Everyone loves a good story. Scheherazade, that ingenious noblewoman in Arabian Nights, knew that a gripping tale could save lives.

Advances in artificial intelligence, coupled with an ongoing labor shortage, have construction companies looking for options to the traditional laborer.

OpenAI, a nonprofit focused on creating human-level artificial intelligence, just released an update to its GPT-2 text generator. I'm not being hyperbolic when I say that, after trying it, I'm legitimately terrified for the future of humanity if we don't figure out a way to detect AI-generated content – and soon. GPT-2 isn't a killer robot and my fears aren't that AI is going to rise up against us. I'm terrified of GPT-2 because it represents the kind of technology that evil humans are going to use to manipulate the population -- and in my opinion that makes it more dangerous than any gun. Here's how it works: you give it a prompt and it near-instantly spits out a bunch of words. What's scary about it is that it works.

At RADAR, journalists develop templates for AI to fill in with local data. These templates include "fragments of text and logical if-then-else rules for how to translate the data into location-specific text," according to its website. Data journalists have to come up with various angles and storylines, and then do some reporting to "add broad-strokes background information and national context, which are written into a template with a basic story structure." The result is stories with a similar core structure but with local details. Using AI, one data journalist can produce around 200 regional stories for each template every week, RADAR said.

The most popular Topic Modeling algorithm is LDA, Latent Dirichlet Allocation. Let's first unravel this imposing name to have an intuition of what it does. Figure 1 below describes how the LDA steps articulate to find the topics within a corpus of documents. "A document is generated by sampling a mixture of these topics and then sampling words from that mixture" (Andrew Ng, David Blei and Michael Jordan from the LDA original paper). NB: In the Figure 1 above, we have set K 3 topics and N 8 words in our vocabulary for illustration ease.

The "Artificial Intelligence in Retail Market" Report offers an inclusive and decision-making overview, including definitions, classifications and its …

To enable wider use of this powerful deep learning architecture, we propose two new methods. The first, adaptive attention span is a way to make Transformer networks more efficient for longer sentences. With this method, we were able to increase the attention span of a Transformer to over 8,000 tokens without significantly increasing computation time or memory footprint. The second, all-attention layer is a way to simplify the model architecture of Transformer networks. Even with a much simpler architecture, our all-attention network matched the state-of-the-art performance of Transformer networks.

This facial mapping technology has been designed to improve television language dubbing, but it also has strong potential for those seeking to deceive. BBC's Media Editor Amol Rajan looks at the benefits and risks - and tries it out himself. This story is part of a series by the BBC on disinformation and fake news - a global problem challenging the way we share information and perceive the world around us. To see more stories and learn more about the series visit www.bbc.co.uk/beyondfakenews