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How to build AI scaling laws for efficient LLM training and budget maximization

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering

When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate […]

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MIT affiliates win AI for Math grants to accelerate mathematical discovery

Algorithms, Artificial Intelligence, Awards, honors and fellowships, Computer science and technology, data, Funding, Mathematics, Research, School of Science, Staff

MIT Department of Mathematics researchers David Roe ’06 and Andrew Sutherland ’90, PhD ’07 are among the inaugural recipients of the Renaissance Philanthropy and XTX Markets’ AI for Math grants. Four additional MIT alumni — Anshula Gandhi ’19, Viktor Kunčak SM ’01, PhD ’07; Gireeja Ranade ’07; and Damiano Testa PhD ’05 — were also honored

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Simpler models can outperform deep learning at climate prediction

Algorithms, Artificial Intelligence, Climate change, Computer modeling, data, EAPS, Earth and atmospheric sciences, Emissions, IDSS, Machine Learning, MIT Schwarzman College of Computing, Pollution, Public health, Research, School of Science, Sustainability

Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.The team demonstrates that, in certain climate scenarios, much simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models.Their analysis also

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3 Questions: The pros and cons of synthetic data in AI

Algorithms, Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Interview, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow

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How OpenAI and Thrive are testing a new enterprise AI model

AI Business Strategy, Artificial Intelligence, business processes, business strategy, ChatGPT, data, enterprise, World of Work

Thrive Holdings’ push to modernise accounting and IT services is entering a new stage, as OpenAI prepares to take an ownership stake in the company and place its own specialists inside Thrive’s businesses. In doing so, OpenAI is testing an AI-driven model that pairs capital, sector expertise, and embedded technical teams. Thrive started its holding

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