A new study by MIT CSAIL researchers maps the challenges of AI in software development, identifying bottlenecks and highlighting research directions to move the field forward, aiming to allow humans to focus on high-level design while automating routine tasks.
This book covers foundational topics within computer vision, with an image processing and machine learning perspective. It aims to build the reader’s intuition through visualizations and is intended for undergraduate and graduate students, as well as experienced practitioners.
PhD student Sarah Alnegheimish is developing Orion, an open-source, user-friendly machine learning framework for detecting anomalies in large-scale industrial and operational settings. She focuses on making machine learning systems accessible, transparent, and trustworthy, and is exploring repurposing pre-trained models for anomaly detection.
A machine learning library for unsupervised time series anomaly detection. Orion provides verified ML pipelines to identify rare patterns in time series data.
>"TL;DR: We unify over 23 methods in contrastive learning, dimensionality reduction, spectral clustering, and supervised learning with a single equation."
>"As the field of representation learning grows, there has been a proliferation of different loss functions to solve different classes of problems. We introduce a single information-theoretic equation that generalizes a large collection of mod- ern loss functions in machine learning. In particular, we introduce a framework that shows that several broad classes of machine learning methods are precisely minimizing an integrated KL divergence between two conditional distributions: the supervisory and learned representations. This viewpoint exposes a hidden information geometry underlying clustering, spectral methods, dimensionality re- duction, contrastive learning, and supervised learning. This framework enables the development of new loss functions by combining successful techniques from across the literature. We not only present a wide array of proofs, connecting over 23 different approaches, but we also leverage these theoretical results to create state-of-the-art unsupervised image classifiers that achieve a +8% improvement over the prior state-of-the-art on unsupervised classification on ImageNet-1K. We also demonstrate that I-Con can be used to derive principled debiasing methods which improve contrastive representation learners."
>"As the field of representation learning grows, there has been a proliferation of different loss functions to solve different classes of problems. We introduce a single information-theoretic equation that generalizes a large collection of mod- ern loss functions in machine learning. In particular, we introduce a framework that shows that several broad classes of machine learning methods are precisely minimizing an integrated KL divergence between two conditional distributions: the supervisory and learned representations. This viewpoint exposes a hidden information geometry underlying clustering, spectral methods, dimensionality re- duction, contrastive learning, and supervised learning. This framework enables the development of new loss functions by combining successful techniques from across the literature. We not only present a wide array of proofs, connecting over 23 different approaches, but we also leverage these theoretical results to create state-of-the-art unsupervised image classifiers that achieve a +8% improvement over the prior state-of-the-art on unsupervised classification on ImageNet-1K. We also demonstrate that I-Con can be used to derive principled debiasing methods which improve contrastive representation learners."
MIT researchers developed a system that uses large language models to convert AI explanations into narrative text that can be more easily understood by users, aiming to help with better decision-making about model trustworthiness.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
MIT researchers have developed a method using large language models to detect anomalies in complex systems without the need for training. The approach, called SigLLM, converts time-series data into text-based inputs for the language model to process. Two anomaly detection approaches, Prompter and Detector, were developed and showed promising results in initial tests.
News articles, videos, and podcasts related to artificial intelligence research, applications, and developments at MIT.
AI agent helping write and fix code, running tests and iterating till code passes tests or matches designs. Uses OpenAI API and aims to make coding easier.
Micro Agent is an AI agent that assists with coding, helping with code generation and iteration processes. It's a focused agent that aims to write code based on provided test cases or design screenshots. It can work in tandem with OpenAI and Anthropic APIs for better visual matching. The agent is designed with a specific focus - creating a clear test case and providing feedback on code that helps improve the generated code. Installation requires Node.js v14 or later, and it can be installed globally using npm. To get started, running the agent in interactive mode is recommended. Micro Agent can work in both unit test matching mode and visual matching mode for coding assistance. It uses a multi-agent approach and connects with Figma for high fidelity design-to-code conversions. Configuration options are available via CLI or UI.
Micro Agent is an AI agent that assists with coding, helping with code generation and iteration processes. It's a focused agent that aims to write code based on provided test cases or design screenshots. It can work in tandem with OpenAI and Anthropic APIs for better visual matching. The agent is designed with a specific focus - creating a clear test case and providing feedback on code that helps improve the generated code. Installation requires Node.js v14 or later, and it can be installed globally using npm. To get started, running the agent in interactive mode is recommended. Micro Agent can work in both unit test matching mode and visual matching mode for coding assistance. It uses a multi-agent approach and connects with Figma for high fidelity design-to-code conversions. Configuration options are available via CLI or UI.
This paper introduces Cross-Layer Attention (CLA), an extension of Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) for reducing the size of the key-value cache in transformer-based autoregressive large language models (LLMs). The authors demonstrate that CLA can reduce the cache size by another 2x while maintaining nearly the same accuracy as unmodified MQA, enabling inference with longer sequence lengths and larger batch sizes.
