Artificial Intelligence is among us, and useful I might add. AI is the new electricity and will transform every industry. Domain specific AI may be the rise of new market verticals, and find solutions where we were unable or unwilling to. Focusing on multiple disciplines is key. Today's challenges intersect with multiple industries requiring broad skill sets able to assess, evaluate, and understand the solutions of tomorrow today.

As companies rely more on machine learning and artificial intelligence (AI) to find the right job candidates, is recruitment in danger of losing that personal touch? Peter Lane, a 21-year-old who graduated last summer from Cardiff University with a degree in History, is hoping to get into business consulting. He's applied for 55 jobs and secured around 15 interviews, but believes technology has hindered rather than helped his search. The interviews weren't what he was expecting. "They were all video-based screening interviews - I didn't even meet my potential employers," Peter tells the BBC.

If you ask my kids how they search for information on the Internet, they'll look at you like you're from outer space. "Why use the Internet when you can just ask Alexa?!?" Just like that, Generation Z is moving away from Googling what they want to know--and asking AI-powered assistants to find out for them. It's a shift that's been made possible due to huge recent leaps in machine learning and natural language processing (NLP), the technology powering conversational devices like Amazon's Echo and Google Home. Perhaps the coolest part: advancements in conversational AI are just now hitting the tip of the iceberg. It may not be long before Generation Z is turning to its bot assistants for talk therapy and dating advice.

This is part one of a three-part tutorial series in which you will use R to perform a variety of analytic tasks on a case study of musical lyrics by the legendary artist, Prince. Musical lyrics may represent an artist's perspective, but popular songs reveal what society wants to hear. Lyric analysis is no easy task. Because it is often structured so differently than prose, it requires caution with assumptions and a uniquely discriminant choice of analytic techniques. Musical lyrics permeate our lives and influence our thoughts with subtle ubiquity. The concept of Predictive Lyrics is beginning to buzz and is more prevalent as a subject of research papers and graduate theses. This case study will just touch on a few pieces of this emerging subject. To celebrate the inspiring and diverse body of work left behind by Prince, you will explore the sometimes obvious, but often hidden, messages in his lyrics. However, you don't have to like Prince's music to appreciate the influence he had on the development of many genres globally. Rolling Stone magazine listed Prince as the 18th best songwriter of all time, just behind the likes of Bob Dylan, John Lennon, Paul Simon, Joni Mitchell and Stevie Wonder. Lyric analysis is slowly finding its way into data science communities as the possibility of predicting "Hit Songs" approaches reality. Prince was a man bursting with music - a wildly prolific songwriter, a virtuoso on guitars, keyboards and drums and a master architect of funk, rock, R&B and pop, even as his music defied genres. In this tutorial, Part One of the series, you'll utilize text mining techniques on a set of lyrics using the tidy text framework.

In sequence-based generative models, besides the generation of samples likely to have been drawn from a data distribution, it is often desirable to finetune the samples towards some domain-specific metrics. This work proposes a method to guide the structure and quality of samples utilizing a combination of adversarial training and expert-based rewards with reinforcement learning. Building on SeqGAN, a sequence based Generative Adversarial Network (GAN) framework modeling the data generator as a stochastic policy in a reinforcement learning setting, we extend the training process to include domain-specific objectives additional to the discriminator reward. The mixture of both types of rewards can be controlled via a tune-able parameter. To improve training stability we utilize the Wasserstein distance as loss function for the discriminator. We demonstrate the effectiveness of this approach in two tasks: generation of molecules encoded as text sequences and musical melodies. The experimental results demonstrate the models can generate samples which maintain information originally learned from data, retain sample diversity, and show improvement in the desired metrics.

Three years ago when someone mentioned the idea of artificial intelligence most people would assume they were going to start referencing Star Wars, Robocop, Terminator, Space Odyssey and the Jetsons. In 2017 A.I. has taken on an entire new trend as more businesses start to invest in the technology and more references are being made to A.I. with tools and technology we use everyday. What tools or technology do I use today that leverages artificial intelligence? What does all this mean for businesses today and how should brands start embracing for a future of digital transformation powered by A.I.? What are some things that will be automated completely by A.I.? (Paperboy?) I answer these questions and more in part 1 of 4 in my "Translate The Geek-Speak" series on the FOMOFanz Podcast.

The Cloverfield Paradox would have been a theatrical failure. It's exactly the sort of B-grade sci-fi critics tend to eviscerate. So how do you generate hype for a movie that's practically doomed? If you're Netflix, you unveil a trailer during the Super Bowl with an unprecedented announcement: you'll be able to watch the film right after the game ends. It's the sort of "holy shit" moment you could only pull off if you're a global entertainment powerhouse.

Lawyers for Google spin-off Waymo are depicting Uber as a conniving company that relied on stolen trade secrets to catch up in the race to build self-driving cars. Uber's attorneys are brushing off the allegations as trumped-up claims designed to thwart a rival.

Is your job safe from automation, artificial intelligence and robotics? In episode 3 of the Project 10 Podcast series, Cormac speaks to a range of experts about the growing levels of fear and panic when it comes to technology and automation encroaching on people's jobs. With certain Oxford Studies claiming up to 47% of the workforce could be replaced by automation by 2034, is there a real reason you should be concerned for the future of your work, or are things being blown out of proportion? Former VP of marketing for Mozilla, Alex is a consultant to a number of top tech firms and co authored a best selling book, 'The Driver in The Driverless Car'. The book was long-listed by the Financial Times and McKinsey as one of their best business books of 2017.

Here at DivvyHQ, we love a good showdown. Take for example, the a-cappella Riff-off from Pitch Perfect 2. So good, right? As technology advances, many journalists, writers, brands and content marketers have reacted by pitting technology against humans with the same Hollywood flare used in all the films mentioned above. Are robo-bosses set to replace management? Will AI software soon write blog posts better than humans?! God help us no! Are Japanese robots replacing creative directors?