Now is an exciting time to be an engineer or technology enthusiast. We're starting to see ideas that were talked about as only concepts in science fiction making their way to the mainstream. Many of these new flashy technologies, like the self-driving car, will be enabled in some way, shape, or form by 5G. The epitome of science-fiction technology, though, is artificial intelligence (AI). It's the robot that will interact with us, or in the case of some horror films, be humanity's ultimate downfall.

Where's the best place to look for free online datasets for NLP? We combed the web to create the ultimate cheat sheet, broken down into datasets for text, audio speech, and sentiment analysis. Sentiment140: a popular dataset, which uses 160,000 tweets with emoticons pre-removed. Twitter US Airline Sentiment: Twitter data on US airlines from February 2015, classified as positive, negative, and neutral tweets. Yelp Reviews: An open dataset released by Yelp, contains more than 5 million reviews.

After Amazon founder and CEO Jeff Bezos went on the offensive against the National Enquirer and its parent company AMI, saying they threatened to release intimate photos of himself and his girlfriend, Lauren Sanchez, many in the media lauded the billionaire for his stance and his defense of privacy. Some critics of Amazon, however, found the situation ironic. "I totally disapprove of what the National Enquirer did to Jeff Bezos, I do," Tucker Carlson said regarding the alleged extortion and blackmail attempt. "But, it does raise the question: What is Amazon doing to the rest of us?" "I wish we were a society that left consensual adult sex to the people engaged in them... That's all the reasons why we should value privacy," journalist Glenn Greenwald said on "Tucker Carlson Tonight" before adding that "one of the companies that poses the greatest threat to [privacy] is Amazon." Greenwald, co-founding editor of The Intercept, said Amazon's online marketplace is just a small part of the huge company's operation -- and, in fact, its primary business has contributed heavily to making America a "surveillance state."

January is a special month in AI history. Because in both the novel and movie 2001: A Space Odyssey, January 12 is when the HAL 9000 sentient computer -- (spoiler alert!) the story's antagonistic artificial intelligence -- goes live. Depending on whether you date HAL to its "birth" in the film, the novel, or when those media originated, HAL is anywhere between 22 years to 51 years old now (For trivia buffs, of which I'm one: The book and film were released in 1968, making HAL's conception over 50 years ago; if you go by the dates given in the film or the book, respectively, HAL is either 27 or 22 years old). HAL is then placed aboard the Discovery One spacecraft to participate in a journey of, well, discovery to the planet Jupiter. Of course, HAL decides pretty quickly that the people on the mission are a significant roadblock to completing the mission, decides the ship and voyage would be better served without all those pesky humans running around… and tries to kill everyone onboard.

Artificial intelligence (AI) has a huge impact on our personal lives and also on our democratic society as a whole. While AI offers vast opportunities for the benefit of people, its potential to embed and perpetuate bias and discrimination remains one of the most pressing challenges deriving from its increasing use. This new study, entitled "Discrimination, Artificial Intelligence and Algorithmic Decision-Making", which was prepared by Prof. Frederik Zuiderveen Borgesius for the Anti-discrimination Department of the Council of Europe, elaborates on the risks of discrimination caused by algorithmic decision-making and other types of artificial intelligence (AI).

As technology advances, we stand on the brink of an AI (artificial intelligence) revolution, which could see swathes of human jobs usurped.

It's not exactly breaking news that biopharma companies are increasingly turning toward machine learning and artificial intelligence (AI) to improve …

"Yes ma'am, Roll-Oh get," the robot says, as it goes to the kitchen to prepare dinner, opening a can of food and blowing fire to a candle. Roll-Oh walks clumsily, suspiciously like a man in an uncomfortable costume. Nonetheless, it ably frees the domestic housewife of all her daily chores, at the simple press of a button. This was the promise of robotics, demonstrated in the 1940 short film "Leave it to Roll-Oh", presented at the New York World's Fair. Mechanical robotics already automate so much of our lives, the film argues, that it will be only a matter of time before we can expect personal, four-limbed metal people as ready-made servants: watering our plants, greeting our mailman, helping cook dinner.

Time-frequency (TF) representations provide powerful and intuitive features for the analysis of time series such as audio. But still, generative modeling of audio in the TF domain is a subtle matter. Consequently, neural audio synthesis widely relies on directly modeling the waveform and previous attempts at unconditionally synthesizing audio from neurally generated TF features still struggle to produce audio at satisfying quality. In this contribution, focusing on the short-time Fourier transform, we discuss the challenges that arise in audio synthesis based on generated TF features and how to overcome them. We demonstrate the potential of deliberate generative TF modeling by training a generative adversarial network (GAN) on short-time Fourier features. We show that our TF-based network was able to outperform the state-of-the-art GAN generating waveform, despite the similar architecture in the two networks.

The pace of content creation has never been faster. Each day, the world generates 2.5 quintillion bytes of data, and more than 90 percent of all data in existence has been produced since 2016. In the process, it is being hidden from search engines, locked away in multimedia formats that cannot be catalogued. A gold mine of information is waiting to be tapped if only the spoken word could be easily converted to text. Doing so by hand is time consuming and, often, prohibitively expensive.