The Sohu AI chip created by Etched holds the title as the fastest AI chip currently available, redefining AI computation and application capabilities. It enables processing of over 500,000 tokens per second on the Llama 70B model, outperforming traditional GPUs. An 8xSohu server can even replace 160 H100 GPUs, demonstrating its superior power and efficiency.…
Multimodal large language models (MLLMs), which integrate sensory inputs like vision and language, play a key role in AI applications, such as autonomous vehicles, healthcare and interactive AI assistants. However, efficient integration and processing of visual data with textual details remain a stumbling block. The traditionally used visual representations, that rely on benchmarks such as…
Multimodal large language models (MLLMs), which integrate sensory inputs like vision and language to create comprehensive systems, have become an important focus in AI research. Their applications include areas such as autonomous vehicles, healthcare, and AI assistants, which require an understanding and processing of data from various sources. However, integrating and processing visual data effectively…
Large Language Models (LLMs) have played a notable role in enhancing the understanding and generation of natural language. They have, however, faced challenges in processing long contexts due to restrictions in context window size and memory usage. This has spawned research to address these limitations and come up with ways of making the LLMs work…
Large language models (LLMs) have made significant progress in the understanding and generation of natural language, but their application over long contexts is still limited due to constraints in context window sizes and memory usage. It's a pressing concern as the demand for LLMs' ability to handle complex and lengthy tasks is on the rise.
Various…
In the field of large language models (LLMs), multi-agent debates (MAD) pose a significant challenge due to their high computational costs. They involve multiple agents communicating with one another, all referencing each other's solutions. Despite attempts to improve LLM performance through Chain-of-Thought (CoT) prompting and self-consistency, these methods are still limited by the increased complexity…
The use of pretrained language models and their creative applications have contributed to significant improvements in the quality of information retrieval (IR). However, there are questions about the necessity and efficiency of training these models on large datasets, especially for languages with scant labeled IR data or niche domains.
Researchers from the University of Waterloo,…
Replete AI has launched Replete-Coder-Qwen2-1.5b, an artificial intelligence (AI) model with extensive capabilities in coding and other areas. Developed using a mix of non-coding and coding data, the model is designed to perform diverse tasks, making it a versatile solution for a range of applications.
Replete-Coder-Qwen2-1.5b is part of the Replete-Coder series and has been…
Natural evolution has meticulously shaped proteins over more than three billion years. Modern-day research is closely studying these proteins to understand their structures and functions. Large language models are increasingly being employed to interpret the complexities of these protein structures. Such models demonstrate a solid capacity, even without specific training on biological functions, to naturally…
Scientists from Evolutionary Scale PBC, Arc Institute, and the University of California have developed an advanced generative language model for proteins known as ESM3. The protein language model is a sophisticated tool designed to understand and forecast proteins' sequence, structure, and function. It applies the masked language modeling approach to predict masked portions of protein…
Researchers have been focusing on an effective method to leverage in-context learning in transformer-based models like GPT-3+. Despite their success in enhancing AI performance, the method's functionality remains partially understood. In light of this, a team of researchers from the University of California, Los Angeles (UCLA) examined the factors affecting in-context learning. They found that…
Advanced language models such as GPT-3+ have shown significant improvements in performance by predicting the succeeding word in a sequence using more extensive datasets and larger model capacity. A key characteristic of these transformer-based models, aptly named as "in-context learning," allows the model to learn tasks through a series of examples without explicit training. However,…