The Advocate reported Saturday that the resolution adopted last month is just another link a long chain of USPS issues the city wants fixed. Plaquemine Mayor Ed Reeves says residents sometimes go days without mail. Residents say packages are sometimes thrown from delivery vans or delivered to the wrong address and expected mail sometimes never appears.

On Wednesday, a polar vortex brought bitter cold to the Midwest. Overnight, Chicago reached a low of 21 degrees Fahrenheit below zero, making it slightly colder than Antarctica, Alaska, and the North Pole. Wind chills were 64 degrees below zero in Park Rapids, Minnesota, and 45 degrees below zero in Buffalo, North Dakota, according to the National Weather Service. Schools, restaurants, and businesses closed, and more than 1,000 flights have been canceled. Even the United States Postal Service stalled mail delivery, temporarily.

Amazon Comprehend Medical is a new HIPAA-eligible service that uses machine learning (ML) to extract medical information with high accuracy. This reduces the cost, time, and effort of processing large amounts of unstructured medical text. You can extract entities and relationships like medication, diagnosis, and dosage, and you can also extract protected health information (PHI). Using Amazon Comprehend Medical allows end users to get value from raw clinical notes that is otherwise largely unused for analytical purposes because it's difficult to parse. There is immense value associated with extracting information from these notes and integrating it with other medical systems like an Electronic Health Record (EHR) and a Clinical Trial Management System (CTMS).

UCLA Samueli School of Engineering and Stanford research has created a computer program with the rather forgettable name of "computer vision" which can independently identify partially seen objects. This goes beyond the usual program/task scenario which limits normal computer behaviours. This is a super-hot area of AI research, and it's on of the most important of all AI functions, in terms of making AI operational in the physical world. The sheer scale of research in this field is indicative; just about every major research organization is putting a lot of work in to this field, and this new achievement is a big, very important breakthrough. Google, in fact, just released information about its custom optical character recognition (OCR) engine AI lens which can identify a billion products, using different tech.

Text-to-speech technology has advanced greatly over the past two decades. Once defined by the robotic sounding voices that they produced, text-to-speech voices today can sound just as lifelike as an actual human. Today, making a natural sounding text-to-speech voice is labor intensive and expensive. The two most popular methods, HMM and USS, require hours of recordings from a voice actor. Then, computer programmers with an understanding of linguistics must break down all of that audio into the tiniest possible pieces, called phonemes, and appropriately tag them and define the rules for when each individual unit of speech should be used.

Microsoft has reached a milestone in text-to-speech synthesis with a production system that uses deep neural networks to make the voices of computers nearly indistinguishable from recordings of people. With the human-like natural prosody and clear articulation of words, Neural TTS has significantly reduced listening fatigue when you interact with AI systems. Our team demonstrated our neural-network powered text-to-speech capability at the Microsoft Ignite conference in Orlando, Florida, this week. The capability is currently available in preview through Azure Cognitive Services Speech Services. Neural text-to-speech can be used to make interactions with chatbots and virtual assistants more natural and engaging, convert digital texts such as e-books into audiobooks and enhance in-car navigation systems.

People online tend to communicate not just with words, but also with images. For a platform like Facebook with over 2 billion monthly active users, that means a plethora of images gets posted every day, including memes. In order to include images with text in relevant photo search results, to give screen readers a way to read what's written on them and to make sure they don't contain hate speech and other words that violate the website's content policy, Facebook has created and deployed a large-scale machine learning system called "Rosetta." Facebook needed an optical character recognition system that can regularly process huge volumes of content, so it had to conjure up its own technology. In a new blog post, the company explained how Rosetta works: it starts by detecting rectangular regions in images that potentially contain text.

Google Cloud Text-to-Speech enables developers to synthesize natural-sounding speech with 30 voices, available in multiple languages and variants. It applies DeepMind's groundbreaking research in WaveNet and Google's powerful neural networks to deliver high fidelity audio. With this easy-to-use API, you can create lifelike interactions with your users, across many applications and devices.

Early experiments in computer vision took place in the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles and squares. In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. This advancement was used to interpret written text for the blind. As the internet matured the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. These growing data sets helped make it possible for machines to identify specific people in photos and videos.

Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from the examples to application, it's much less common to have a fully annotated set of data at your fingertips. This tutorial will show you how you can use Amazon Mechanical Turk (MTurk) from within your Amazon SageMaker notebook to get annotations for your data set and use them for training. TensorFlow provides an example of using an Estimator to classify irises using a neural network classifier.