Coding Update: Challenges, My Latest Project, and Website Optimization. Coding Update: Challenges, My Latest Project, and Website Optimization.

Several recent surveys show that more than 80% of consumers prefer spending with a credit card over cash. Thanks to advances in AI and machine learning (ML), credit card fraud can be detected quickly, which makes credit cards one of the safest and easiest payment methods to use. The challenge with cards, however, is that in some countries when fraud is suspected the credit card is blocked immediately, which leaves the cardholder without a reason as to why, how, or when. Depending on the situation, it can take anywhere from a few hours to days until the customer is notified and even longer to resolve. With Amazon Connect, a cardholder can be notified immediately of a suspected card fraud and interactively verify if the suspected transactions were indeed fraudulent over the phone.

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 […]

Factories that produce a high volume of inventory must ensure that defective products are not shipped. This is often accomplished with human workers on the assembly line or through computer vision. You can build an application that uses a custom image classification model to detect and report back any defects in a product, then takes appropriate action. This method provides a powerful, scalable, and simple solution for quality control. It uses Amazon S3, Amazon SQS, AWS Lambda, AWS Step Functions, and Amazon SageMaker.

In this blog post we're going to walk you through an example situation where you've just built a machine learning system that labels your data at volume and you want to perform manual quality assurance (QA) on some of the labels. How can you do so without overwhelming your limited resources? Rather than asking your workers to validate items one at a time, you'll accomplish custom labeling by presenting a small batch of already-labeled items that have been assigned the same label. You'll ask the worker to mark any that aren't correct. In this way, a workforce is able to quickly assess a much larger quantity of data than they could label from scratch in the same time. Amazon SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks.

Scavenger hunts have existed for generations. I've written an app for Amazon's Alexa platform that modernizes scavenger hunts. The premise of the game doesn't change: find ten random items located in your home -- or around your neighborhood -- within an hour. Except that it is Alexa who facilitates the game play. My app is free to enable if you're one of the millions of people who own an Amazon Alexa.

You can't just assume that when an application reads each letter of a sentence that the output will make sense. Amazon Polly provides speech synthesis functionality that overcomes those challenges, allowing you to focus on building applications that use text-to-speech instead of addressing interpretation challenges. Amazon Polly turns text into lifelike speech. It lets you create applications that talk naturally, enabling you to build entirely new categories of speech-enabled products. Amazon Polly is an Amazon AI service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice.