Latest from IBM Developer : Locate and count items with object detection

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. Level Topic Type 100 Introduction to computer vision Article 101 Introduction to IBM Maximo Visual Inspection Article 201 Build and deploy an IBM Maximo Visual Inspection model and use it in an iOS app Tutorial 202 Locate and count…

Latest from IBM Developer : Validate computer vision deep learning models

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. Level Topic Type 100 Introduction to computer vision Article 101 Introduction to IBM Maximo Visual Inspection Article 201 Build and deploy an IBM Maximo Visual Inspection model and use it in an iOS app Tutorial 202 Locate and count…

Latest from IBM Developer : Object tracking in video with OpenCV and Deep Learning

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. Level Topic Type 100 Introduction to computer vision Article 101 Introduction to IBM Maximo Visual Inspection Article 201 Build and deploy an IBM Maximo Visual Inspection model and use it in an iOS app Tutorial 202 Locate and count…

Latest from MIT Tech Review – To accelerate business, build better human-machine partnerships

Businesses that want to be digital leaders in their markets need to embrace automation, not only to augment existing capabilities or to reduce costs but to position themselves to successfully maneuver the rapid expansion of IT demand ushered in through digital innovation. “It’s a scale issue,” says John Roese, global chief technology officer at Dell…

Latest from Google AI – Interpretable Deep Learning for Time Series Forecasting

Posted by Sercan O. Arik, Research Scientist and Tomas Pfister, Engineering Manager, Google Cloud Multi-horizon forecasting, i.e. predicting variables-of-interest at multiple future time steps, is a crucial challenge in time series machine learning. Most real-world datasets have a time component, and forecasting the future can unlock great value. For example, retailers can use future sales…

Latest from Google AI – A Fast WordPiece Tokenization System

Posted by Xinying Song, Staff Software Engineer and Denny Zhou, Senior Staff Research Scientist, Google Research Tokenization is a fundamental pre-processing step for most natural language processing (NLP) applications. It involves splitting text into smaller units called tokens (e.g., words or word segments) in order to turn an unstructured input string into a sequence of…

Latest from Google AI – More Efficient In-Context Learning with GLaM

Posted by Andrew M Dai and Nan Du, Research Scientists, Google Research, Brain Team Large language models (e.g., GPT-3) have many significant capabilities, such as performing few-shot learning across a wide array of tasks, including reading comprehension and question answering with very few or no training examples. While these models can perform better by simply…

Latest from MIT : A tool to speed development of new solar cells

In the ongoing race to develop ever-better materials and configurations for solar cells, there are many variables that can be adjusted to try to improve performance, including material type, thickness, and geometric arrangement. Developing new solar cells has generally been a tedious process of making small changes to one of these parameters at a time….

Latest from MIT : Machine-learning system flags remedies that might do more harm than good

Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death. Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient’s deterioration, so choosing…