Presented by ArmAI is no longer confined to the cloud or data centers. Increasingly, it’s running directly where data is created — in devices, sensors, and networks at the edge. This shift toward on-device intelligence is being driven by latency, privacy, and cost concerns that companies are confronting as they continue their investments in AI. For leadership teams, the opportunity is clear, says Chris Bergey, SVP and GM, of Arm’s Client Business: Invest in AI-first platforms that complement cloud usage, deliver real-time responsiveness, and protect sensitive data. "With the explosion of connected devices and the rise of IoT, edge AI provides a significant opportunity for organizations to gain a competitive edge through faster, more efficient AI," Bergey explains. "Tho [...]
The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]
Two days after releasing what analysts call the most powerful open-source AI model ever created, researchers from China's Moonshot AI logged onto Reddit to face a restless audience. The Beijing-b [...]
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]
In a new paper that studies tool-use in large language model (LLM) agents, researchers at Google and UC Santa Barbara have developed a framework that enables agents to make more efficient use of tool [...]
Researchers at the University of Illinois Urbana-Champaign and Google Cloud AI Research have developed a framework that enables large language model (LLM) agents to organize their experiences into a m [...]
Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, TT [...]
While the world's leading artificial intelligence companies race to build ever-larger models, betting billions that scale alone will unlock artificial general intelligence, a researcher at one of [...]
A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical constrai [...]
Cerebras Systems, the Silicon Valley chipmaker that built the world's largest commercial AI processor, erupted onto the Nasdaq on Wednesday, opening at $350 per share — nearly double its $185 I [...]