By John P. Desmond, AI Trends Editor Engineers tend to see things in unambiguous terms, which some may call Black and White terms, such as a choice between right or wrong and good and bad. The consideration of ethics in AI is highly nuanced, with vast gray areas, making it challenging for AI software engineers to…
By Lance Eliot, the AI Trends Insider We already expect that humans to exhibit flashes of brilliance. It might not happen all the time, but the act itself is welcomed and not altogether disturbing when it occurs. What about when Artificial Intelligence (AI) seems to display an act of novelty? Any such instance is bound to get our attention;…
By John P. Desmond, AI Trends Editor More companies are successfully exploiting predictive maintenance systems that combine AI and IoT sensors to collect data that anticipates breakdowns and recommends preventive action before break or machines fail, in a demonstration of an AI use case with proven value. This growth is reflected in optimistic market forecasts….
By AI Trends Staff While AI in hiring is now widely used for writing job descriptions, screening candidates, and automating interviews, it poses a risk of wide discrimination if not implemented carefully. Keith Sonderling, Commissioner, US Equal Opportunity Commission That was the message from Keith Sonderling, Commissioner with the US Equal Opportunity Commision, speaking at the AI…
By John P. Desmond, AI Trends Editor Advancing trustworthy AI and machine learning to mitigate agency risk is a priority for the US Department of Energy (DOE), and identifying best practices for implementing AI at scale is a priority for the US General Services Administration (GSA). That’s what attendees learned in two sessions at the AI…
By John P. Desmond, AI Trends Editor The AI stack defined by Carnegie Mellon University is fundamental to the approach being taken by the US Army for its AI development platform efforts, according to Isaac Faber, Chief Data Scientist at the US Army AI Integration Center, speaking at the AI World Government event held in-person and virtually…
Summary The IBM Model Asset eXchange (MAX) models that are hosted on the Machine Learning eXchange (https://ml-exchange.org/models/) have given application developers without data science experience easy access to prebuilt machine learning models. This code pattern shows how to create a simple web application to visualize the text output of a MAX model. The web app…
Summary The introduction of the IBM Model Asset eXchange (MAX) that is hosted on the Machine Learning eXchange has given application developers without data science experience easy access to prebuilt machine learning models. This code pattern shows how simple it can be to create a web app that utilizes a MAX model. The web app…
Summary This developer code pattern demonstrates how you can create your own music based on your arm movements in front of a webcam. It uses the Model Asset eXchange (MAX) Human Pose Estimator model and TensorFlow.js. Description This code pattern is based on Veremin, but modified to use the Human Pose Estimator model from the…
Summary This IBM Developer code pattern explains how to detect human poses in a given image by using the Human Pose Estimator model from the Model Asset eXchange that is hosted on the Machine Learning eXchange. Using coordinates, the pose lines that are created by the model are assembled into full body poses for each…