The use of fake news stories for political disinformation has become a major concern for governments around the world in the wake of the 2016 U.S. presidential election. The Federal Bureau of Investigation concluded Russia used false news reports, spread through social media, to try to sway voters. Writing these stories still needed someone to sit behind a keyboard. Now OpenAI, a non-profit artificial intelligence research group in San Francisco, has unveiled a machine learning algorithm that can generate coherent text, including fake news articles, after being given just a small sample to build on. The algorithm can be tuned to imitate the writing style of the sample text.

OpenAI, a non-profit research company investigating "the path to safe artificial intelligence," has developed a machine learning system called Generative Pre-trained Transformer-2 (GPT-2), capable of generating text based on brief writing prompts. The result comes so close to mimicking human writing that it could potentially be used for "deepfake" content. Built based on 40 gigabytes of text retrieved from sources on the Internet (including "all outbound links from Reddit, a social media platform, which received at least 3 karma"), GPT-2 generates plausible "news" stories and other text that match the style and content of a brief text prompt. The performance of the system was so disconcerting, now the researchers are only releasing a reduced version of GPT-2 based on a much smaller text corpus. Due to concerns about large language models being used to generate deceptive, biased, or abusive language at scale, we are only releasing a much smaller version of GPT-2 along with sampling code.

The AI icon Andrew Ng is quoted as saying, "Artificial intelligence is the new electricity."

Anyone who's watched "Bridget Jones's Diary" knows one of her New Year's resolutions is "Not go out every night but stay in and read books and listen to classical music." The reality, however, is substantially different. What people actually do in their leisure time often doesn't match with what they say they'll do. Economists have termed this phenomenon "hyperbolic discounting." In a famous study titled "Paying Not to Go to the Gym," a couple of economists found that, when people were offered the choice between a pay-per-visit contract and a monthly fee, they were more likely to choose the monthly fee and actually ended up paying more per visit.

"It's very visible that technological sectors are now prioritising the implementation of AI in their everyday workforce." "We can see that the companies listed in the research are already using different types of AI-technology to improve the way they engage with their users and customers." Google's translation service, for example, uses AI tools such as machine learning and natural language processes to provide real-time translations, he explained.

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anankumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.

So convincing, in fact, that the researchers have refrained from open-sourcing the code, in hopes of stalling its potential weaponization as a means of mass-producing fake news. An OpenAI employee printed out this AI-written sample and posted it by the recycling bin: https://t.co/PT8CMSU2AR While the impressive results are a remarkable leap beyond what existing language models have achieved, the technique involved isn't exactly new. Instead, the breakthrough was driven primarily by feeding the algorithm ever more training data--a trick that has also been responsible for most of the other recent advancements in teaching AI to read and write. "It's kind of surprising people in terms of what you can do with [...] more data and bigger models," says Percy Liang, a computer science professor at Stanford.

The organization created a machine learning algorithm, GPT-2, that can produce natural-looking language largely indistinguishable from that of a human writer while largely "unsupervised" – it needs only a small prompt text to provide the subject and context for the task. The team have made some strides toward this lofty goal, but have also somewhat inadvertently admitted that, once perfected, the device can mass-produce fake news on an unprecedented scale. "We have observed various failure modes," the team observed. "Such as repetitive text, world modelling failures (eg the model sometimes writes about fires happening under water), and unnatural topic switching." Here's a short story i generated using OpenAI's GPT-2 tool (prompt in bold) pic.twitter.com/DGIVwGuAUV