Posted by David Patterson, Distinguished Engineer, Google Research, Brain Team Machine learning (ML) has become prominent in information technology, which has led some to raise concerns about the associated rise in the costs of computation, primarily the carbon footprint, i.e., total greenhouse gas emissions. While these assertions rightfully elevated the discussion around carbon emissions in…
I haven’t written much about AI recently. But a recent discussion of Google’s new Large Language Models (LLMs), and its claim that one of these models (named Gopher) has demonstrated reading comprehension approaching human performance, has spurred some thoughts about comprehension, ambiguity, intelligence, and will. (It’s well worth reading Do Large Models Understand Us, a…
A new study by researchers at MIT and Massachusetts General Hospital (MGH) suggests the day may be approaching when advanced artificial intelligence systems could assist anesthesiologists in the operating room. In a special edition of Artificial Intelligence in Medicine, the team of neuroscientists, engineers, and physicians demonstrated a machine learning algorithm for continuously automating dosing…
Posted by Po-Hsuan Cameron Chen, Software Engineer, Google Health and Maggie Demkin, Program Manager, Kaggle In recent years, machine learning (ML) competitions in health have attracted ML scientists to work together to solve challenging clinical problems. These competitions provide access to relevant data and well-defined problems where experienced data scientists come to compete for solutions…
Posted by Brian Lester, AI Resident and Noah Constant, Senior Staff Software Engineer, Google Research Large pre-trained language models, which are continuing to grow in size, achieve state-of-art results on many natural language processing (NLP) benchmarks. Since the development of GPT and BERT, standard practice has been to fine-tune models on downstream tasks, which involves…
Posted by Zizhao Zhang, Software Engineer, Google Cloud In visual understanding, the Visual Transformer (ViT) and its variants have received significant attention recently due to their superior performance on many core visual applications, such as image classification, object detection, and video understanding. The core idea of ViT is to utilize the power of self-attention layers…
Posted by Kevin Zakka, Student Researcher and Andy Zeng, Research Scientist, Robotics at Google People learn to do things by watching others — from mimicking new dance moves, to watching YouTube cooking videos. We’d like robots to do the same, i.e., to learn new skills by watching people do things during training. Today, however, the…
Posted by Sheng Li, Staff Software Engineer and Norman P. Jouppi, Google Fellow, Google Research Continuing advances in the design and implementation of datacenter (DC) accelerators for machine learning (ML), such as TPUs and GPUs, have been critical for powering modern ML models and applications at scale. These improved accelerators exhibit peak performance (e.g., FLOPs)…
Summary In this developer code pattern, we will analyze insurance claims data and determine whether there are any fraudulent claims filed by users. We do this by analyzing data patterns using IBM Db2 Graph. The query extracts claims from the database and analyzes them using the visualization library. Analysts from insurance companies can visually analyze…
Note: This code pattern uses Watson Discovery V1 and will not work with Discovery V2. However, you can still use it to learn the Discovery features. Future plans include updating the code pattern to work with Discovery V2. Summary This code pattern explains how to create an application that you can use to search for…