (DNN)-based benchmark and uniform shaping, while providing network resilience as well as robust out-of-distribution performance under low-SNR regimes and non-Gaussian assumptions. Numerical evaluations highlight 30% improvement in terms of cosine similarity and a threefold improvement in terms of mutual information compared to DNN-based approach for 64-QAM geometry.

Often machine learning models tend to automatically learn associations present in the training data without questioning their validity or appropriateness. This undesirable property is the root cause of the manifestation of spurious correlations, which render models unreliable and prone to failure in the presence of distribution shifts. Research shows that most methods attempting to remedy spurious correlations are only effective for a model's known spurious associations. Current spurious correlation detection algorithms either rely on extensive human annotations or are too restrictive in their formulation. Moreover, they rely on strict definitions of visual artifacts that may not apply to data produced by generative models, as they are known to hallucinate contents that do not conform to standard specifications. In this work, we introduce a general-purpose method that efficiently detects potential spurious correlations, and requires significantly less human interference in comparison to the prior art. Additionally, the proposed method provides intuitive explanations while eliminating the need for pixel-level annotations. We demonstrate the proposed method's tolerance to the peculiarity of AI-generated images, which is a considerably challenging task, one where most of the existing methods fall short. Consequently, our method is also suitable for detecting spurious correlations that may propagate to downstream applications originating from generative models.

Large language models have revolutionized the field of artificial intelligence and have been used in various applications. Among these models, ChatGPT (Chat Generative Pre-trained Transformer) has been developed by OpenAI, it stands out as a powerful tool that has been widely adopted. ChatGPT has been successfully applied in numerous areas, including chatbots, content generation, language translation, personalized recommendations, and even medical diagnosis and treatment. Its success in these applications can be attributed to its ability to generate human-like responses, understand natural language, and adapt to different contexts. Its versatility and accuracy make it a powerful tool for natural language processing (NLP). However, there are also limitations to ChatGPT, such as its tendency to produce biased responses and its potential to perpetuate harmful language patterns. This article provides a comprehensive overview of ChatGPT, its applications, advantages, and limitations. Additionally, the paper emphasizes the importance of ethical considerations when using this robust tool in real-world scenarios. Finally, This paper contributes to ongoing discussions surrounding artificial intelligence and its impact on vision and NLP domains by providing insights into prompt engineering techniques.

Record companies can request the removal of songs that use artificial intelligence-generated versions of artists' voices under new guidelines issued by YouTube. The video platform is introducing a tool that will allow music labels and distributors to flag content that mimics an artist's "unique singing or rapping voice". Fake AI-generated music has been one of the side-effects of leaps forward this year in generative AI – the term for technology that can produce highly convincing text, images and voice from human prompts. One of the most high-profile examples is Heart on My Sleeve, a song featuring AI-made vocals purporting to be Drake and the Weeknd. It was pulled from streaming services after Universal Music Group, the record company for both artists, criticised the song for "infringing content created with generative AI".