GoogleAI researchers have developed a new tool called DIDACT (Dynamic Integrated Developer ACTivity) to help developers resolve build errors more efficiently. The tool uses machine learning (ML) technology to automate the process of identifying and rectifying build errors, focusing specifically on Java development. Build errors, which range from simple typos to complex problems like generics…
GoogleAI has introduced an ML-based solution called DIDACT (Dynamic Integrated Developer ACTivity) aimed at streamlining the tedious process of identifying and fixing build errors. This ML solution centres mainly around enhancing the Java development experience. Build errors, responsible for time wastage for developers and contributing to complexity, can include various issues, such as cryptic error…
In the wake of the introduction of ChatGPT, AI applications have increasingly adopted the Retrieval Augmented Generation (RAG), with a primary focus on improving these RAG systems to influence the future generation of AI applications. The ideal AI agents are designed to enhance the capabilities of the Language Model (LM) to solve real-world problems, especially…
FineWeb, a groundbreaking open-source dataset, developed by a consortium led by huggingface, consists of over 15 trillion tokens extracted from CommonCrawl dumps between the years 2013 and 2024. Designed to advance language model research, FineWeb has gone through a systematic processing pipeline using the datatrove library, which has rigorously cleaned and deduplicated the dataset, making…
In 2023, MIT had an eventful year filled with numerous achievements, advancements, and major breakthroughs. From the commencement address by YouTuber and ex-NASA engineer Mark Rober and the inauguration of President Sally Kornbluth, to Professor Moungi Bawendi’s Nobel Prize in Chemistry, the year marked significant milestones for the university.
MIT played an influential role in a…
In 2023, the Massachusetts Institute of Technology (MIT) had an eventful year, making remarkable advances in diverse areas from artificial intelligence to healthcare, climate change, and astrophysics. This included cutting-edge research such as inventing tools for earlier cancer detection and exploring the science behind spreading kindness.
One of the highlights was Professor Moungi Bawendi winning the…
Generative models, a class of probabilistic machine learning, have seen extensive use in various fields, such as the visual and performing arts, medicine, and physics. These models are proficient in creating probability distributions that accurately describe datasets, making them ideal for generating synthetic datasets for training data and discovering latent structures and patterns in an…
Large Language Models (LLMs) and Large Multi-modal Models (LMMs) are effective across various domains and tasks, but scaling up these models comes with significant computational costs and inference speed limitations. Sparse Mixtures of Experts (SMoE) can help to overcome these challenges by enabling model scalability while reducing computational costs. However, SMoE struggles with low expert…
Large Language Models (LLMs), while transformative for many AI applications, necessitate high computational power, especially during inference phases. This poses significant operational costs and efficiency challenges as the models become bigger and more intricate. Particularly, the computational expenses incurred when running these models at the inference stage can be intensive due to their dense activation…