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 walk you through the basics of creating a streaming application powered by Apache Kafka, one of the most popular open source distributed event-streaming platforms used for creating real-time data pipeline and streaming apps. The application will be built using IBM Streams on IBM Cloud Pak® for Data. Description…
Summary To enable mobile users to leverage IBM Watson® services through a messenger app, complete this developer code pattern and build a framework that can act as an intermediator in connecting Watson services to WhatsApp Messenger. Description There are currently 2.4 billion users on WhatsApp, and the number keeps climbing. For medium and large businesses,…
Summary In this code pattern, learn how to use AutoAI to automatically generate a Jupyter Notebook that contains Python code of a machine learning model. Then, explore, modify, and retrain the model pipeline using Python before deploying the model in IBM Watson® Machine Learning using Watson Machine Learning APIs. Description AutoAI is a graphical tool…