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Artificial Intelligence

The brain’s language network has to exert more effort when dealing with complicated and unfamiliar sentences.

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.…

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AWS Inferentia and AWS Trainium provide the most economical solution for deploying Llama 3 models via Amazon SageMaker JumpStart.

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…

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A paper on Artificial Intelligence authored by MIT and Harvard exhibits an AI methodology to automate hypothesis generation and testing in a virtual environment, achievable with the implementation of SCMs.

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…

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Begin exploring Amazon Titan Text Embeddings V2: An innovative embeddings model presented by Amazon Bedrock.

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),…

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Amazon Personalize introduces new features that support larger product catalogs and decrease latency.

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…

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Transform customer contentment by incorporating custom reward structures for your business on Amazon SageMaker.

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…

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PyTorch Launches ExecuTorch Alpha: A Comprehensive Solution Concentrating on Implementation of Substantial Language and Machine Learning Models to the Periphery.

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…

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Adjusting AdvPrompter: A New AI Technique for Creating Understandably Written Adversarial Prompts

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…

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The AI research paper by Princeton and Stanford presents CRISPR-GPT as a groundbreaking enhancement for gene-editing.

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…

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