To engineer proteins for specific functions, scientists change a protein sequence and experimentally test how that change alters its function. Because there are too many possible amino acid sequence changes to test them all in the laboratory, researchers build computational models that predict protein function based on amino acid sequences. Scientists have now combined multiple machine learning approaches for building a simple predictive model that often works better than established, complex methods.

Obtaining Insights – While many service leaders jump to the cost savings potential of AI, one of AI's key benefits is in its ability to obtain insights and predictions. Insight generation allows organizations to move beyond cutting costs to generating value. Organizations can use these insights to guide agent and application decisions, ensuring customers receive the best service experience possible. Three examples of how AI is used to obtain insights in customer service are personalization, customer lifetime value and AI-based customer routing. Ensuring Optimal User Experiences – Another key benefit of AI is in how it creates optimal customer and agent experiences.

MD&M West Booth #1259 -- MBX Systems, a specialized hardware designer and integrator, announced today that it is working with the NVIDIA Clara Holoscan MGX AI computing platform to streamline the development of next-generation AI-defined medical instrument solutions. Medical technology developers using the NVIDIA platform can deploy their solutions on MBX's embedded and edge hardware building blocks to eliminate costly and time-consuming custom hardware development. Clara Holoscan MGX is an AI computing platform for commercial medical devices that require high-performance, low-latency AI capabilities. The platform can help medical technology developers reduce development time as well as certification time and effort. It also provides long-term whole-stack software support and long-lifecycle support for NVIDIA hardware components, reducing the need for costly hardware updates for obsolete components while also ensuring the software is regularly updated with bug fixes and security vulnerability patches.

It detected high risk in the heart circled in red. A new artificial intelligence-based approach can predict, significantly more accurately than a doctor, if and when a patient could die of cardiac arrest. The technology, built on raw images of patient's diseased hearts and patient backgrounds, stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine's deadliest and most puzzling conditions. The work, led by Johns Hopkins University researchers, is detailed today in Nature Cardiovascular Research. "Sudden cardiac death caused by arrhythmia accounts for as many as 20 percent of all deaths worldwide and we know little about why it's happening or how to tell who's at risk," said senior author Natalia Trayanova, the Murray B. Sachs professor of Biomedical Engineering and Medicine.

As an example of the efficiency that AI can introduce, let's consider production-line workers and engineers who manage reject rates in the pulley assembly process. This used to be a multi-step, manual process,

(AIAD), an artificial intelligence (AI) and machine learning (ML) data science and technology company, announced today that it has entered into a …

The Motion Planning ML research team introduces and advances new Deep Learning techniques for our Motion Planner. Team members perform research not commonly found in existing literature, develop machine learning infrastructure, build and train models, integrate them into existing motion planer, and write production level software. We have produced groundbreaking advancements in the autonomous vehicle industry including nuScenes (https://www.nuscenes.org), Motional is a driverless technology company making self-driving vehicles a safe, reliable, and accessible reality. The Motional team is made up of engineers, researchers, innovators, dreamers and doers, who together are creating a first-of-its-kind technology with the potential to transform the way we move.

In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo. While tools exist to help experts make sense of a model's reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns. Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model's behavior.

Scientists are combining artificial intelligence and advanced computer technology with biological know how to identify insects with supernatural speed. Insects are the most diverse group of animals on Earth and only a small fraction of these have been found and formally described. In fact, there are so many species that discovering all of them in the near future is unlikely. This enormous diversity among insects also means that they have very different life histories and roles in the ecosystems. For instance, a hoverfly in Greenland lives a very different life than a mantid in the Brazilian rainforest.

A study by researchers at the Universitat Politècnica de València (UPV), part of BDSLab-ITACA group and the Institute of Pure and Applied Mathematics (IUMPA), has become an international benchmark for the reliable use of artificial intelligence in monitoring and managing COVID-19, writes the Technical University of Valencia in this press release. In the article, published in the Journal of the American Medical Informatics Association, the team from the UPV demonstrates the limitations that the variability or heterogeneity of data may have in reliably applying artificial intelligence when it comes from multiple sources, e.g. a range of hospitals or countries. Furthermore, the UPV team has developed new tools based on this study to help describe and classify patients with COVID-19. "The results of our study may, combined with these tools, assist in clinically assessing patients, and help with automated early classification by risk level both before and after hospital admission. They can even help to plan the allocation of resources, which is particularly beneficial for patients that will be admitted to the ICU," says Carlos Sáez, a member of the BDSLab-ITACA group research team at Universitat Politècnica de València, who coordinated the study.