"Connected to other part," my iPhone says to me as I stand somewhere in London's Soho, trying to decipher the letter on the top of a bus stop. "Hello?" says an American woman, reminding me of Scarlett Johansson's disembodied artificially intelligent character from the sci-fi film Her. "Hey, er … can you give me a hand by reading the letter on the bus stop?" "Sure … can you move your phone a bit more up, and to the left … Ya! I thank her, end the session, pull up Citymapper and navigate my way onto the 453 going to New Cross. I have a little bit of vision, but only enough to see motion and movement. I am using an app called Be My Eyes, an app that connects blind and visually impaired people to sighted volunteers via a remote video connection. Through the phone's camera, the blind person is able to show the sighted individual what they are looking at in the real world, allowing the volunteer to assist them with any of their vision-related problems. I began to lose my sight in the summer of 2013 to a rare genetic mitochondrial disease called Leber's hereditary optic neuropathy and was soon registered blind. I consequently found myself relying on an assortment of assistive technologies to do the simplest of tasks. Be My Eyes has just over 35,000 visually-impaired users registered for the app and over half a million volunteers. Whenever a visually impaired user requests assistance a sighted volunteer receives a notification and a video connection is established. Jose Ranola, a 55-year-old from the Philippines who works in construction and has retinitis pigmentosa, said: "I use it to help me identify medicine and read printed materials and also to describe places and objects." He adds: "All my experiences were good.

To see what kind of talent we are currently looking for and submit your resume, please visit: https://a9.com/careers/ We are always looking for talented people with backgrounds in: · Computer Vision · Machine Learning · Natural Language Processing · Backend Infrastructure / Systems Software Development · Analytics Data Mining · Pattern Recognition · Artificial Intelligence · Optical Character Recognition · Server Infrastructure · Augmented Reality · DevOps / Operations Engineer · Software Developer in Test A9 solves some of the biggest challenges in search and advertising. We focus on helping people find the things they want. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.

Global banks that have a large mortgage business are facing pressure internally and externally to upgrade their operating model to save money, decrease processing times and enhance the customer experience – today it can take more than 60 days to complete a mortgage transaction. The pressure is particularly strong with FinTechs like US online lender Rocket Mortgage and UK digital mortgage broker Trussle creating a completely digital experience for prospective home buyers. Banks, therefore, are exploring everything from mature technologies like Optical Character Recognition (OCR) to more leading edge and high-tech solutions based on blockchain and artificial intelligence. While some of these solutions could dramatically impact day-to-day business for lenders and their brokers and customers, blockchain has the potential to completely transform the entire mortgage financing industry. The financial services industry is all about trust – whether relationship based, reputational, authoritative (legal) or transactional – banking today is built on trust.

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Chinese tech giant Baidu's text-to-speech system, Deep Voice, is making a lot of progress toward sounding more human. The latest news about the tech are audio samples showcasing its ability to accurately portray differences in regional accents. The company says that the new version, aptly named Deep Voice 2, has been able to "learn from hundreds of unique voices from less than a half an hour of data per speaker, while achieving high audio quality." That's compared to the 20 hours hours of training it took to get similar results from the previous iteration, for a single voice, further pushing its efficiency past Google's WaveNet in a few months time. Baidu says that unlike previous text-to-speech systems, Deep Voice 2 finds shared qualities between the training voices entirely on its own, and without any previous guidance.

TL;DR Baidu's TTS system now supports multi-speaker conditioning, and can learn new speakers with very little data (a la LyreBird). I'm really excited about the recent influx of neural-net TTS systems, but all of the them seem to be too slow for real time dialog, or not publicly available, or both. Hoping that one of them gets a high quality open-source implementation soon!

Next time you hear a voice generated by Baidu's Deep Voice 2, you might not be able to tell whether it's human. Baidu, the Beijing-based juggernaut that commands 80 percent of the Chinese internet search market, is investing heavily in artificial intelligence. In 2013, it opened the Institute of Deep Learning, an R&D center focused on machine learning. And in May, it took the wraps off the newest version of Deep Voice, its AI-powered text-to-speech engine. Deep Voice 2, which follows on the heels of Deep Voice's public debut earlier this year, can produce real-time speech that's nearly indistinguishable from a human voice.

A version of this post originally appeared on Tedium, a twice-weekly newsletter that hunts for the end of the long tail. We live in a world where facial recognition has become so sophisticated that we're being forced to ask very serious ethical questions about it. In China, it's being used to detect toilet paper theft. But I want to take a step back from the big hairy ethical questions and consider how we started on this road--with typography. Optical character recognition, or OCR, is a technology that came up with computing in general.

The bar for what counts as artificial intelligence is continually rising. Technologies should lose their AI status when they become routine (optical character recognition is no longer recognized as an example of AI for this reason). However, for marketers it's tempting to go in the opposite direction and rebrand all kinds of routine technologies as AI in order to make them sound more exciting and newsworthy. AI was a key theme at this year's Mobile World Congress. One AI-themed announcement was "aia", from telecoms IT giant Amdocs, which claims to enable the "self-driving telco".

In this survey, we conduct an investigation on the robustness of several features and classifiers in automatic authorship attribution. Our corpus consists in 25 different documents written by 5 different American philosophers in English. The different documents pass throw a digital conversion into grey-scaled images and several levels of noise are added to corrupt those image documents. The noise consists in a “Salt & Pepper” type, which is randomly added on the surface of the images with the following noise levels: 0%, 1%, 2%, 3%, 4%, 5%, 6% and 7%. Thus, each image goes throw an OCR program (Optical Character Recognition) to extract the text from the image. Then, the obtained text document is kept to be used during the experiments of authorship attribution. Several features and classifiers are employed and evaluated with regards to the classification performances. Results are quite interesting and show that the most robust feature in au-thorship attribution is the character-tetragram, which provides a score of 100% even at a noise level of 7%.