This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.
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EPFL research investigating the potential impact on education of AI assistants has found that systems like GPT-4 can answer up to 85% of university assessment questions correctly.
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The method could help communities visualize and prepare for approaching storms.
Stanford engineers are testing foods using an approach that combines a three-dimensional mechanical technique and AI to mimic our sensory experience.
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Artificial intelligence (AI) is rapidly transforming healthcare by leveraging machine learning techniques to analyze medical images, patient data, and genetic information with unprecedented speed and accuracy, significantly improving early disease detection and enhancing diagnostic precision.
Optical sensors are the “eyes” of industrial systems, relying on robust peripherals for reliable image processing. As sensors grow more powerful, challenges like data management, mechanical stress, and thermal issues in compact designs demand innovative solutions.
Large language models (LLMs) are increasingly automating tasks like translation, text classification and customer service. But tapping into an LLM’s power typically requires users to send their requests to a centralized server — a process that’s expensive, energy-intensive and often slow.
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Fine-tuning large language models adapts pre-trained models to specific tasks or domains using tailored datasets, while Retrieval-Augmented Generation (RAG) combines retrieval systems with generative models to dynamically incorporate external, up-to-date knowledge into outputs.
A team of MIT CSAIL researchers have developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments.
Smarter use of processor speeds saves energy without compromising training speed and performance.
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In this episode, we explore how AI co-pilots are equipping doctors with powerful tools to enhance decision-making and patient care and discuss how it could impact you - a potential patient - in the not distant future.
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Large Language Model (LLM) training involves teaching AI models to understand and generate human-like text by processing vast amounts of data, significantly enhancing their language comprehension and production capabilities.
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
Discover how Shadow Robot’s DEX-EE gripper is advancing robotic dexterity with AI-driven precision.
In the current AI zeitgeist, sequence models have skyrocketed in popularity for their ability to analyze data and predict what to do next. For instance, you’ve likely used next-token prediction models like ChatGPT, which anticipate each word (token) in a sequence to form answers to users’ queries.
