Researchers from MIT, led by neuroscience associate professor Evelina Fedorenko, have used an artificial language network to identify which types of sentences most effectively engage the brain’s language processing centers. The study showed that sentences of complex structure or unexpected meaning created strong responses, while straightforward or nonsensical sentences did little to engage these areas.…
Meta Llama 3 inference is now available on Amazon Web Services (AWS) Trainium and AWS Inferentia-based instances in Amazon SageMaker JumpStart. Meta Llama 3 models are pre-trained generative text models that can be used for a range of applications, including chatbots and AI assistants. AWS Inferentia and Trainium, used with Amazon EC2 instances, provide a…
The latest advancements in econometric modeling and hypothesis testing have signified a vital shift towards the incorporation of machine learning technologies. Even though progress has been made in estimating econometric models of human behaviour, there is still much research to be undertaken to enhance the efficiency in generating these models and their rigorous examination.
Academics from…
Amazon recently announced the launch of its second-generation model for text embeddings, Amazon Titan Text Embeddings V2. Text embeddings are essential for various natural language processing (NLP) applications such as knowledge bases, language models, and recommendation systems. The Amazon Titan V2 model is optimized to support customer use cases such as Retrieval Augmented Generation (RAG),…
Amazon Personalize, a machine learning (ML) technology used for customizing user experiences, has announced the general availability of two advanced recipes named User-Personalization-v2 and Personalized-Ranking-v2. These features utilize the Transformers architecture to support larger quantities of item catalogs with lower latency.
The new features are improvements on previous versions, particularly in terms of scalability, latency, model…
The prevalence of large language models (LLM) has necessitated an efficient method of customizing these systems to align with organizational values and provide reliable and accurate customer experiences. However, with customization comes the challenge of obtaining diverse, subjective human feedback to refine the model's performance, which can be time-consuming and unscalable.
To overcome these hurdles, companies…
PyTorch recently launched the alpha version of its state-of-the-art solution, ExecuTorch, enabling the deployment of intricate machine learning models on resource-limited edge devices such as smartphones and wearables. Poor computational power and limited resources have traditionally hindered deploying such models on edge devices. PyTorch's ExecuTorch Alpha aims to bridge this gap, optimizing model execution on…
Advanced language models (LLMs) have significantly improved natural language understanding and are broadly applied in multiple areas. However, they can be sensitive to specific input prompts, prompting research into understanding this characteristic. Through exploring this behavior, prompts for learning tasks like zero-shot and in-context training are created. One such application, AutoPrompt, recognizes task-specific tokens to…
Gene editing, a vital aspect of modern biotechnology, allows scientists to precisely manipulate genetic material, which has potential applications in fields such as medicine and agriculture. The complexity of gene editing creates challenges in its design and execution process, necessitating deep scientific knowledge and careful planning to avoid adverse consequences. Existing gene editing research has…