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

Researchers at the non-profit AI research group OpenAI just wanted to train their new text generation software to predict the next word in a sentence. It blew away all of their expectations and was so good at mimicking writing by humans they've decided to pump the brakes on the research while they explore the damage it could do. Elon Musk has been clear that he believes artificial intelligence is the "biggest existential threat" to humanity. Musk is one of the primary funders of OpenAI and though he has taken a backseat role at the organization, its researchers appear to share his concerns about opening a Pandora's box of trouble. This week, OpenAI shared a paper covering their latest work on text generation technology but they're deviating from their standard practice of releasing the full research to the public out of fear that it could be abused by bad actors.

If the eyes are the windows to the soul, the pair belonging to the cyborg heroine of Alita: Battle Angel are a set of double doors flung wide open, as limpid and blossoming as a Keane painting's. Alita (Rosa Salazar) enters the movie atop a heap of scrap outside the settlement of Iron City, which is where most of what human life remains on Earth has clustered in the mid–26th century. Or rather, her head does, along with a remnant of metallic spine dangling below. Storefront cybernetic surgeon Dyson Ido (Christoph Waltz) finds Alita's central nervous system and rebuilds her from the neck down, but the movie, which was directed by Robert Rodriguez and co-written by James Cameron and Laeta Kalogridis, works in the other direction, from the gut--or is it the crotch--to the heart, only occasionally making it all the way to the brain. Although it's set in the year 2563, the driving force behind Alita is nostalgia.

We will now explore the most important ones.

The spread of fake news is already a very real problem. Artificial intelligence could make the problem even worse. That prospect is so frightening that an Elon Musk-backed non-profit called OpenAI has decided not to publicly circulate AI-based text generation technology that enables researchers to spin an all-too-convincing--and yes, fabricated--machine-written article. "Due to our concerns about malicious applications of the technology, we are not releasing the trained model," OpenAI blogged. Such concerns go beyond just generating misleading news articles.

For visual-effects artists, time is always a struggle. When the call comes in to create something spectacular, artists and supervisors have to calculate how much run- way they have to get from the point of the idea for the vfx to the deadline. On "Avengers: Infinity War," the vfx crew found that a new innovation -- machine learning -- made it possible to create the character Thanos in a way that would have simply been impossible without it. The filmmakers envisioned a version of Thanos -- played by Josh Brolin -- that would be CG, but also incorporate all the subtle facial expressions and delicate hallmarks of a physical performance that could only been done by an actor. They knew that the facial tracking tech was there but asking vfx artists to manually adjust every inch of the CG version of the face of Thanos once they had all the tracking and scanning information would have been a disaster.

Algorithms have long been able to produce basic news stories from press releases or sets of financial data; that's not much of a threat to most humans in the news business. Now, however, artificial intelligence has taken a step further. It's learned to perform a tougher task – to produce convincing-looking fake news. Stringing together a few formulaic passages from a set of numbers is a mechanical job. Inventing a fake news story on a random subject requires imagination; not every human is up to it. The San Francisco-based nonprofit OpenAI, founded by Tesla Chief Executive Officer Elon Musk and Y Combinator President Sam Altman, has produced a so-called language model that can do it.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. In September 2012, Alex Krizhevsky and Ilya Sutskever, two AI researchers from the University of Toronto, made history at ImageNet, a popular competition in which participants develop software that can recognize objects in a large database of digital images. Krizhevsky and Sutskever, and their mentor, AI pioneer Geoffrey Hinton, submitted an algorithm that was based on deep learning and neural networks, an artificial intelligence technique that the AI community viewed with skepticism because of its past shortcomings. AlexNet, the deep learning algorithm developed by the U of T researchers, was able to win the competition with an error rate of 15.3 percent, a whopping 10.8 percent better than the runner up. By some accounts, the event triggered the deep learning revolution, creating interest in the field by many academic and commercial organizations. Today, deep learning has become pivotal to many of the applications we use every day such as content recommendation systems, translation apps, digital assistants, chatbots and facial recognition systems.