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Perform medical transcription analysis in real-time with AWS AI services and Twilio Media Streams

#artificialintelligence

Medical providers often need to analyze and dictate patient phone conversations, doctors’ notes, clinical trial reports, and patient health records. By automating transcription, providers can quickly and accurately provide patients with medical conditions, medication, dosage, strength, and frequency. Generic artificial intelligence-based transcription models can be used to transcribe voice to text. However, medical voice data […]


How Deep Fakes Can Hurt Your Business And What To Do About It

#artificialintelligence

Fake video and audio streams that appear to be real can ruin the reputations of your executives and your company. They can cost you money. They can even cost you your job. Fortunately, there are steps you can take that can help. If you're at all familiar with the term "deep fake" you probably think about it in terms of fake videos about celebrities or politicians where they're already being used as parts of disinformation campaigns.


Multi-Modal Learning over User-Contributed Content from Cross-Domain Social Media

Lee, Wen-Yu (National Taiwan University)

AAAI Conferences

The goal of the research is to discover and summarize data from the emerging social media into information of interests. Specifically, leveraging user-contributed data from cross-domain social media, the idea is to perform multi-modal learning for a given photo, aiming to present people’s description or comments, geographical information, and events of interest, closely related to the photo. These information then can be used for various purposes, such as being a real-time guide for the tourists to improve the quality of tourism. As a result, this research investigates modern challenges of image annotation, image retrieval, and cross-media mining, followed by presenting promising ways to conquer the challenges.