While Coronavirus has us all confined at home, Artificial Intelligence and its technologies are continuing to develop around us. This week’s AI guide discusses augmented AI, AI embedded computing and more.
Amazon Web Services (AWS) has recently announced the general availability of Amazon Augmented Artificial Intelligence. Also known as the A2I, Augmented Artificial Intelligence is a fully managed service that assists in adding human reviews to machine learning predictions, in order to improve a model and its application accuracy by identifying and improving low confidence predictions. It uses reviewers from Mechanical Turk, third-party vendors, or even their own employees to review the system, structure the process, and manage the human workforce. For instance, a developer can use Amazon A2I to spin up and manage a workforce of humans to review and validate the accuracy of ML predictions for an application that uses image recognition for identification of counterfeit items online. This is to ensure that the quality of results improves over time. Moreover, there are no commitments to use Amazon A2I, users are expected to pay only for each review needed.
With Amazon A2I, a developer can also add a human review to ML applications without having to build or manage expensive systems for it. This is because Amazon A2I provides over 60 pre-built human review workflows for common ML tasks, like object detection in images, transcription of speech, or content moderation — that allow ML predictions from Amazon Rekognition and Amazon Textract.
U.S. Air Force researchers are looking at making bigger and better improvements in embedded computing through AIML capabilities. The officials of the Air Force Research Laboratory’s Information Directorate in Rome, N.Y., announced the Robust and Efficient Computing Architectures, Algorithms, and Applications For Embedded Deep Learning — a potential $99 million five-year project that seeks to achieve improvements in size, weight and power (SWaP) by deploying Artificial Intelligence and Machine Learning capabilities in an embedded computing environment. The objectives of interest are to advance efficient computing architectures, algorithms, and applications.
The Air Force researchers aim to demonstrate their modular computing approaches for future real-time embedded plug-and-play capabilities. The technologies essentially include artificial intelligence and machine learning models for big data analytics for sensor processing; data fusion algorithms for situational awareness and sense-making; and autonomous decision making. Modular designs should support interchangeable sensors, with automatic software reconfiguration based on available resources. As per the researchers, these kinds of designs will lead to unconventional circuits based on emerging nanotechnology like memristors and nano-photonics.
If you are at home, have time to spare and are intrigued by Artificial Intelligence, now is the time to explore online learning resources. Explore career and upskill with Great Learning’s Artificial Intelligence and Machine Learning program.0