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Checklists: Apple’s Game-Changing Approach to Aligning AI and Their Proven Impact Across Critical Fields
In the fast-evolving world of artificial intelligence, where large language models (LLMs) like ChatGPT and Grok are becoming integral to our daily lives, ensuring these systems are both helpful and safe is paramount. A groundbreaking new study co-authored by researchers from Apple, titled “Checklists Are Better Than Reward Models For Aligning Language Models”, introduces a…
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AGI and Global Finance: Abundance and Instability
OpenAI has warned investors that Artificial General Intelligence (AGI) could disrupt economic systems so deeply that money itself may lose relevance. According to leaked investor materials, “it may be difficult to know what role money will play in a post-AGI world.” Yet, at the same time, the company is securing billions in funding—SoftBank valuing it…
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The Overwhelming Surge of AI Crawlers: Challenges, Offenders, and the Path Forward
In the digital age, websites serve as the backbone of information dissemination, commerce, and community building. However, a growing menace is undermining this ecosystem: the relentless activity of AI crawlers and fetchers. These automated tools, designed to scrape and retrieve data for training artificial intelligence models or generating real-time responses, are inundating servers with unprecedented…
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The AI Bubble: Parallels to the Dot-Com Era and Beyond
In the realm of technological innovation, few phenomena capture the collective imagination like a speculative bubble. A bubble occurs when asset prices inflate rapidly due to hype, speculation, and investor enthusiasm, often detached from underlying fundamentals, only to burst and cause widespread financial fallout. Today, as of September 2025, the artificial intelligence (AI) sector is…
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Teaching LLMs to Ask Smarter Questions: Bayesian Experimental Design for Multi-Turn Information Gathering
Large Language Models (LLMs) have shown remarkable capabilities in understanding and generating text, but they struggle with adaptive, multi-turn information gathering – i.e. asking relevant follow-up questions based on previous answers. The paper “BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design” (arXiv:2508.21184) addresses this shortcoming by introducing a new approach that enables LLMs…
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