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.

These days, there is no part of our lives that is unaffected via computerization. A few illustrations incorporate clothes washers, microwaves, autopilot mode for autos and planes, Nestlé utilizing Robots to offer espresso units in stores in Japan, Walmart testing automatons to convey items in the US, our bank checks being arranged to utilize Optical Character Recognition (OCR), and ATMs. Automation, in basic words, is innovation that arrangements with the utilization of machines and PCs to the generation of merchandise and enterprises. This aids in completing works with practically no human help. With the appearance of PCs, numerous product frameworks were created to achieve assignments that were beforehand done on paper to oversee organizations, or not being done at all because of the absence of devices.

Arcticsid asked about turning text into a .jpg. I'll also explain converting an image back into text. Your browser will select the word, and then you'll be able to copy and paste it into your word processor or email program. But try double-clicking a word in the picture above (or in any of the other pictures in this article). In the digital world, there's a big difference between real text and an image that looks like text--even if it's not always obvious to the user.

Money is one of many challenges for people who are visually impaired. But Pedro Liz, who is blind from Retinitis pigmentosa, is able to accurately decipher different bills using the ORCAM MyEye--a device attached to glasses that works with artificial intelligence. Its features include recognizing different kinds of products which are then spoken into an earpiece. "Oreos cookies, it will tell me it's Oreos cookies this is how you recognize the product," said Pedro. Dr. Georgia Crozier with the Moore Eye Institute says MyEye is unlike other devices that work with magnification.

E-readers have become one of the most pervasive pieces of tech for many reasons. They survive alongside tablets because they're accessible -- Amazon's entry-level Kindle is just $80 -- and don't require daily charging. E-ink displays don't strain your eyes nearly as much as backlit screens, nor do they keep you up at night. Above all else, though, they can hold the entire works of Shakespeare countless times over while being thinner and lighter than any paperback. But this idea of portability, of condensing the written word into a format only a device can understand, is older than The Great Gatsby.

Crowdworker online communities — operating in fora like mTurkForum and TurkerNation — are an important actor in microwork markets. Albeit central to market dynamics, how the behavior of crowdworker communities and the dynamics of online marketplaces influence each other is yet to be understood. To provide quantitative evidence of such influence, we performed an analysis on 6-years worth of mTurk market activities and community discussions in six fora. We investigated the nature of the relationships that exist between activities in fora, tasks published in mTurk, requesters for such tasks, and task completion speed. We validate -- and expand upon — results from previous work by showing that (i) there are differences between market demand and community activities that are specific to fora and task types; (ii) the temporal progression of HIT availability in the market is predictive of the upcoming amount of crowdworker discussions, with significant differences across fora and discussion categories; (iii) activities in fora can have a significant positive impact on the completion speed of tasks available in the market.