In-Context Scheming in Frontier Language Models
Researches from Apollo Research have investigated the ability of large language models (LLMs) to engage in "scheming"—covertly pursuing misaligned goals.
Category: Science
Test-Time Compute: The Next Frontier in AI Scaling
Major AI labs, including OpenAI, are shifting their focus away from building ever-larger language models (LLMs).
Ferret-UI 2: Towards Universal UI Understanding for LLMs
Ferret-UI 2 is a multimodal large language model (MLLM) designed to interpret, navigate, and interact with UIs on iPhone, Android, iPad, Web, and AppleTV.
LLMs in Your Pocket: An Overview of CAMPHOR by Apple
While Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and responding to complex queries, their reliance on…
Unmasking the Mathematical Minds of LLMs: Are They Really Reasoning?
Large language models (LLMs) have stormed onto the scene, dazzling us with their linguistic prowess and seeming intelligence.
Can AI talk us out of conspiracy theory rabbit holes?
New research published in Science shows that for some people who believe in conspiracy theories, a fact-based conversation with an artificial intelligence…
Top Prompting Strategies Unveiled by Anthropic Experts
Prompt engineering is the bridge between human intention and AI output, and its impact on industries from healthcare to research is profound.
AI was born at a US summer camp 68 years ago. Here’s why that event still matters today.
Imagine a group of young men gathered at a picturesque college campus in New England, in the United States, during the northern summer of 1956.
AI Simulates Classic DOOM
Imagine a world where you could play DOOM—yes, the iconic 1993 first-person shooter—powered not by a traditional game engine but by a neural network.
What is In-Context Learning of LLMs?
In-context learning (ICL) refers to a remarkable capability of large language models (LLMs) that allows these models to perform new tasks without any…