MIT scientists have developed two machine-learning models, the "PRISM" neural network and a logistic regression model, for early detection of pancreatic cancer. These models outperformed current methods, detecting 35% of cases compared to the standard 10% detection rate.
MIT Policy Hackathon brings together students and professionals from around the world to tackle societal challenges using generative AI tools like ChatGPT. Winning team "Ctrl+Alt+Defeat" focuses on addressing the eviction crisis in the US.
MIT PhD students are using game theory to improve the accuracy and dependability of natural language models, aiming to align the model's confidence with its accuracy. By recasting language generation as a two-player game, they have developed a system that encourages truthful and reliable answers while reducing hallucinations.
MIT researchers have developed an automated interpretability agent (AIA) that uses AI models to explain the behavior of neural networks, offering intuitive descriptions and code reproductions. The AIA actively participates in hypothesis formation, experimental testing, and iterative learning, refining its understanding of other systems in real time.
Researchers at MIT and IBM have developed a new method called "physics-enhanced deep surrogate" (PEDS) that combines a low-fidelity physics simulator with a neural network generator to create data-driven surrogate models for complex physical systems. The PEDS method is affordable, efficient, and reduces the training data needed by at least a factor of 100 while achieving a target error of 5 per...
The article discusses the importance of understanding context windows in Transformer training and usage, particularly with the rise of proprietary LLMs and techniques like RAG. It explores how different factors affect the maximum context length a transformer model can process and questions whether bigger is always better.
Google Brain introduced Transformer in 2017, a flexible architecture that outperformed existing deep learning approaches, and is now used in models like BERT and GPT. GPT, a decoder model, uses a language modeling task to generate new sequences, and follows a two-stage framework of pre-training and fine-tuning.
This article explores the limitations of using Large Language Models (LLMs) for conversational data analysis and proposes a 'Data Recipes' methodology as an alternative. The methodology allows for the creation of a reusable Data Recipes Library, improving response times and enabling community contribution.
Developing LLM applications can be both exciting and challenging, with the need to consider safety, performance, and cost. Starting with low-risk applications and adopting a "Cheap LLM First" policy can help mitigate risks and reduce the amount of work required for launch.
Generative AI applications using large language models (LLMs) offer economic value, but managing security, privacy, and compliance is crucial. This article provides guidance on addressing vulnerabilities, implementing security best practices, and architecting risk management strategies for generative AI applications.
OpenAI introduces updates to ChatGPT AI models, addressing the "laziness" issue in GPT-4 Turbo and launching the new GPT-3.5 Turbo model with lower pricing. Users have reported a decline in task completion depth with ChatGPT-4, prompting OpenAI's response.
OpenAI has released an easy-to-use web tool to create custom AI assistants without coding, requiring only a Google or Microsoft account and a $20/month OpenAI Plus subscription. Users can personalize their AI assistant's name, picture, tone, and interaction style, and enhance its knowledge by uploading specific documents.
GeForce NOW levels up PC gaming on mobile with higher-resolution support on Android, offering immersive gameplay on the go. New games added to the library include Stargate: Timekeepers, Enshrouded, and Metal: Hellsinger.
The article discusses the singular value decomposition (SVD) algorithm and the author's process of refactoring the Jacobi algorithm from the GNU Scientific Library to Python/NumPy. The author validates their from-scratch SVD function using the np.linalg.svd() function and highlights the usefulness of SVD in classical statistics and machine learning.
This article explores monocular depth estimation (MDE) and its importance in computer vision applications. It provides a walkthrough on loading and visualizing depth map data, running inference with Marigold and DPT, and evaluating depth predictions using the SUN RGB-D dataset.