Ben Dickson

o1 does not reveal its reasoning chain, which makes it difficult to get consistent results and correct the model’s responses and logic.Read More

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Researchers at Sakana AI have developed a resource-efficient framework that can create hundreds of language models specializing in different tasks. Called CycleQD, the technique uses evolutionary algorithms to combine the skills of different models without the need for expensive and slow training processes. CycleQD can create swarms of task-specific agents that offer a more sustainable alternative

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Chinese e-commerce giant Alibaba has released the latest model in its ever-expanding Qwen family. This one is known as Qwen with Questions (QwQ), and serves as the latest open source competitor to OpenAI’s o1 reasoning model. Like other large reasoning models (LRMs), QwQ uses extra compute cycles during inference to review its answers and correct its

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The recent release of OpenAI o1 has brought great attention to large reasoning models (LRMs), and is inspiring new models aimed at solving complex problems classic language models often struggle with. Building on the success of o1 and the concept of LRMs, researchers at Alibaba have introduced Marco-o1, which enhances reasoning capabilities and tackles problems with

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI‘s o1 model has shown that inference-time scaling—using more compute during inference—can significantly boost a language model’s reasoning abilities. LLaVA-o1, a new model developed by researchers from multiple universities in China, brings this paradigm to open-source vision language models (VLMs). Early open-source VLMs typically use a direct prediction approach, generating answers without reasoning about the prompt

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Since Anthropic released the “Computer Use” feature for Claude in October, there has been a lot of excitement about what AI agents can do when given the power to imitate human interactions. A new study by Show Lab at the National University of Singapore provides an overview of what we can expect from the current generation

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More One-bit large language models (LLMs) have emerged as a promising approach to making generative AI more accessible and affordable. By representing model weights with a very limited number of bits, 1-bit LLMs dramatically reduce the memory and computational resources required to run them. Microsoft Research has been pushing the boundaries of 1-bit LLMs with its BitNet