CyberScoop reports that Congress is preparing the No FAKES Act targeting AI-generated deepfakes. This indicates a proposed legislative action rather than an enacted law.
The NO FAKES Act, a US bill targeting AI deepfakes, is set for a US Senate Judiciary Committee vote on June 18. This is a proposed legislative step, not enacted law.
The No Fakes Act, intended to curb unauthorized digital replicas, cleared the Senate Judiciary Committee. This indicates proposed federal legislation moving forward but not yet enacted.
The U.S. Department of Justice seeks to halt an air pollution lawsuit involving an xAI data center. This is a formal legal enforcement action in an ongoing case.
Illinois has passed an AI Safety Act. This is a state legislative action that may impose new AI-related compliance obligations.
Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms. We introduce DeXposure-Claw, a forecast-grounded agentic supervision system that routes LLM decisio
While LLM-powered agents offer end-to-end automation for industrial asset lifecycles, real-world Industry 4.0 deployment is hindered by latency, concurrency instability, and safety risks. We present DynAMO (Dynamic Asset Management Orchestration), a deployment-ready engine using a Plan-then-Execute architecture to generate verifiable workflow graphs. DynAMO supports both SequentialWorkflow (topolo
The ``Pre-train, then fine-tune'' paradigm has revolutionized Natural Language Processing (NLP). In this context, transferable backdoors pose a severe threat to the Pre-trained Language Models (PLMs) supply chain, yet defensive research remains nascent, primarily relying on detecting anomalies in the output feature space. We identify a critical flaw that fine-tuning on downstream tasks inevitably
Instruction fine-tuning of large language models (LLMs) often involves selecting a subset of instruction training data from a large candidate pool, using a small query set from the target task. Despite growing interest, the literature on targeted instruction selection remains fragmented and opaque: methods vary widely in selection budgets, often omit zero-shot baselines, and frequently entangle th
In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at deployment time, this paradigm supports few-shot adaptation to novel tasks. However, recent ICIL methods rely on Transformers, which have computational limitations and tend to underperform when handling longer prompts than
Capital remains concentrated in a small number of very large transactions, with one acquisition alongside other late-stage-sized events. The pattern suggests continued appetite for platform-scale assets rather than a broad spread of early checks.
Capital remains concentrated in a small number of very large transactions, with one acquisition alongside other late-stage-sized events. The pattern suggests continued appetite for platform-scale assets rather than a broad spread of early checks.
Capital remains concentrated in a small number of very large transactions, with one acquisition alongside other late-stage-sized events. The pattern suggests continued appetite for platform-scale assets rather than a broad spread of early checks.
Named moves are sparse but directional: one senior departure from a major lab and two founder launches show both churn at the top and continued company formation. The desk matters when individual moves reshape sales, research, or founding capacity.
Named moves are sparse but directional: one senior departure from a major lab and two founder launches show both churn at the top and continued company formation. The desk matters when individual moves reshape sales, research, or founding capacity.
Named moves are sparse but directional: one senior departure from a major lab and two founder launches show both churn at the top and continued company formation. The desk matters when individual moves reshape sales, research, or founding capacity.
The benchmark feed is narrow, but it still matters because it tracks a single model across multiple evaluation surfaces. That kind of clustered reporting helps show whether gains are broad or confined to one capability slice.
The benchmark feed is narrow, but it still matters because it tracks a single model across multiple evaluation surfaces. That kind of clustered reporting helps show whether gains are broad or confined to one capability slice.
The benchmark feed is narrow, but it still matters because it tracks a single model across multiple evaluation surfaces. That kind of clustered reporting helps show whether gains are broad or confined to one capability slice.