By Shashwat Chauhan Nov 12 (Reuters) - Global AI-linked stocks fell on Thursday after Nvidia CEO Jensen Huang resigned following a deepening scandal around his affairs, upending the chipmaker's leadership and posing fresh risks to a sector already reeling with funding and regulatory concerns.
As artificial intelligence usage expands, research is showing 50% of American adults feel more concerned than excited about it. Is Humanizing AI agents the answer?
Mount Thor has raised $45 million in equity funding to make Apple hardware available as cloud infrastructure for AI research and deployment. The San Francisco startup is targeting a growing requirement for computer-use
Kindo has raised $30 million in a financing expansion to expand its enterprise AI agent management platform, bringing its total capital raised to $57.6 million. The financing was co-led by Crosspoint Capital and
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Chinese AI companies are trying to rent Nvidia-powered computing capacity outside China, worried that the scandal involving the US chipmaker's CEO Jensen Huang could spur Washington to tighten restrictions on their remote use of advanced processors,
Jensen Huang has resigned from Nvidia following an affair, which could have ramifications for national security as the U.S. and China race to develop the most advanced AI infrastructure.
We present the Spoken Wikipedia Presentation Corpus, an extension of the Spoken Wikipedia Corpora featuring LLM-generated slide decks for multimodal ASR. Slides are created from LLM-segmented sections using a hybrid pipeline that combines LLM-based content planning with…
Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural-language expression space. Consequently, they inherently incur excessive linguistic overhead, leading to information dilution…
Probabilistic Graphical Models (PGMs), especially Bayesian Networks (BNs), expose directed structure and probabilistic parameters, making them natural symbolic targets for neurosymbolic AI. Yet training text-to-parameterized-BN systems requires paired text-to-BN resources…
Large language models (LLMs) achieve strong performance but suffer from slow and costly inference. Existing acceleration methods often lead to noticeable performance degradation, while Mixture-of-Experts (MoE) models require extensive computational resources. In this paper, we…