Researchers from MIT and other institutions have discovered the key to why AI chatbot conversations can break down and developed a solution that enables continuous dialogue. The issue lies in the chatbot's key-value cache (akin to a conversational memory). In some models, earlier data points are discarded when the cache reaches its limit, causing the…
The MIT Morningside Academy for Design (MAD) unveiled the 2024 Design Fellows at an event held at the MIT Museum on May 1, 2024. The Academy has continually supported MIT graduate students since its inception in 2022 by providing them with a fellowship enabling the pursuit of design research and projects, along with community-building. Interns…
In the field of machine learning, multi-task learning (MTL) is a crucial aspect which enables the simultaneous training of interrelated algorithms. Given its ability to enhance model generalizability, it has been successfully utilized in various fields such as biomedicine, computer vision, and natural language processing. However, combining different types of tasks such as regression and…
A team of researchers from MIT and other institutions have found a way to prevent chatbots driven by large language machine-learning models from collapsing during lengthy conversations. The failure typically occurs when the key-value cache, or "conversation memory", in some methods cannot contain more information than its capacity, resulting in the first data points being…
Generative Artificial Intelligence (Gen AI) is leading to significant advancements in sectors such as science, economy, and education. At the same time, it also raises significant concerns that stem from its potential to produce robust content based on input. These advancements are leading to in-depth socio-technical studies to understand the profound implications and assessing risks…
Google AI researchers are working towards generating high-quality synthetic datasets while ensuring user privacy. The increasing reliance on large datasets for machine learning (ML) makes it essential to safeguard individuals' data. To resolve this, they use differentially private synthetic data, new datasets that are completely artificial yet embody key features of the original data.
Existing privacy-preserving…
AI researchers at Google have developed a new approach to generating synthetic datasets that maintain individuals' privacy, essential for training predictive models. With machine learning models relying increasingly on large datasets, ensuring the privacy of personal data has become critical. They achieve this privacy through differentially private synthetic data created by generating new datasets that…
Transformer-based neural networks have demonstrated remarkable capabilities in tasks such as text generation, editing and answering questions. These networks often improve as their parameters increase. Notably, some models perform optimally when small, like the 2B model MiniCPM, which fares comparably to larger models. Yet as computational resources for training these models increase, high-quality data availability…
Transformer-based neural networks have demonstrated proficiency in a variety of tasks, such as text generation, editing, and question-answering. Perplexity and end task accuracy measurements consistently show models with more parameters perform better, leading industries to develop larger models. However, in some cases, larger models do not guarantee superior performance. The 2 billion parameter model, MiniCPM,…