If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
FREMONT, CA: Researchers are developing solutions to integrate artificial intelligence (AI) in the clinical setting. AI is relevant in medical applications like disease prediction, diagnosis, and prognosis. Data from patients in medical images, texts, and electronic records provide enough information for machine learning (ML) to undertake automation certain functions with accuracy and reliability. Cardiology: Cardiovascular diseases is a major cause of morbidity and mortality, globally. It is an expensive treatment that requires expensive treatments. AI in cardiology improves prediction and diagnosis of cardiac events.
According to a 2020 survey of data scientists conducted by Anaconda, data preparation is one of the critical steps in machine learning (ML) and data analytics workflows, and often very time consuming for data scientists. Data scientists spend about 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and visualizing data (21%). Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. If you prefer a GUI-based and interactive interface, you can use Amazon SageMaker Data Wrangler, with over 300 built in visualizations, analyses, and transformations to efficiently process data backed by Spark without writing a single line of code.
Among the most fundamental trends in the consumer electronics industry today is the proliferation of sensors. For applications such the Internet of Things (IoT), wearables, and AR/VR, sensing has been a core technology. As devices become embedded with more and more sensors, system developers face about how to best handle and fuse that data into a useful format. To address this issue, STMicroelectronics (ST) has recently released a new 6-axis IMU which includes a number of integrated features such as sensor fusion blocks and Machine Learning (ML) cores. In this article, we'll discuss sensor fusion, why it's a challenge, and how ST's new product hopes to enable low-power sensing applications.