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Understanding natural language involves the engagement of specific left-lateralized frontal and temporal brain regions, forming what is commonly referred to as the 'language network.' Despite significant progress, many aspects of the representations and algorithms supporting language comprehension in this network remain elusive. Recent advancements in computing power, the availability of extensive text corpora, and breakthroughs

Singapore - HPC-AI Tech, a pioneering company specializing in efficient large AI model training, is delighted to announce the successful completion of its Series A funding round, securing a total of 22 Million USD. This latest funding milestone, following two previous fundraising rounds, highlights the company's rapid growth since its establishment just 18 months ago. The

Deep visual recognition models have seen substantial performance improvements in recent years but are still typically trained for only one specific task, such as segmentation, classification, etc. Although these models often have the same core architectural backbone, there is no existing method for easily combining multiple task-specific models into one that can handle multiple tasks.

One of the most vexing public concerns regarding powerful AI systems such as large language models (LLMs) is their lack of interpretability: How can people trust a model’s output when they cannot understand how it was formed? In the new paper Finding Neurons in a Haystack: Case Studies with Sparse Probing, a research team from

The wheel, electricity and the computer are among some two dozen general purpose technologies, aka GPTs, that have greatly transformed human economies and societies. Is it just a coincidence that OpenAI’s GPT shares this initialism? In the new paper GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,