When Manoj Saxena surveys what's next in the world of computing, he sees a huge change coming that will transform the way we live our lives. The former general manager of IBM's Watson project, which scours millions of pages of data to answer questions posed in natural language, says machine learning and artificial intelligence represent the fourth big inflection point in history -- after the alphabet, the printing press and the Internet. "This shift is going to make the Internet look small in terms of impact," he says. The 2 billion already invested in AI is just one-tenth of what's to come. "In about five years, saying, 'My software does AI,' will be like saying, 'My software does the Internet,'" he said, in a recent interview with Texas CEO Magazine.

Researchers at Google's DeepMind artificial intelligence division claim to have come up with a way of producing much more natural-sounding synthesized speech, compared with the techniques that are currently in use. Existing text-to-speech (TTS) systems tend to use a system called concatenative TTS, where the audio is generated by recombining fragments of recorded speech. There's also a technique called parametric TTS that generates speech by passing information through a vocoder, but that sounds even less natural. So DeepMind has come up with a new technique called WaveNet that learns from the audio it's fed, and produces raw audio sample-by-sample. To give an idea of how detailed that is, we're talking at least 16,000 samples per second.

Google's DeepMind unit, which is working to develop super-intelligent computers, has created a system for machine-generated speech that it says outperforms existing technology by 50 percent. U.K.-based DeepMind, which Google acquired for about 400 million pounds ( 533 million) in 2014, developed an artificial intelligence called WaveNet that can mimic human speech by learning how to form the individual sound waves a human voice creates, it said in a blog post Friday. In blind tests for U.S. English and Mandarin Chinese, human listeners found WaveNet-generated speech sounded more natural than that created with any of Google's existing text-to-speech programs, which are based on different technologies. WaveNet still underperformed recordings of actual human speech. Many computer-generated speech programs work by using a large data set of short recordings of a single human speaker and then combining these speech fragments to form new words.

Inspired by this article about sentiment analysis and this guide to webscraping, I have decided to get my hands dirty by scraping and analyzing a sample of reviews on the website Goodreads. The goal of this project is to demonstrate a complete example, going from data collection to machine learning analysis, and to illustrate a few of the dead ends and mistakes I encountered on my journey. We'll be looking at the reviews for five popular romance books. I have voluntarily chosen books in the same genre in order to make comments text more homogeneous a priori; these five books are popular enough that I can easily pull a few thousands reviews for each, yielding a significant corpus with minimum effort. If you don't like romance books, feel free to replicate the analysis with your genre of choice!

Would you like to work for a robot? Although the automation of skilled jobs is a reality, your boss is probably (still) a human. Even though the most optimistic artificial intelligence (AI) enthusiast would struggle to persuade us that the technology to create non-human leaders is upon us just yet, robots could be managing people in the decades to come. So would a robot leader be much worse than your current boss? Most people think of leadership as an occupation or a person who is formally in charge of others, but leadership is really the mechanism that enables a group to perform better.

AI-enabled messaging programs that respond to text-based requests -- are the latest innovation that startups and corporations are using to serve existing customers and bring in new ones. Companies across a wide variety of industries are building these tools on popular messaging apps like Facebook Messenger, Slack, Kik, and Hipchat, as well as on their own websites and apps. Some are even available by text, to help users do things like fight parking tickets, respond to customer service inquiries, and order tacos. This is by no means an exhaustive list, so if you see we're missing a chatbot that's currently up and running, please share the link with us in the comments section. We'll add new, significant chatbots to the list over time.

I think the recent article in the New York Times about the boom in'artificial intelligence' in Silicon Valley made many people think hard about the future of cybersecurity – both the near and distant future. Sometimes when I hang around with A.I. enthusiasts here in the valley, I feel like an atheist at a convention of evangelicals. What's going on now in the field of'AI' resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): Now, of course, without bold steps and risky investments a fantastical future will never become a reality.

Anyone that might be concerned about computers taking over look away now, because they are a step closer to sounding just like humans. Researchers in the UK at Google's DeepMind unit have been working on making computer-generated speech sound as "natural" as humans. The technology, called WaveNet, which is focused on the area of speech synthesis, or text-to-speech, was found to sound more natural than any of Google's products. However, this was only achieved after the WaveNet artificial neural network was trained to produce English and Chinese speech which required copious amounts of computing power, so the technology probably won't be hitting the mainstream any time soon. Using a convolutional neural network, which is used for artificial intelligence in deep learning, it is trained on data and then the systems make inferences about new data, in addition to being used to generate new data.

Over the past century, we have made massive strides in the rights revolution. These include rights for women, children, the LGBT community, animals, and so much more. Exploring the future, we must ask ourselves: what next? Will we ever fight for the rights of artificial intelligence? If so, when will this AI rights revolution occur, and what will it look like?

In this special guest feature, Chris Monberg, President and CTO of Boomtrain, discusses some ways to demystify AI to the C-suite in order to get over the feeling that AI is overwhelming and inaccessible and see how it can actually be quite valuable when used in the right context. In his current role as President and CTO, Chris is hyper-focused on building the team that is building the most advanced predictive personalization technology in the market. Previously, Chris was the VP of Interactive at Hornall Anderson, a global design firm. In addition to leading a team of designers, engineers, and strategists, Chris launched a R&D lab focused on redefining the relationships between brands, consumers, and technology. As artificial intelligence (AI) gains popularity, many C-suite executives have looked to make it a priority to integrate the technology to help their business. However, with vendors popping up left and right, hawking their products and touting their solutions, it's become quite difficult for these executives to know how AI can help achieve their goals.