Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

Cambridge, MA – Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions.

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Randy Shoup discusses the "Velocity ...

In this post, we share the motivations, design choices, experiments, and learnings that informed its development, as well as an evaluation of the model’s performance and guidance on how to use it. Our ...

I've often wondered if the way in which individuals view and treat their own and other dogs is related to how they view animal-human relationships in general. Based on discussions with many people, I ...

“Plain language” has been defined by the International Standards Organization as “communication that puts readers first.” (ISO 24495-1) Ergonomics aims to design work for people. Logically, one might ...

Smartphone use now exceeds three hours a day on average, while total daily screen time for many adults crosses six hours. This constant close-up focus has made eye fatigue, dryness, blurred vision, ...

Dietitians say you shouldn't take these vitamins in the morning US, Israel launch strikes against Iranian military infrastructure I tried club sandwiches from Subway, Jimmy John's, and Jersey Mike's, ...

DeepSeek published a paper outlining a more efficient approach to developing AI, illustrating the Chinese artificial intelligence industry’s effort to compete with the likes of OpenAI despite a lack ...

According to @SciTechera, a new AI training approach applies next-token prediction—commonly used in language models—to Vision AI by treating visual embeddings as sequential tokens. This method for ...

Speculative decoding is a widely adopted technique for accelerating inference in large language models (LLMs), yet its application to vision-language models (VLMs) remains underexplored, with existing ...