Lara Ozkan named 2025 Marshall Scholar
The MIT senior will pursue graduate studies in the UK at Cambridge University and Imperial College London.
Basic Generative AI
If you're looking to dip your toes into the world of Artificial Intelligence and Machine Learning, "Basic Generative AI: Beginner's Guide to Artificial Intelligence, ChatGPT and Machine Learning, Practical AI…
MIT affiliates named 2024 Schmidt Futures AI2050 Fellows
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines
Ensemble models are becoming popular within the ML communities. They generate more accurate predictions through combining the predictions of multiple models. Pipelines can quickly be used to create and end-to-end…
Teaching a robot its limits, to complete open-ended tasks safely
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore…
AI in health should be regulated, but don’t forget about the algorithms, researchers say
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 2: ModelBuilder
In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta…
AI Ethics: Status of the Present, Ethical Dilemmas, and Frameworks for the Practical Mind
"AI Ethics" is a must-read for anyone interested in understanding the moral principles that should guide the development and deployment of artificial intelligence technologies. The book provides a comprehensive exploration…
Researchers reduce bias in AI models while preserving or improving accuracy
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Study: Some language reward models exhibit political bias
Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.