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.
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.
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.
This article explores TPU vs GPU differences in architecture, performance, energy efficiency, cost, and practical implementation, helping engineers and designers choose the right accelerator for AI workloads today!
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Nakahira received a Young Investigator Award from the Japan Science and Technology Agency for developing a machine that builds autonomous systems.
Princeton researchers have developed a new tool to speed the discovery of advanced materials known as metal organic frameworks, or MOFs.
EPFL roboticists have shown that when a modular robot shares power, sensing, and communication resources among its individual units, it is significantly more resistant to failure than traditional robotic systems, where the breakdown of one element often means a loss of functionality.
MIT researchers' DiffSyn model offers recipes for synthesizing new materials, enabling faster experimentation and a shorter journey from hypothesis to use.
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.
4 minutes read
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.
3 minutes read
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.
17 minutes read
Discover how computer vision is solving quality challenges in metal manufacturing through enhanced defect detection, proactive process monitoring, and flexible integration solutions.
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.
In this episode, we explore how AI technology is revolutionizing the detection and monitoring of infrastructure defects and learn how these innovative solutions are enhancing safety, improving maintenance, and preventing failures in critical infrastructure systems
11 minutes read
Alumni-founded Ambience Healthcare automates routine tasks for clinicians before, during, and after patient visits.
Robots, combined with artificial intelligence (AI), are transforming industries such as manufacturing, healthcare, and research.
10 minutes read
Edge AI is the process of running artificial intelligence (AI) and machine learning (ML) algorithms on computing devices at the periphery of a network, rather than on large cloud servers.
Similar to visual data, collecting and curating sound data that accurately reflects real-world scenarios is a major hurdle in training effective machine learning models.
2 minutes read
