Deep learning (DL) systems have been gaining popularity in critical tasks such as credit evaluation and crime prediction.

Freecash will actually pay money out to users but not for watching videos. This misleading marketing coincides with the app's rising popularity. I first encountered the Freecash app after clicking on a sponsored TikTok video with dubious claims. The advertisement didn't promote this app by name, rather it showed a young woman expressing her excitement about seemingly getting hired by TikTok at $35 an hour to watch videos on her "For You" page. When I tapped the link to "order now," it sent me to a website with TikTok and Freecash logos, featuring a download link for the Freecash app.

Elon Musk has been promoting Grok's popularity as if it were a piece of productivity software. Elon Musk has been promoting Grok's popularity as if it were a piece of productivity software. Today, we discuss Elon Musk's rosy depiction of Grok's image generation controversy; the seven-figure panic among Silicon Valley billionaires over a proposed wealth tax in California, though with one notable exception; and how AI and robotics have revitalized the Consumer Electronics Showcase. The firestorm over the Grok AI tool has been raging for more than a week now, and it shows no signs of dying down. Last week, I wrote about the rising backlash against Elon Musk's Grok AI tool, which in recent weeks has allowed users to generate thousands of sexualized images of women.

Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search. CRAG is designed to encapsulate a diverse array of questions across five domains and eight question categories, reflecting varied entity popularity from popular to long-tail, and temporal dynamisms ranging from years to seconds. Our evaluation on this benchmark highlights the gap to fully trustworthy QA.

LLM-based Search Engines (LLM-SEs) introduces a new paradigm for information seeking. Unlike Traditional Search Engines (TSEs) (e.g., Google), these systems summarize results, often providing limited citation transparency. The implications of this shift remain largely unexplored, yet raises key questions regarding trust and transparency. In this paper, we present a large-scale empirical study of LLM-SEs, analyzing 55,936 queries and the corresponding search results across six LLM-SEs and two TSEs. We confirm that LLM-SEs cites domain resources with greater diversity than TSEs. Indeed, 37% of domains are unique to LLM-SEs. However, certain risks still persist: LLM-SEs do not outperform TSEs in credibility, political neutrality and safety metrics. Finally, to understand the selection criteria of LLM-SEs, we perform a feature-based analysis to identify key factors influencing source choice. Our findings provide actionable insights for end users, website owners, and developers.

Accurately predicting music popularity is a critical challenge in the music industry, offering benefits to artists, producers, and streaming platforms. Prior research has largely focused on audio features, social metadata, or model architectures. This work addresses the under-explored role of lyrics in predicting popularity. We present an automated pipeline that uses LLM to extract high-dimensional lyric embeddings, capturing semantic, syntactic, and sequential information. These features are integrated into HitMusicLyricNet, a multimodal architecture that combines audio, lyrics, and social metadata for popularity score prediction in the range 0-100. Our method outperforms existing baselines on the SpotGenTrack dataset, which contains over 100,000 tracks, achieving 9% and 20% improvements in MAE and MSE, respectively. Ablation confirms that gains arise from our LLM-driven lyrics feature pipeline (LyricsAENet), underscoring the value of dense lyric representations.

Low-Rank Adaptation (LoRA) has become the de facto method for parameter-efficient fine-tuning of large language models (LLMs), enabling rapid adaptation to diverse domains. In production, LoRA-based models are served at scale, creating multi-tenant environments with hundreds of adapters sharing a base model. However, state-of-the-art serving systems co-batch heterogeneous adapters without accounting for rank (size) variability, leading to severe performance skew, which ultimately requires adding more GPUs to satisfy service-level objectives (SLOs). Existing optimizations, focused on loading, caching, and kernel execution, ignore this heterogeneity, leaving GPU resources underutilized. We present LoRAServe, a workload-aware dynamic adapter placement and routing framework designed to tame rank diversity in LoRA serving. By dynamically rebalancing adapters across GPUs and leveraging GPU Direct RDMA for remote access, LoRAServe maximizes throughput and minimizes tail latency under real-world workload drift. Evaluations on production traces from Company X show that LoRAServe elicits up to 2$\times$ higher throughput, up to 9$\times$ lower TTFT, while using up to 50% fewer GPUs under SLO constraints compared to state-of-the-art systems.

Starmer could have scrapped child benefit cap last year - why did he wait? I can't remember when I last heard Sir Keir Starmer sounding so passionate. The prime minister's critics regularly lambast him for what they see as robotic or emotion-free communication, but you could not accuse him of that as we spoke on a post-Budget visit to a community centre in Rugby, Warwickshire. I could see it in his eyes and hear it in his tone. I have repeatedly said that I want my government to drive down child poverty.