This is a guest post by Chris Adzima, a Senior Information Systems Analyst for the Washington County Sheriff's Office. In law enforcement, it is extremely important to identify persons of interest quickly. In most cases, this is accomplished by showing a picture of the person to multiple law enforcement officers in hopes that someone knows the person. In Washington County, Oregon, there are nearly 20,000 different bookings (when a person is processed into the jail) every year. As time passes, officers' memories of individual bookings fade.
The democratization of mass surveillance is upon us. Insanely cheap tools with the power to track individuals en masse are now available for anyone to use, as exemplified by a Forbes test of an Amazon facial recognition product, Rekognition, that made headlines last month. Jeff Bezos' behemoth of a business is seen by most as a consumer-driven business, not a provider of easy-to-use spy tech. But as revealed by the American Civil Liberties Union (ACLU) last week, Amazon Web Services (AWS) is shipping Rekognition to various U.S. police departments. And because Rekognition is open to all, Forbes decided to try out the service.
A research paper and associated article published yesterday made claims about the accuracy of Amazon Rekognition. We welcome feedback, and indeed get feedback from folks all the time, but this research paper and article are misleading and draw false conclusions. This blog post shares details which we hope will help clarify several misperceptions and inaccuracies. People often think of accuracy as an absolute measure, such as a percentage score on a math exam, where each answer is either right or wrong. To understand, interpret, and compare the accuracy of machine learning systems, it's important to understand what is being predicted, the confidence of the prediction, and how the prediction is to be used, which is impossible to glean from a single absolute number or score.
The world has become a global village and interactions between people from different parts of the world are increasing day-by-day. Language was one of the major roadblocks in enabling free communication between people all over the world. The natural language processing services of Amazon like Amazon Comprehend and Amazon Translate help us to understand the dominant language any given text text, translate it and perform the sentiment analysis for the incoming textual information. Talend integrates these Amazon AI services to convert end to end applications like real-time sentimental analysis dashboard and multilingual customer care system. A quick example is the sentimental analysis dashboard as shown below. Talend is integrated with Amazon's Comprehend service to identify the customer sentiments in real time and to send the sentimental analysis details to downstream system dashboards. Another example which showcases Talend's integration capabilities with Amazon Comprehend and Amazon Translate services is the creation of a multi-lingual customer care system. The incoming messages are analyzed to understand the dominant language used in it and the text is translated from non-supported languages to supported language automatically. The two Talend KB articles I would recommend getting a detailed overview and hands-on experience about Talend's integration with above two Amazon services are as shown below.
Amazon has started to offer artificial intelligence based services on its AWS platform, to give developers more tools to engage with customers. The three new AI tools are called Lex, Polly and Rekognition. Lex is the technology that powers Amazon Alexa, and allows developers to integrate rich conversational experiences in their offerings. Polly is a state of the art text to speech service that has forty seven life like voices in twenty languages. Rekognition is an image processing service, that can identify content in images.