Recent neural networks such as WaveNet and sampleRNN that learn directly from speech waveform samples have achieved very high-quality synthetic speech in terms of both naturalness and speaker similarity even in multi-speaker text-to-speech synthesis systems. Such neural networks are being used as an alternative to vocoders and hence they are often called neural vocoders. The neural vocoder uses acoustic features as local condition parameters, and these parameters need to be accurately predicted by another acoustic model. However, it is not yet clear how to train this acoustic model, which is problematic because the final quality of synthetic speech is significantly affected by the performance of the acoustic model. Significant degradation happens, especially when predicted acoustic features have mismatched characteristics compared to natural ones. In order to reduce the mismatched characteristics between natural and generated acoustic features, we propose frameworks that incorporate either a conditional generative adversarial network (GAN) or its variant, Wasserstein GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that uses the WaveNet vocoder. We also extend the GAN frameworks and use the discretized mixture logistic loss of a well-trained WaveNet in addition to mean squared error and adversarial losses as parts of objective functions. Experimental results show that acoustic models trained using the WGAN-GP framework using back-propagated discretized-mixture-of-logistics (DML) loss achieves the highest subjective evaluation scores in terms of both quality and speaker similarity.

From the world's largest tech companies to start-ups, everyone is looking for people well-versed with Artificial Intelligence (AI). But a career in this business is no cakewalk: A lot of mathematics, constant leaning and understanding human behaviour are just some of the ways to get a foothold in this fast-growing industry. We spoke to five AI professionals, who tell us that a career in this field is about many different things, from data analysis, text and image recognition to linguistics--and no, evil robots do not figure in the list. AI researcher and founding member, Qure.ai Ghosh, 26, spends his days looking at X-rays. "I am almost a semi-radiologist.

By reading and paying attention to present-day signals from our future, we can best make sense of the higher education IT world in 2038. In 1992, I was in a meeting at Apple Computer and was asked if I wanted to see the next "killer technology" the company would soon release. My Apple colleague left the conference room and came back to unveil the Apple Newton, a handheld device (sort of) that Apple was calling a "Personal Digital Assistant" (PDA) and that featured handwriting recognition. I flipped back the gray metal lid and tried the stylus, writing to my wife, "Dear Pat." My writing, converted to text on the fly, came back as "Deal Pot."

That solves a challenge for robotic hands, which look like the fist of a robot from the 1980s "Terminator" science fiction film. The hands have been commercially available for years but are difficult for engineers to program. Engineers can write specific computer code for each new task, which requires a pricey new program each time. Or robots can be equipped with software that lets them "learn" through physical training.

I am into my first term of Udacity's Self Driving Car Nanodegree and I want to share my experiences regarding one of my recent projects. The objective of this project is to basically apply the concepts of Deep Learning and Convolutional Neural Networks to teach the machine to drive car autonomously. How is this even possible? First things first, it is not magic but it really feels like magic. With just a bunch of Python libraries, some lines of Code and huge amount of Data we can teach a car to drive itself.

Today's young professionals grew up in an age of mind-boggling technological change, seeing the growth of the internet, the invention of the smartphone, and the development of machine-learning systems. These advances all point toward the total automation of our lives, including the way we work and do business. It's no wonder, then, that young people are anxious about their ability to compete in the job market. As executives who have spent our lives assessing and implementing digital technology in every type of organization, we often get asked by them: "What should I learn today so that I'll have a job in the future?" In what follows we'll share seven skills that can not only make you unable to be automated, but will make you employable no matter what the future holds.

REVENUE FORECAST: Baidu's quarterly revenue is likely to have reached $3.9 billion, up from $3.1 billion a year ago, the survey showed. AD GROWTH: Baidu has seen its search-related advertising bounce back, boosting both its revenue and profit in recent quarters. Baidu should continue to benefit as companies--especially in areas such as health and online education--allocate more of their ad budgets to search advertising, which has a higher conversion rate than newsfeeds, Shawn Yang, executive director at Blue Lotus Capital Advisors, wrote in a research report. "We see an increasing demand of search ads from both users and advertisers," he wrote. "Chinese internet users become more mature in gaining information and tend to use search engines more frequently."

You will learn how to use Python to analyze data (big data analytics), create beautiful visualizations (data visualization) and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more.

Analytics has been viewed as a messiah that has the potential to transform every aspect of our functioning, be it industry or society. As is the case with most technologies, as compared to most other domains, industry has been in the forefront of adopting analytics tools and approaches to businesses. What is noteworthy is that analytics and artificial intelligence (AI) in particular are receiving a lot of attention in the areas of research and innovation in academic institutions around the world. However, most of this work is yet to be put to use for the education processes within the academic system. Analytics and AI have the potential to deliver superior learning experience and targetted problem solving capabilities which need to be explored by the academics practitioners.

When Facebook and Twitter were born, a new era of social media was ushered in, opening the gates for new areas of expertise that hadn't existed before. At first, we all grappled to establish the culture together, but fast forward a decade and it is literally a science with thousands of supporting technology companies. So as Artificial Intelligence (AI) takes over marketing, doesn't that mean it will replace marketers? If you can ask your smart speaker in your office what your engagement growth increase was for your Facebook Page, and ask for recommendations of growth, how do marketing professionals survive? Marketers will survive the same way they did as social media was introduced – the practice will evolve and new niches will be born. There are 7 skills marketers will need to adapt in order to evolve.