Combining technology, education, and human connection to improve online learning

Combining technology, education, and human connection to improve online learning

MIT Morningside Academy for Design (MAD) Fellow Caitlin Morris is an architect, artist, researcher, and educator who has studied psychology and used online learning tools to teach herself coding and other skills. She’s a soft-spoken observer, with a keen interest in how people use space and respond to their environments. Combining her observational skills with active community engagement,…

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ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks

ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks

LLMs primarily enhance accuracy through scaling pre-training data and computing resources. However, the attention has shifted towards alternate scaling due to finite data availability. This includes test-time training and inference compute scaling. Reasoning models enhance performance by emitting thought processes before answers, initially through CoT prompting. Recently, reinforcement learning (RL) post-training has been used. Scientific…

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How AI Agents Are Transforming the Education Sector: A Look at Kira Learning and Beyond

How AI Agents Are Transforming the Education Sector: A Look at Kira Learning and Beyond

Today’s classrooms are changing fast because of Artificial Intelligence (AI). AI agents are now part of how teaching and learning happen. They do more than automate tasks. These agents help teachers provide personal support and give students feedback that fits their own learning style. Kira Learning is a platform leading this change. It uses AI…

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Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford University explored the generalization capabilities of these two…

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A Coding Implementation on Introduction to Weight Quantization: Key Aspect in Enhancing Efficiency in Deep Learning and LLMs

A Coding Implementation on Introduction to Weight Quantization: Key Aspect in Enhancing Efficiency in Deep Learning and LLMs

In today’s deep learning landscape, optimizing models for deployment in resource-constrained environments is more important than ever. Weight quantization addresses this need by reducing the precision of model parameters, typically from 32-bit floating point values to lower bit-width representations, thus yielding smaller models that can run faster on hardware with limited resources. This tutorial introduces…

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Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle “stop-and-go” waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient…

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Millions of new materials discovered with deep learning

Millions of new materials discovered with deep learning

Research Published 29 November 2023 Authors Amil Merchant and Ekin Dogus Cubuk AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies Modern technologies from computer chips and batteries to solar panels rely on inorganic crystals. To enable new technologies, crystals must be stable otherwise they can decompose,…

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