An AI startup called Lyrebird just invented an algorithm that can mimic the voice of any person, based on just 60 seconds of speech. Do you remember the cool Mission Impossible tech that lets Tom Cruise's character Ethan Hunt mimic the voice of other characters using some nifty speech synthesis technology? Well, a Montreal-based startup called Lyrebird (named after the sound-imitating bird) just invented it for real. "We are developing new speech synthesis technologies which, among other features, allow us to copy the voice of someone with very little data," Alexandre de Brebisson, one of the PhD students who developed the deep-learning tech behind the project. "Our experiments show that one minute of audio already contains a lot of the DNA of a human voice. We are able to learn a new voice with as little data because our model is able to capture similarities between the new voice and all the voices it already knows. Our models understand the underlying variables that make [one] voice different from another."

Fan Hui, the European Go champion, needed some fresh air. "I don't understand myself anymore." Hui was the first professional Go player to face AlphaGo, Google's artificial intelligence system and the title of a new documentary by Greg Kohs that debuted last week at the Tribeca Film Festival in New York. When Hui was invited to visit Google's London office housing the DeepMind research group that developed AlphaGo, he was feeling confident. After all, as Hui puts it, "it is just a program."

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We are living in an era of hype. In this article, I am trying to discover the hype around Artificial Intelligence. The First thing I want to clear is that ML/DL are algorithms, neither conscious nor intelligent or smart machines. I agree that Deep Learning has penetrated industries and it holds the potential to disrupt industries, but it is nowhere near to being conscious or an intelligent machines. Singularity, AI taking over the world, End of the world were one of the most used phrases in the media last year.

Persistent Systems is a technology services company headquartered in Pune. Every year it hosts a global hackathon called'Semicolons' -- which lasts for 24 hours and comprises self-managed teams who ideate and compete against each other to come up with innovative solutions to everyday problems. This year's theme was Digital Transformation, which saw participation by 45 teams across 11 global centres in 5 countries involving 600 Persistent employees. The winning solution this year was Drishti -- an application which uses advances in deep learning, artificial intelligence, image recognition, person identification, speech-to-text and accessibility technologies to bring value to those who are visually impaired. We spoke to team leader Pandurang Kamat, chief architect -- innovation and R&D at Persistent Systems, who also happens to head the Blockchain division at Persistent Systems.

Almost everyone loves to spend their leisure time to watch movies with their family and friends. We all have the same experience when we sit on our couch to choose a movie that we are going to watch and spend the next two hours but can't even find one after 20 minutes. We definitely need a computer agent to provide movie recommendation to us when we need to choose a movie and save our time. Apparently, a movie recommendation agent has already become an essential part of our life.. According to Data Science Central "Although hard data is difficult to come by, many informed sources estimate that, for the major ecommerce platforms like Amazon and Netflix, that recommenders may be responsible for as much as 10% to 25% of incremental revenue."

Fan Hui, the European Go champion, needed some fresh air. "I don't understand myself anymore." Hui was the first professional Go player to face AlphaGo, Google's artificial intelligence system and the title of a new documentary by Greg Kohs that debuted last week at the Tribeca Film Festival in New York. When Hui was invited to visit Google's London office housing the DeepMind research group that developed AlphaGo, he was feeling confident. After all, as Hui puts it, "it is just a program."

While understanding and trusting models and their results is a hallmark of good (data) science, model interpretability is a serious legal mandate in the regulated verticals of banking, insurance, and other industries. Moreover, scientists, physicians, researchers, analysts, and humans in general have the right to understand and trust models and modeling results that affect their work and their lives. Today many organizations and individuals are embracing deep learning and machine learning algorithms but what happens when people want to explain these impactful, complex technologies to one-another or when these technologies inevitably make mistakes? This talk presents several approaches beyond the error measures and assessment plots typically used to interpret deep learning and machine learning models and results. The talk will include: - Data visualization techniques for representing high-degree interactions and nuanced data structures.

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models. Originally posted here in PDF format. Click on the image below to zoom in. For other cheat sheets covering all data science topics, click here.

In the 1990s, there was a popular book called Re-engineering the Corporation. Looking back now, Re-engineering certainly has had a mixed success – but it did have an impact over the last two decades. ERP deployments led by SAP and others were a direct result of the Business Process re-engineering phenomenon. So, now, with the rise of AI: Could we think of a new form of Re-engineering the Corporation – using Artificial Intelligence? The current group of Robotic process automation companies focus on the UI layer.