Approximate Nearest-Neighbor Search (ANNS) efficiently finds data items whose embeddings are close to that of a given query in a high-dimensional space, aiming to balance accuracy with speed. Used in recommendation systems, image and video retrieval, natural language processing, and retrieval-augmented generation (RAG), ANNS algorithms such as IVFPQ, HNSW graphs, Annoy, and MRPT utilize graph, tree, clustering, and quantization techniques to navigate large vector spaces. Despite this progress, ANNS systems spend up to 99% of query time to compute distances in their final refinement phase. Such transforms compact over 90% of signal energy into the first half of dimensions, enabling early candidate pruning with partial distance computations. Experiments across diverse datasets--from image-based CIFAR-10 and GIST to modern embedding spaces including OpenAI's Ada 2 and Large 3--demonstrate that P The proliferation of large-scale neural embeddings has transformed machine learning applications, from computer vision and recommendation systems (Lowe, 2004; Koren et al., 2009) to bioinformat-ics (Altschul et al., 1990) and modern retrieval-augmented generation (RAG) systems (Lewis et al., 2020; Gao et al., 2023). As embedding models evolve from hundreds to thousands of dimensions-- exemplified by OpenAI's text-embedding-3-large (Neelakantan et al., 2022)--the demand for efficient and scalable real-time Approximate Nearest-Neighbor Search (ANNS) intensifies.Figure 1: Common ANNS operations on vector databases. Current ANNS methods fall into four major categories: graph-based, clustering-based, tree-based, and hash-based. Tree-based methods, including kd-trees (Bentley, 1975) and FLANN (Muja & Lowe, 2014), recursively divide the space but degrade in high dimensions due to the curse of dimensionality. Finally, hash-based methods, such as LSH (Indyk & Motwani, 1998; Andoni & Indyk, 2006) and multi-probe LSH (Lv et al., 2007), map points into buckets so that similar points are likely to collide. Despite this diversity, all such methods operate in two phases (Babenko & Lempitsky, 2016): filtering and refinement (or verification).

Hallucination in generative AI is often treated as a technical failure to produce factually correct output. Yet this framing underrepresents the broader significance of hallucinated content in language models, which may appear fluent, persuasive, and contextually appropriate while conveying distortions that escape conventional accuracy checks. This paper critically examines how regulatory and evaluation frameworks have inherited a narrow view of hallucination, one that prioritises surface verifiability over deeper questions of meaning, influence, and impact. We propose a layered approach to understanding hallucination risks, encompassing epistemic instability, user misdirection, and social-scale effects. Drawing on interdisciplinary sources and examining instruments such as the EU AI Act and the GDPR, we show that current governance models struggle to address hallucination when it manifests as ambiguity, bias reinforcement, or normative convergence. Rather than improving factual precision alone, we argue for regulatory responses that account for languages generative nature, the asymmetries between system and user, and the shifting boundaries between information, persuasion, and harm.

ChatGPT's new browser has potential, if you're willing to pay A few minutes into using ChatGPT Atlas, the new internet browser from OpenAI, I ran into quite a big road block. This isn't like Google Chrome, which is used by roughly 60% of people. It's all built around a chatbot you're meant to talk to to surf the web. Messages limit reached, read one note. No available models support the tools in use, said another.

OpenAI's Atlas Wants to Be the Web's Tour Guide. In OpenAI's new Atlas browser, the Ask ChatGPT sidebar is moderately helpful at best. OpenAI's recently launched Atlas browser is a fascinating inversion of what users may expect from a browser, centering AI answers above traditional web links. Every click in a regular browser is a chance to see a new part of the web. Every click in Atlas is a chance to use ChatGPT .

Parents of Adam Raine update their lawsuit against OpenAI, alleging the company twice relaxed ChatGPT safety protocols around suicide discussions before their son's death.

Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you're writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. Those data aren't enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task.

What we enjoy about generative A.I. may also be its ultimate limitation: we want to see ourselves. During the past few weeks, I've seen a proliferation of A.I.-generated video in my social-media feeds and group texts. The more impressive--or, at least, more personalized--of these have been the work of Sora 2, the updated version of OpenAI's video-generation platform, which the company released on an invitation-only basis at the end of September. This iteration of Sora comes with a socially networked app, and it appears to be much better at integrating you and your friends, say, into a stock scene. What this means is that, when you open up Sora 2, you'll likely see a video of someone you know winning a Nobel Prize, getting drafted into the N.B.A., or flying a bomber plane in the Second World War.

Large Vision Language models have seen huge application in several sports use-cases recently. Most of these works have been targeted towards a limited subset of popular sports like soccer, cricket, basketball etc; focusing on generative tasks like visual question answering, highlight generation. This work analyzes the applicability of the modern video foundation models (both encoder and decoder) for a very niche but hugely popular dance sports - breakdance. Our results show that Video Encoder models continue to outperform state-of-the-art Video Language Models for prediction tasks. We provide insights on how to choose the encoder model and provide a thorough analysis into the workings of a finetuned decoder model for breakdance video classification.

It is often claimed that machine learning-based generative AI products will drastically streamline and reduce the cost of legal practice. This enthusiasm assumes lawyers can effectively manage AI's risks. Cases in Australia and elsewhere in which lawyers have been reprimanded for submitting inaccurate AI-generated content to courts suggest this paradigm must be revisited. This paper argues that a new paradigm is needed to evaluate AI use in practice, given (a) AI's disconnection from reality and its lack of transparency, and (b) lawyers' paramount duties like honesty, integrity, and not to mislead the court. It presents an alternative model of AI use in practice that more holistically reflects these features (the verification-value paradox). That paradox suggests increases in efficiency from AI use in legal practice will be met by a correspondingly greater imperative to manually verify any outputs of that use, rendering the net value of AI use often negligible to lawyers. The paper then sets out the paradox's implications for legal practice and legal education, including for AI use but also the values that the paradox suggests should undergird legal practice: fidelity to the truth and civic responsibility.

Generative Artificial Intelligence (GenAI) presents transformative opportunities for organizations, yet both midsize organizations and larger enterprises face distinctive adoption challenges. Midsize organizations encounter resource constraints and limited AI expertise, while enterprises struggle with organizational complexity and coordination challenges. Existing technology adoption frameworks, including TAM (Technology Acceptance Model), TOE (Technology Organization Environment), and DOI (Diffusion of Innovations) theory, lack the specificity required for GenAI implementation across these diverse contexts, creating a critical gap in adoption literature. This paper introduces FAIGMOE (Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises), a conceptual framework addressing the unique needs of both organizational types. FAIGMOE synthesizes technology adoption theory, organizational change management, and innovation diffusion perspectives into four interconnected phases: Strategic Assessment, Planning and Use Case Development, Implementation and Integration, and Operationalization and Optimization. Each phase provides scalable guidance on readiness assessment, strategic alignment, risk governance, technical architecture, and change management adaptable to organizational scale and complexity. The framework incorporates GenAI specific considerations including prompt engineering, model orchestration, and hallucination management that distinguish it from generic technology adoption frameworks. As a perspective contribution, FAIGMOE provides the first comprehensive conceptual framework explicitly addressing GenAI adoption across midsize and enterprise organizations, offering actionable implementation protocols, assessment instruments, and governance templates requiring empirical validation through future research.