What does recognising a Palestinian state mean? AI creates new'memories' of Palestinians killed in Gaza NewsFeed AI creates new'memories' of Palestinians killed in Gaza People are using AI to create new'memories' of Palestinians killed by Israel in Gaza, generating touching images of them with their loved ones. Drone strike on mosque kills dozens in Sudan's el-Fasher Russia becoming'more dangerous', EU foreign affairs head warns

Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. To address the crucial need to keep models updated, online learning has emerged as a critical tool when utilizing LLMs for real-world applications. However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention. We propose a feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank.

Human interaction with the external world fundamentally involves the exchange of personal memory, whether with other individuals, websites, applications, or, in the future, AI agents. A significant portion of this interaction is redundant, requiring users to repeatedly provide the same information across different contexts. Existing solutions, such as browser-stored credentials, autofill mechanisms, and unified authentication systems, have aimed to mitigate this redundancy by serving as intermediaries that store and retrieve commonly used user data. The advent of large language models (LLMs) presents an opportunity to redefine memory management through an AI-native paradigm: SECOND ME. SECOND ME acts as an intelligent, persistent memory offload system that retains, organizes, and dynamically utilizes user-specific knowledge. By serving as an intermediary in user interactions, it can autonomously generate context-aware responses, prefill required information, and facilitate seamless communication with external systems, significantly reducing cognitive load and interaction friction. Unlike traditional memory storage solutions, SECOND ME extends beyond static data retention by leveraging LLM-based memory parameterization. This enables structured organization, contextual reasoning, and adaptive knowledge retrieval, facilitating a more systematic and intelligent approach to memory management. As AI-driven personal agents like SECOND ME become increasingly integrated into digital ecosystems, SECOND ME further represents a critical step toward augmenting human-world interaction with persistent, contextually aware, and self-optimizing memory systems. We have open-sourced the fully localizable deployment system at GitHub: https://github.com/Mindverse/Second-Me.

Recent studies attempt to optimize maintain acceptable error rates in the face of massive-scale networks sketches using machine learning; however, these approaches and highly skewed traffic distributions [7, 15]. In practice, a face the challenges of lacking adaptivity to dynamic networks and small fraction of large flows typically accounts for the majority of incurring high training costs. In this paper, we propose LLM-Sketch, total traffic volume, while many small flows remain numerous yet based on the insight that fields beyond the flow IDs in packet headers contribute only modestly. A representative example is the Count-can also help infer flow sizes. By using a two-tier data structure Min Sketch (CMS) [12], which updates and queries counters based and separately recording large and small flows, LLM-Sketch improves on hashed flow IDs. Although CMS is simple and memory-efficient, accuracy while minimizing memory usage. Furthermore, it it faces a fundamental trade-off: counters sized for small flows undercount leverages fine-tuned large language models (LLMs) to reliably estimate the large ones, while counters sized for large flows waste flow sizes. We evaluate LLM-Sketch on three representative memory on the many small ones. Consequently, CMS cannot accurately tasks, and the results demonstrate that LLM-Sketch outperforms capture the minority of large flows without significantly state-of-the-art methods by achieving a 7.5 accuracy improvement.

Domain incremental learning aims to adapt to a sequence of domains with access to only a small subset of data (i.e., memory) from previous domains. Various methods have been proposed for this problem, but it is still unclear how they are related and when practitioners should choose one method over another. In response, we propose a unified framework, dubbed Unified Domain Incremental Learning (UDIL), for domain incremental learning with memory. Our UDIL unifies various existing methods, and our theoretical analysis shows that UDIL always achieves a tighter generalization error bound compared to these methods. The key insight is that different existing methods correspond to our bound with different fixed coefficients; based on insights from this unification, our UDIL allows adaptive coefficients during training, thereby always achieving the tightest bound.

It's no secret that filters can transform us into almost anything - whether it be a dog or a dancing hotdog. But Snapchat has now taken this up a notch, with the launch of a new tool that uses artificial intelligence (AI) to completely reimagine your photographs. The so-called'Dreams' feature will allow users to create fantasy-themed AI selfies in just a few taps - and the results are unbelievably realistic. Deep-sea mermaids and Renaissance-era royals are among the initial pack of eight complimentary Dreams that can be created, while others start at $0.99. The AI tool will be launched first in Australia and New Zealand, before making its way to other Snapchatters across the globe in a couple of weeks.

Created in 2013, the Stars of Europe awards recognize the coordinators of European collaborative research projects. On December 6, Sylvie Retailleau, Minister of Higher Education and Research, presented trophies to twelve winners at a ceremony at the Quai Branly Museum. Among them, Nicolas Mansard, CNRS researcher in robotics at LAAS-CNRS, holder of the ANITI chair " Artificial and natural movement", rewarded for the coordination of the MEMMO (memory of motion) project. Funded by the Horizon 2020 program over a four-year period, MEMMO (Memory of Motion) is a collaborative project initiated in 2018 that brought together a consortium of 10 European partners for a budget of €4 million: LAAS-CNRS (France), IDIAP (Switzerland), University of Edinburgh (UK), Max Planck Institute (Germany), Oxford University (UK), Trento University (IT), PAL-Robotics (Spain), Wandercraft (France), Airbus (France), Costain (UK) and APAJH (France). "I would like to thank the people who helped me coordinate this project. It is a project put together by a consortium of young researchers. It was a great pride for me to be chosen to coordinate this project. "We wanted to prove that it was possible to generate complex motions for arbitrary robots with arms and legs interacting in a dynamic environment in real time.