Posted by Oran Lang and Inbar Mosseri, Software Engineers, Google Research Neural networks can perform certain tasks remarkably well, but understanding how they reach their decisions — e.g., identifying which signals in an image cause a model to determine it to be of one class and not another — is often a mystery. Explaining a…
Imagine a team of physicians using a neural network to detect cancer in mammogram images. Even if this machine-learning model seems to be performing well, it might be focusing on image features that are accidentally correlated with tumors, like a watermark or timestamp, rather than actual signs of tumors. To test these models, researchers use…
Posted by Sneha Kudugunta, Research Software Engineer and Orhan Firat, Research Scientist, Google Research Scaling large language models has resulted in significant quality improvements natural language understanding (T5), generation (GPT-3) and multilingual neural machine translation (M4). One common approach to building a larger model is to increase the depth (number of layers) and width (layer…
A family gathers around their kitchen island to unbox the digital assistant they just purchased. They will be more likely to trust this new voice-user interface, which might be a smart speaker like Amazon’s Alexa or a social robot like Jibo, if it exhibits some humanlike social behaviors, according to a new study by researchers…
Posted by Carlos Riquelme, Research Scientist and Joan Puigcerver, Software Engineer, Google Research, Brain team Advances in deep learning over the last few decades have been driven by a few key elements. With a small number of simple but flexible mechanisms (i.e., inductive biases such as convolutions or sequence attention), increasingly large datasets, and more…
Adedolapo Adedokun has a lot to look forward to in 2023. After completing his degree in electrical engineering and computer science next spring, he will travel to Ireland to undertake an MS in intelligent systems at Trinity College Dublin as MIT’s fourth student to receive the prestigious George J. Mitchell Scholarship. But there’s more to Adedokun, who…
Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT. Attendees were entertained and challenged as they explored “the good and bad of computing,” explained CSAIL Director Professor Daniela Rus,…
Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the entire Google Research community Over the last several decades, I’ve witnessed a lot of change in the fields of machine learning (ML) and computer science. Early approaches, which often fell short, eventually gave rise to modern approaches that have been…
Every year starts with a round of predictions for the new year, most of which end up being wrong. But why fight against tradition? Here are my predictions for 2022. The safest predictions are all around AI. We’ll see more “AI as a service” (AIaaS) products. This trend started with the gigantic language model GPT-3….
Machine-learning algorithms are often referred to as a “black box.” Once data are put into an algorithm, it’s not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the “black box” problem,…