Insight analyst Lizzie Dunmore today published an analysis of Twitter interactions on the topic of AI between the general public, the media, and the 18 most influential AI influencers. Dunmore, who works for Onalytica, looked at a year's worth of tweets from 3,000 influencers and 1,000 news media outlets to figure out who the most influential AI tweeters tweeting on Twitter'twere (that's bad grammar; good poetic license). She also determined what impact their interactions (likes and retweets) had on the top media outlets publishing AI stories. Furthermore, she even revealed whether the media outlets or the individual influencers had more influence on the general public. The Top 50 Media Outlets had a 93x larger reach on average than the Top 50 Influencers yet, despite this, influencers' average engagement per follower on AI posts was 168x higher than that seen for media outlets.

In 1818, Mary Shelley created popular culture's first and most enduring monster in "Frankenstein; or, The Modern Prometheus." Since then, "women have always been the most important part of monster movies," as Mallory O'Meara states in "The Lady From the Black Lagoon," her engaging and compelling, if uneven, book about artist Milicent Patrick, the unsung designer of another iconic monster. As a teenager, indie horror filmmaker O'Meara became captivated by Universal Pictures' 1954 "Creature From the Black Lagoon." Its eponymous amphibian star -- a scaled, humanoid figure fondly known to generations of sci-fi geeks as the Gill-man -- was the last of Universal's classic monsters, joining the studio's pantheon alongside Dracula, the Frankenstein monster and his Bride, and the Wolfman, among others. The Gill-man was also, as O'Meara learned to her delighted amazement, the first -- and at the time, only -- movie monster to have been designed by a woman.

In addition to being fascinating, Artificial Intelligence (AI), is a game changer. It will have a great impact that go far beyond corporate profits or the economy. It will open vast new opportunities, but also reopen old technical, economical and even philosophical debates. And how should we evaluate it? There is a lot of confusion about what AI is. The term is now so overused it is approaching the status of marketing jargon. Scanning market research reports, it is clear that too many software companies now describe their products as "AI".

Machine learning is something new under the sun: a technology that builds itself. Right now we have limited algorithms with limited potential, but, if it exists, the Master Algorithm could derive all knowledge in the world from data. Inventing it would be one of the greatest advances in the history of science, speeding up the progress of knowledge across the board and changing the world in ways we can barely even begin to imagine. So says Pedro Domingos, professor of computer science at the University of Washington and author of the book "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World." He joined the podcast to discuss his book and the possible utopian and dystopian futures of AI. Below is a lightly-edited transcript of our conversation. You can also subscribe to my podcast on iTunes or Stitcher, or download the podcast here. JAMES PETHOKOUKIS: In the book you write, "Machine learning is something new under the sun, a technology that builds itself. And at its core, machine learning is about prediction: predicting what we want, the results of our actions, how to achieve our goals, how the world will change." Now, your book came out in 2015, and while it made the bookshelf of China's president in his New Year's address, I missed the book when it came out. The reason we are chatting today and I found out about the book was because Amazon recommended it to me. I had bought a previous book, called "Why Information Grows" by Cesar Hidalgo, who will also be a guest on an upcoming podcast, and when I bought that book it recommended your book as something I would also like. So, I bought it, and indeed I liked it very much. Now, for that recommendation I can thank machine learning, right? So machine learning tries to figure out what your tastes in books are, and clearly in this case it was a good call. So, for example, "Why Information Grows" -- by the way, I know Cesar well; he is a great guy -- is related to machine learning, so if you read that book you might like "The Master Algorithm" as well, and it seems that was a good call. Now, I suppose as a way of kind of explaining what machine learning is: How did that algorithm, Amazon, how did it do that?

A new type of spiking tool has been shown to improve artificially intelligent devices.

Nowadays, the field of artificial intelligence is more vibrant than ever, and some believe we're on the threshold of discoveries that could change …

Toyota unveil a self-driving car in 2013. Image: David Berkowitz (CC BY 2.0) (CC BY 2.0) From the algorithms used by social media platforms to the machine-learning that powers home automation, artificial intelligence has quietly embedded itself into our lives. But as the technology involved grows more advanced and its reach widens, the question of how to regulate has become increasingly urgent. The pitfalls of AI are well-documented. Race and gender prejudices have been discovered in a number of systems built using machine learning – from facial recognition software to internet search engines. Last week, the UK's Digital, Culture, Media and Sport select committee released its long-awaited fake news report.

The QWERTY typewriter was introduced in 1872, and since then tapping on a keyboard or screen has become the standard way to interact with digital technology. But this isn't always convenient or safe, so new "touchless" ways to control machines are being developed. Imagine being out for a jog, headphones on, and wanting to turn up the volume without breaking your stride. Or receiving a "new message" alert on your phone while driving and wanting to activate the text-to-speech function without taking your eye off the road. These are scenarios where touchless control would come in handy.

Recent advancements in radio frequency machine learning (RFML) have demonstrated the use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet, deep learning techniques have been shown, in other applications, to be vulnerable to adversarial machine learning (ML) techniques, which seek to craft small perturbations that are added to the input to cause a misclassification. The current work differentiates the threats that adversarial ML poses to RFML systems based on where the attack is executed from: direct access to classifier input, synchronously transmitted over the air (OTA), or asynchronously transmitted from a separate device. Additionally, the current work develops a methodology for evaluating adversarial success in the context of wireless communications, where the primary metric of interest is bit error rate and not human perception, as is the case in image recognition. The methodology is demonstrated using the well known Fast Gradient Sign Method to evaluate the vulnerabilities of raw IQ based Automatic Modulation Classification and concludes RFML is vulnerable to adversarial examples, even in OTA attacks. However, RFML domain specific receiver effects, which would be encountered in an OTA attack, can present significant impairments to adversarial evasion.