Looking to leverage AI in your organization? Don't miss the Business Summit at the AI Conference in New York, April 15–18, 2019. Register before March 1 to save with Early Price. In a recent post, we described what it would take to build a sustainable machine learning practice. By "sustainable," we mean projects that aren't just proofs of concepts or experiments. A sustainable practice means projects that are integral to an organization's mission: projects by which an organization lives or dies. These projects are built and supported by a stable team of engineers, and supported by a management team that understands what machine learning is, why it's important, and what it's capable of accomplishing. Finally, sustainable machine learning means that as many aspects of product development as possible are automated: not just building models, but cleaning data, building and managing data pipelines, testing, and much more. Machine learning will penetrate our organizations so deeply that it won't be possible for humans to manage them unassisted. Organizations throughout the world are waking up to the fact that security is essential to their software projects. Nobody wants to be the next Sony, the next Anthem, or the next Equifax.

When a news article revealed that Clarifai was working with the Pentagon and some employees questioned the ethics of building artificial intelligence that analyzed video captured by drones, the company said the project would save the lives of civilians and soldiers. "Clarifai's mission is to accelerate the progress of humanity with continually improving A.I.," read a blog post from Matt Zeiler, the company's founder and chief executive, and a prominent A.I. researcher. Later, in a news media interview, Mr. Zeiler announced a new management position that would ensure all company projects were ethically sound. As activists, researchers, and journalists voice concerns over the rise of artificial intelligence, warning against biased, deceptive and malicious applications, the companies building this technology are responding. From tech giants like Google and Microsoft to scrappy A.I. start-ups, many are creating corporate principles meant to ensure their systems are designed and deployed in an ethical way.

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.