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All articles tagged with #llm

Tiny quantum tweaks give a production AI model a measurable edge
technology21 hours ago

Tiny quantum tweaks give a production AI model a measurable edge

IBM and Multiverse Computing demonstrated a quantum-classical hybrid approach that inserts Cayley-parameterized unitary adapters into a frozen Llama 3.1 8B model and runs the training-then-inference cycle on an IBM 156-qubit Quantum System Two. The result is a 1.4% reduction in perplexity with only about 6,000 extra parameters (a tiny, 0.000075% increase), enabling the hybrid model to answer certain questions correctly that the base model got wrong. The work shows a path toward quantum-enhanced AI on real hardware, but faces challenges from quantum noise and the need for future work to encode the entire quantum circuit to push perplexity further with even smaller classical needs.

via Live Science|
#ai#hybrid-quantum-classical#llm
ArXiv Tightens Rules: One-Year Ban for AI Slop and Peer-Reviewed Gatekeeping
technology10 days ago

ArXiv Tightens Rules: One-Year Ban for AI Slop and Peer-Reviewed Gatekeeping

ArXiv will ban authors for a year if there is incontrovertible evidence that LLM-generated content in a submission hasn’t been checked (e.g., hallucinated references or misleading meta-comments), with future submissions requiring acceptance at a reputable peer‑reviewed venue. This strengthens efforts to curb AI-generated “slop” and builds on prior policies restricting AI-heavy content to peer‑reviewed publications; bans are subject to moderator review and author appeals.

via The Verge|
#ai#arxiv#llm
AI Writing Footprint: Heavy AI Use Alters Meaning and Voice
technology2 months ago

AI Writing Footprint: Heavy AI Use Alters Meaning and Voice

A peer‑reviewed study from West Coast universities finds heavy reliance on large language models (LLMs) reshapes both meaning and style in human writing. In experiments on the money–happiness question, essays written with heavy AI use were neutral far more often (69% higher) and participants produced 50% fewer pronouns with fewer personal anecdotes. LLM edits also replaced more words than human edits, often changing the essays’ meaning. Researchers warn of long‑term impacts on thought, language, and institutions, and say ideal LLMs would mirror a writer’s voice rather than overwrite it.

via NBC News|
#ai#creativity#llm
The Agent Wave: AI's Real Demand, Not a Bubble
technology2 months ago

The Agent Wave: AI's Real Demand, Not a Bubble

Not in a bubble: the rise of agentic AI—where a harness guides the model and verifies results—drives sustained, higher compute demand and shifts value to integrated AI providers. Thompson traces three inflection points (ChatGPT, o1 reasoning, Opus 4.5/Codex/Claude enabling agents) and shows how enterprise adoption (e.g., Microsoft's Copilot Cowork) will amplify productivity and compute demand, while Apple leans on licensing. The result is lasting demand and fewer people needed to unlock AI's impact, making the investment case for AI capex more durable than hype suggests.

via Stratechery by Ben Thompson|
#agents#ai#compute
Adaptive drafting speeds up reasoning LLM training using idle compute
technology2 months ago

Adaptive drafting speeds up reasoning LLM training using idle compute

MIT researchers introduce Taming the Long Tail (TLT), an adaptive speculative-decoding framework that trains a lightweight “drafter” on idle processors to predict the outputs of large reasoning LLMs, with an adaptive rollout engine selecting the best strategy for each batch. This speeds reinforcement-learning–based training by 70–210% while preserving accuracy, and the drafter can also be reused for efficient deployment. The approach aims to reduce training cost and energy for complex AI models and has been tested across multiple models and datasets.

via MIT News|
#adaptive-training#ai#llm
Boeing Unveils Space-Grade AI, Pushing BA Higher on Edge-Computing Breakthrough
business3 months ago

Boeing Unveils Space-Grade AI, Pushing BA Higher on Edge-Computing Breakthrough

Boeing engineers demonstrate a space-qualified edge AI by running a compact large language model on standard hardware to autonomously analyze satellite telemetry, a development that helped BA stock rise about 2%. The story also covers a Supreme Court denial to hear a Southwest pilots’ union case, while analysts still rate BA as a Strong Buy with roughly 18.8% upside based on a $278 target after a year of gains.

via TipRanks|
#ai#ba#boeing
AI-assisted Arkanix Stealer: a fleeting dark-web info-stealer experiment
technology3 months ago

AI-assisted Arkanix Stealer: a fleeting dark-web info-stealer experiment

Kaspersky researchers say Arkanix Stealer, promoted on dark-web forums in Oct 2025, was likely an AI-assisted, short-lived information-stealer project with Python and native C++ versions, a Discord community, and a referral scheme. It could harvest browser data (including 0Auth2 tokens), cryptocurrency wallet data, and credentials from Telegram and Discord, plus local-file exfiltration and modular plugins. The premium variant added anti-sandbox/debugging, RDP credential theft, and advanced post-exploitation tools like ChromElevator to bypass protections. The operation’s unclear purpose points to rapid, low-cost AI-driven malware development rather than a sustained campaign, with IoCs published by Kaspersky.

via BleepingComputer|
#cybersecurity#dark-web#information-stealer
Training AI on Low-Quality Data Causes Cognitive Decline
technology7 months ago

Training AI on Low-Quality Data Causes Cognitive Decline

Researchers from Texas A&M, the University of Texas, and Purdue University have proposed the 'LLM brain rot hypothesis,' suggesting that training large language models on low-quality 'junk' data, such as trivial or sensationalist tweets, can cause lasting cognitive decline in these models, similar to human attention and memory issues caused by internet overuse.

via Ars Technica|
#ai-research#brain-rot#junk-data
Apple research reveals LLMs gain from classic productivity techniques
technology9 months ago

Apple research reveals LLMs gain from classic productivity techniques

A study by Apple researchers demonstrates that large language models (LLMs) can significantly improve their performance and alignment by using a simple checklist-based reinforcement learning method called RLCF, which scores responses based on checklist items. This approach enhances complex instruction following and could be crucial for future AI-powered assistants, although it has limitations in safety alignment and applicability to other use cases.

via 9to5Mac|
#ai-alignment#apple#checklist-feedback