Samuel Greengard is an author and journalist based in West Linn, OR, USA.

During the last months of my mother's life, as she ventured further from lucidity, she was visited by music. In collusion with her dementia, her hearing loss filled her consciousness with musical hallucinations. Sometimes welcome, more often not, her musical visitations were vivid, yet segmented and tattered. She would occasionally comment on the singers. On rare occasions she would identify the performer. Mitch Miller, who wrote oppressively cheerful arrangements of popular songs from the 1950s, seemed to command a prominent role in her hallucinations.

The year is 1989 and we're introduced to the World Wide Web. The Berlin Wall is coming down. The Exxon Valdez is spilling oil in Prince William Sound, Alaska. Students are calling for democracy and free speech in Tiananmen Square. Crockett and Tubbs are clearing the mean streets of Miami.

Detecting fake news is getting more difficult as more false information pours onto the internet ... [ ] every day, and from very influential sources. New papers from MIT explore how current methods are failing, and bring new weapons to the fight against fake news. Now is the time to make facts great again. "Fake news," the 2017 Collins word of the year, poses a serious threat to the values of honesty, truth, and accountability--values that purveyors of falsified information don't seem to hold too closely. Apart from the most obvious dangers of spreading false information (erosion of trust, political or national hostility, widespread uncertainty) the prevalence of AI systems on social media mean that unverified claims and slanderous falsehoods are picked up and distributed at eye-watering speeds.

What does a milkman and a copywriter have in common? I'm not a milkman nor have I lived in the 50s. I have been a copywriter for almost a year. And I can share a bit of how a copywriter's life goes in the modern century. Nowadays, most of our hours involves facing a computer screen, whether at home or at the office. We don't carry pens and paper as much as we used to, unless we need something more physical to mimic our vintage creative processes.

From September 23 to 28, 2019, the Markkula Center's Director of Technology Ethics, Brian Green, attended two Vatican-sponsored meetings on AI. AI and Faith, an organization seeking to promote conversations about AI with religious organizations, interviewed Dr. Green on his experience. This interview is cross-posted on the Markkula Center website from AI and Faith with permission. First, Brian, please summarize your background for us, especially as it relates to AI. What, particularly, has drawn you to the topic of ethical AI? I have been working on technology ethics issues, including AI, for about 15 years, starting with my graduate studies at the Graduate Theological Union in Berkeley, where I had a strong focus on biotechnology and ethics.

Businesses know that rich analytics capabilities are critical to remain competitive in today's reality. While Canadian executives may feel challenged by the immense amount of data they already have access to, they plan to continue investing in new and expanded data sources to enable them to better mitigate risk – and meet constantly shifting consumer expectations,

In the last few years artificial intelligence (AI) and machine learning (ML) have both started to feature more prominently in technology. That is especially the case in video editing, where artificial intelligence is being integrated in more and more ways. Not long from now, AI may completely transform video editing – and in some ways it already is starting to do just that. One of the earliest examples of AI's use in video editing was as far back as 2016 when IBM used their Watson supercomputer to curate footage and create a trailer for the horror film Morgan. Essentially Watson used machine learning to analyze other trailers, and then applied what it learned to curate and select scenes from the film that it felt would be good for the trailer.

The summer of 2014 was a heady time in Silicon Valley. Cash was flowing as freely as Soylent as every Stanford graduate with a half-baked idea about a "pinch point" and a semi-plausible pitch book was lining up checks from venture capital firms. Into this mix came Washboard, a startup so utterly absurd that most of the news outlets that wrote about it (and boy did they write about it) took the trouble to clarify that it was, in fact, "real". Washboard was designed to solve a real, if insubstantial, problem: it can be difficult for those who rely on coin-operated laundry machines to acquire enough quarters to run a load. Banks have limited hours, small businesses are not always obliging, and most apartment buildings don't have change machines.

Disclaimer: This is a third-party implementation. The original authors stated that they will be releasing code soon. A recent research showed that fully-convolutional GAN called MelGAN can invert mel-spectrogram into raw audio in non-autoregressive manner. They showed that their MelGAN is lighter & faster than WaveGlow, and even can generalize to unseen speakers when trained on 3 male 3 female speakers' speech. I thought this is a major breakthrough in TTS reserach, since both researchers and engineers can benefit from this fast & lightweight neural vocoder.