Neural Metamorphosis (NeuMeta) is a recent paradigm for generating neural networks of varying width and depth. Based on Implicit Neural Representation (INR), NeuMeta learns a continuous weight manifold, enabling the direct generation of compressed models, including those with configurations not seen during training. While promising, the original formulation of NeuMeta proves effective only for the final layers of the undelying model, limiting its broader applicability. In this work, we propose a training algorithm that extends the capabilities of NeuMeta to enable full-network metamorphosis with minimal accuracy degradation. Our approach follows a structured recipe comprising block-wise incremental training, INR initialization, and strategies for replacing batch normalization. The resulting metamorphic networks maintain competitive accuracy across a wide range of compression ratios, offering a scalable solution for adaptable and efficient deployment of deep models.

Retrieval-Augmented Generation (RAG) methods have proven highly effective for tasks requiring factual consistency and robust knowledge retrieval. However, large-scale RAG systems consume significant computational resources and are prone to generating hallucinated content from Humans. In this work, we introduce $\texttt{DRAG}$, a novel framework for distilling RAG knowledge from large-scale Language Models (LLMs) into small LMs (SLMs). Our approach leverages evidence- and knowledge graph-based distillation, ensuring that the distilled model retains critical factual knowledge while significantly reducing model size and computational cost. By aligning the smaller model's predictions with a structured knowledge graph and ranked evidence, $\texttt{DRAG}$ effectively mitigates hallucinations and improves factual accuracy. We further present a case demonstrating how our framework mitigates user privacy risks and introduce a corresponding benchmark. Experimental evaluations on multiple benchmarks demonstrate that our method outperforms the prior competitive RAG methods like MiniRAG for SLMs by up to 27.7% using the same models, preserving high-level efficiency and reliability. With $\texttt{DRAG}$, we provide a practical and resource-efficient roadmap to deploying enhanced retrieval and generation capabilities in small-sized LLMs.

App markets have evolved into highly competitive and dynamic environments for developers. While the traditional app life cycle involves incremental updates for feature enhancements and issue resolution, some apps deviate from this norm by undergoing significant transformations in their use cases or market positioning. We define this previously unstudied phenomenon as 'app metamorphosis'. In this paper, we propose a novel and efficient multi-modal search methodology to identify apps undergoing metamorphosis and apply it to analyse two snapshots of the Google Play Store taken five years apart. Our methodology uncovers various metamorphosis scenarios, including re-births, re-branding, re-purposing, and others, enabling comprehensive characterisation. Although these transformations may register as successful for app developers based on our defined success score metric (e.g., re-branded apps performing approximately 11.3% better than an average top app), we shed light on the concealed security and privacy risks that lurk within, potentially impacting even tech-savvy end-users.

As readers recall, I'd previously reviewed Richard Abbott's debut sci-fi novel, Far from the Spaceports, later returning for more Mitnash and Slate in its sequel, Timing. It was rather exciting listening to it, and I am so pleased to have the opportunity to share it here, along with some author comments as to the linguistics involved in setting up the pieces. First, for those unfamiliar with the novels and their plots, I've linked the book covers to their respective Amazon blurbs. Abbott's world-building opens a new type of sci-fi, one accessible even to those not typically enamored of the genre (such as myself), and the above-mentioned duo will capture your imagination as they seek to solve the mysteries of high-tech crime in space. Today you'll hear--and can read along--a bit of discussion between Mitnash and Slate, along with another pair, Rydal and Capstone, as the group talks about oddities in the data they are studying.

Olivia Rodrigo's latest single, "Good 4 U," comes from a long lineage of teen girl pop rock--that 2007 Radio Disney sound, as fellow young rocker Willow Smith put it. The 18-year-old Rodrigo's trio of singles have garnered praise for paying homage to her female Disney Channel predecessors, who similarly explored the emotional spectrum of girlhood through their music, chronicling its cheesy jubilance, frustration, pettiness, adventurousness, and confusion. For young girls in the 2000s, Disney-produced pop rock provided an outlet for those budding teenage feelings of rage against various "machines," defined as anything from annoying boys to the restrictions of youth--"they just don't understand me" is perhaps the catchphrase of ages 12 to 19. At almost 21 years old, barely two years removed from this demographic, I'm still desperately on the hunt for self-definition; it's an endless quest that, like all journeys, deserves a proper soundtrack. Radio Disney rock spoke to a generation of girls who grew up watching blogs, tabloids, and TV news conduct public crucifixions of women for daring to have fun, feel human emotions, or have a body--basically, for existing.

Kafka's "The Metamorphosis" has a famous opening: "One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin." The rest of the story follows, logically and ludicrously, from that original degrading miracle. Gregor struggles to get out of bed. His mother tells him that it's time to go to work. His boss, the chief clerk, shows up and demands that he return to the business no matter what shape he's in.

Kafka's "The Metamorphosis" has a famous opening: "One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin." The rest of the story follows, logically and ludicrously, from that original degrading miracle. Gregor struggles to get out of bed. His mother tells him that it's time to go to work. His boss, the chief clerk, shows up and demands that he return to the business no matter what shape he's in.

NLP enabled bots are trained with comprehensive data, thus generating a precise solution to the customer's queries. With the advent of Chatbots, the process of solving customer's query has transformed. It has become amongst the highly valued entity in the industry. Every sector is seeking to enhance operations and reduce human dependency through Chatbots. The global chatbot market size is forecasted to grow from US$2.6 billion in 2019 to US$ 9.4 billion by 2024, at a CAGR of 29.7% during the forecast period. Trained using machine learning models, chatbots are fed static information to ease out customer experience.

Dr. Stephen Friend on how machine learning will disrupt medicine - but why we need decision making to remain with the patient. Next Now: The 21st century has ushered in a new age where all aspects of our lives are impacted by technology. How will humanity anticipate, mitigate, and manage the consequences of AI, robots, quantum computing and more? How do we ensure tech works for the good of all? Dr. Stephen Friend is a globally acclaimed serial entrepreneur and biomedical researcher.

Humanity is at the edge of a revolution driven by artificial intelligence. It has the potential to be one of the most significant and far-reaching revolutions in history, yet it has developed out of disparate efforts to solve specific practical problems rather than a comprehensive plan. Ironically, the ultimate effect of this case-by-case problem solving may be the transformation of human reasoning and decision making. Attempts to halt it would cede the future to that element of humanity more courageous in facing the implications of its own inventiveness. Instead, we should accept that AI is bound to become increasingly sophisticated and ubiquitous, and ask ourselves: How will its evolution affect human perception, cognition, and interaction?