Sarah Alnegheimish’s research interests reside at the intersection of machine learning and systems engineering. Her objective: to make machine learning systems more accessible, transparent, and trustworthy. Alnegheimish is a PhD student in Principal Research Scientist Kalyan Veeramachaneni’s Data-to-AI group in…
Scientists at the McGovern Institute for Brain Research at MIT and the Broad Institute of MIT and Harvard have re-engineered a compact RNA-guided enzyme they found in bacteria into an efficient, programmable editor of human DNA. The protein they created,…
In natural language processing (NLP), RL methods, such as reinforcement learning with human feedback (RLHF), have been utilized to enhance model outputs by optimizing responses based on feedback signals. A specific variant, reinforcement learning with verifiable rewards (RLVR), extends this…
Human reasoning naturally operates through abstract, non-verbal concepts rather than strictly relying on discrete linguistic tokens. However, current LLMs are limited to reasoning within the boundaries of natural language, producing one token at a time through predefined vocabulary. This token-by-token…
Diffusion models, known for their success in generating high-quality images, are now being explored as a foundation for handling diverse data types. These models denoise data and reconstruct original content from noisy inputs. This ability makes diffusion models promising for…
In this tutorial, we introduce a streamlined approach for extracting, processing, and analyzing YouTube video transcripts using Lyzr, an advanced AI-powered framework designed to simplify interaction with textual data. Leveraging Lyzr’s intuitive ChatBot interface alongside the youtube-transcript-api and FPDF, users…
The rise of artificial intelligence resurfaces a question older than the abacus: If we have a tool to do it for us, why learn to do it ourselves? The answer, argues MIT electrical engineering and computer science (EECS) Professor Devavrat…
In this tutorial, we implement the Agent2Agent collaborative framework built atop Google’s Gemini models. The guide walks through the creation of specialized AI personas, ranging from data scientists and product strategists to risk analysts and creative innovators. It demonstrates how…
Mistral has introduced its Agents API, a framework designed to facilitate the development of AI agents capable of executing a variety of tasks including running Python code, generating images, and performing retrieval-augmented generation (RAG). This API aims to provide a…
Multi-modal large language models (MLLMs) have shown great progress as versatile AI assistants capable of handling diverse visual tasks. However, their deployment as isolated digital entities limits their potential impact. The growing demand to integrate MLLMs into real-world applications like…
