Ben Dickson

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

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Meta made several major announcements for robotics and embodied AI systems this week. This includes releasing benchmarks and artifacts for better understanding and interacting with the physical world. Sparsh, Digit 360 and Digit Plexus, the three research artifacts released by Meta, focus on touch perception, robot dexterity and human-robot interaction. Meta is also releasing PARTNR a

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A well-known problem of large language models (LLMs) is their tendency to generate incorrect or nonsensical outputs, often called “hallucinations.” While much research has focused on analyzing these errors from a user’s perspective, a new study by researchers at Technion, Google Research and Apple investigates the inner workings of LLMs, revealing that these models possess a

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google DeepMind and Hugging Face have just released SynthID Text, a tool for marking and detecting text generated by large language models (LLMs). SynthID Text encodes a watermark into AI-generated text in a way that helps determine if a specific LLM produced it. More importantly, it does so without modifying how the underlying LLM works or

Talker-Reasoner is inspired by the two-system thinking cognitive framework proposed by Daniel Kahneman.Read More

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Improving the capabilities of large language models (LLMs) in retrieving in-prompt information remains an area of active research that can impact important applications such as retrieval-augmented generation (RAG) and in-context learning (ICL). Microsoft Research and Tsinghua University researchers have introduced Differential Transformer (Diff Transformer), a new LLM architecture that improves performance by amplifying attention to relevant

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Large language models (LLMs) with very long context windows have been making headlines lately. The ability to cram hundreds of thousands or even millions of tokens into a single prompt unlocks many possibilities for developers.  But how well do these long-context LLMs really understand and utilize the vast amounts of information they receive? Researchers at Google

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Retrieval-augmented generation (RAG) has become a popular method for grounding large language models (LLMs) in external knowledge. RAG systems typically use an embedding model to encode documents in a knowledge corpus and select those that are most relevant to the user’s query. However, standard retrieval methods often fail to account for context-specific details that can make

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More While large language models (LLMs) are becoming increasingly effective at complicated tasks, there are many cases where they can’t get the correct answer on the first try. This is why there is growing interest in enabling LLMs to spot and correct their mistakes, also known as “self-correction.” However, current attempts at self-correction are limited and have

From explicit fact retrieval to hidden rationales, all you need to know about data-augmented LLM applications.Read More