This month we heard about a fascinating AI experiment from a US Air Force colonel. An AI-controlled drone trained to autonomously carry out bombing missions had turned on its human operator when told not to attack targets; its programming prioritised successfully carrying out missions, so it saw human intervention as an obstacle in its way and decided to forcefully take it out. The only problem with the story was that it was nonsense. Firstly, as the colonel told it, the test was a simulation. Secondly, a US Air Force statement was hastily issued to clarify that the colonel, speaking at a UK conference, had "mis-spoke" and that no such tests had been carried out.

In a story that could be ripped from a sci-fi thriller, the hyper-motivated AI had been trained to destroy surface-to-air missiles only with approval from a human overseer – and when denied approval, it turned on its handler. Only, it is no surprise that story sounds fictional – because it is. The story emerged from a report by the Royal Aeronautical Society, describing a presentation by US Air Force (USAF) colonel Tucker Hamilton at a recent conference. That report noted the incident was only a simulation, in which there was no real drone and no real risk to any human – a fact missed by many attention-grabbing headlines. Later, it emerged that even the simulation hadn't taken place, with the USAF issuing a denial and the original report updated to clarify that Hamilton "mis-spoke". The apocalyptic scenario was nothing but a hypothetical thought experiment.

Before my first professional job, I would listen to developers talk about what it was like to work on someone else's messy code that consisted of anti-patterns. They would tell horror stories. Then, I took my second assignment as a fresh Dotnet developer, and that horror was exactly what I had been scared of. My new job was to integrate engineering rule sets into an engineering application. The application was already developed and running with a library with three rule sets.

We all know that there are troves of data that exist online about us and our browsing, clicking, and spending habits. However, given all that information and the people that spend their lives on the internet, how do those who tailor the ads we see parse that information? As with many things these days, it's useful to have machines to help. RJ Talyor is the CEO and founder of Pattern89, an Indianapolis-based marketing firm using the power of artificial intelligence (AI) to help advertisers figure out what works and what doesn't when it comes to the ads we see everyday. I spoke with him about how AI is helping marketers figure out not only who to target, but what elements to include in those ads.

Slate has relationships with various online retailers. If you buy something through our links, Slate may earn an affiliate commission. We update links when possible, but note that deals can expire and all prices are subject to change. All prices were up to date at the time of publication. The simplest way to summarize the plot of Ready Player Two is to repeat the plot of its predecessor, Ready Player One, as they are largely the same.

One of the classic tools for decision automation is a business rules management system, or BRMS. While these systems are powerful and effective when used correctly, many companies fail to apply best practices when using them. Here's one of our favorite horror stories about a well-intentioned BRMS implementation gone awry. Some years ago, we got a call from a company asking for our help with their business rules project. They said that they had hit a milestone in their rules analysis--it was 80% complete, or so they thought.

Sign in to report inappropriate content. The MIT Media Lab created an artificial intelligence to write short horror stories. "Shelley," as the A.I. was later named, came up with tales that are the stuff of nightmares.

AI attracts a lot of attention from many different industries, and marketing is no exception. There are a lot of discussions going on about how AI can help companies make their marketing efforts more efficient. When speaking about AI in marketing, the focus is usually on how AI can effectively process big data to make predictions, help marketers save time on repetitive tasks, or drive conclusions from big chunks of data. AI already helps us with automation and making better marketing decisions, so it's safe to say the technology can execute complex analytical tasks. But we all know marketing is not only about crunching data -- it is also about creativity.

She's a robot who uses artificial intelligence to write and share hair-raising horror stories. Named after famed "Frankenstein" writer Mary Shelley, the bot is the brainchild of MIT postdoctoral candidate Pinar Yanardag and research scientist Manuel Cebrian. They claim that Shelley is the world's first collaborative AI horror writer. On Oct. 27, Shelley came to life. But, before she did, co-creators Pinar Yanardag and Manuel Cebrian spent a lot of time figuring out where to obtain enough data to power Shelley.

A "lazy animator" has given a glimpse of what futuristic films might look like if artificial intelligence takes over the job of filmmaker. Digital animator Julius Horsthuis created a fantasy science fiction film using fractals--infinitely complex patterns that are created using an ongoing feedback loop. His latest short film Fraktaal used fractal patterns to automatically generate an alien civilization and landscape. The alien worlds generated by artificial intelligence are made using fractals. "I borrowed the aesthetic from science fiction films, and isolated that into something that I hope will stimulate the imagination into its own spacefaring journey," said Horsthuis.