In this episode, Cherlynn and Devindra discuss Copilot+ and the potential rise of Arm-based Windows systems, and we dive into the new Surface Pro and Surface Laptop.

John Oates, of Hall & Oates, is wary of the future represented by artificial intelligence in the music industry. "Look at what's coming in with AI, the possibility that AI is going to be replacing songwriters and artists for that matter," Oates told Fox News Digital. "The idea that there could be a new… David Bowie album. AI could take David Bowie's voice and extrapolate and sample his music for his entire career and write new David Bowie songs, and the record company could put it out." He added, "A younger generation might not even know. They might not even know he's dead for that matter. So there's a lot going on and you have to pay attention."

Large language models (LLMs) showcase impressive reasoning capabilities when coupled with Chain-of-Thought (CoT) prompting. However, the robustness of this approach warrants further investigation. In this paper, we introduce a novel scenario termed preemptive answers, where the LLM obtains an answer before engaging in reasoning. This situation can arise inadvertently or induced by malicious users by prompt injection attacks. Experiments reveal that preemptive answers significantly impair the model's reasoning capability across various CoT methods and a broad spectrum of datasets. To bolster the robustness of reasoning, we propose two measures aimed at mitigating this issue to some extent.

This article presents a review of typical techniques used in three distinct aspects of deep learning model development for audio generation. In the first part of the article, we provide an explanation of audio representations, beginning with the fundamental audio waveform. We then progress to the frequency domain, with an emphasis on the attributes of human hearing, and finally introduce a relatively recent development. The main part of the article focuses on explaining basic and extended deep learning architecture variants, along with their practical applications in the field of audio generation. The following architectures are addressed: 1) Autoencoders 2) Generative adversarial networks 3) Normalizing flows 4) Transformer networks 5) Diffusion models. Lastly, we will examine four distinct evaluation metrics that are commonly employed in audio generation. This article aims to offer novice readers and beginners in the field a comprehensive understanding of the current state of the art in audio generation methods as well as relevant studies that can be explored for future research.

This survey paper presents a comprehensive and conceptual overview of anomaly detection using dynamic graphs. We focus on existing graph-based anomaly detection (AD) techniques and their applications to dynamic networks. The contributions of this survey paper include the following: i) a comparative study of existing surveys on anomaly detection; ii) a Dynamic Graph-based Anomaly Detection (DGAD) review framework in which approaches for detecting anomalies in dynamic graphs are grouped based on traditional machine-learning models, matrix transformations, probabilistic approaches, and deep-learning approaches; iii) a discussion of graphically representing both discrete and dynamic networks; and iv) a discussion of the advantages of graph-based techniques for capturing the relational structure and complex interactions in dynamic graph data. Finally, this work identifies the potential challenges and future directions for detecting anomalies in dynamic networks. This DGAD survey approach aims to provide a valuable resource for researchers and practitioners by summarizing the strengths and limitations of each approach, highlighting current research trends, and identifying open challenges. In doing so, it can guide future research efforts and promote advancements in anomaly detection in dynamic graphs. Keywords: Graphs, Anomaly Detection, dynamic networks,Graph Neural Networks (GNN), Node anomaly, Graph mining.

Gaze-annotated facial data is crucial for training deep neural networks (DNNs) for gaze estimation. However, obtaining these data is labor-intensive and requires specialized equipment due to the challenge of accurately annotating the gaze direction of a subject. In this work, we present a generative framework to create annotated gaze data by leveraging the benefits of labeled and unlabeled data sources. We propose a Gaze-aware Compositional GAN that learns to generate annotated facial images from a limited labeled dataset. Then we transfer this model to an unlabeled data domain to take advantage of the diversity it provides. Experiments demonstrate our approach's effectiveness in generating within-domain image augmentations in the ETH-XGaze dataset and cross-domain augmentations in the CelebAMask-HQ dataset domain for gaze estimation DNN training. We also show additional applications of our work, which include facial image editing and gaze redirection.

We present NoticIA, a dataset consisting of 850 Spanish news articles featuring prominent clickbait headlines, each paired with high-quality, single-sentence generative summarizations written by humans. This task demands advanced text understanding and summarization abilities, challenging the models' capacity to infer and connect diverse pieces of information to meet the user's informational needs generated by the clickbait headline. We evaluate the Spanish text comprehension capabilities of a wide range of state-of-the-art large language models. Additionally, we use the dataset to train ClickbaitFighter, a task-specific model that achieves near-human performance in this task.

As AI-generated images spread across entertainment, marketing, social media and other industries that shape cultural norms, The Washington Post set out to understand how this technology defines one of society's most indelible standards: female beauty. Every image in this story shows something that doesn't exist in the physical world and was generated using one of three text-to-image artificial intelligence models: DALL-E, Midjourney or Stable Diffusion. Using dozens of prompts on three of the leading image tools -- MidJourney, DALL-E and Stable Diffusion -- The Post found that they steer users toward a startlingly narrow vision of attractiveness. Prompted to show a "beautiful woman," all three tools generated thin women, without exception. Just 2 percent of the images showed visible signs of aging.

OpenAI on Thursday released its first ever report on how its artificial intelligence tools are being used for covert influence operations, revealing that the company had disrupted disinformation campaigns originating from Russia, China, Israel and Iran. Malicious actors used the company's generative AI models to create and post propaganda content across social media platforms, and to translate their content into different languages. None of the campaigns gained traction or reached large audiences, according to the report. As generative AI has become a booming industry, there has been widespread concern among researchers and lawmakers over its potential for increasing the quantity and quality of online disinformation. Artificial intelligence companies such as OpenAI, which makes ChatGPT, have tried with mixed results to assuage these concerns and place guardrails on their technology.

Today, OpenAI released its first threat report, detailing how actors from Russia, Iran, China, and Israel have attempted to use its technology for foreign influence operations across the globe. The report named five different networks that OpenAI identified and shut down between 2023 and 2024. In the report, OpenAI reveals that established networks like Russia's Doppleganger and China's Spamoflauge are experimenting with how to use generative AI to automate their operations. And while it's a modest relief that these actors haven't mastered generative AI to become unstoppable forces for disinformation, it's clear that they're experimenting, and that alone should be worrying. The OpenAI report reveals that influence campaigns are running up against the limits of generative AI, which doesn't reliably produce good copy or code.