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.

Recycling is NOT good for the world. It is bad for the environment, it is bad for our health, and it is bad for our economy. These are the words of GPT-2, an artificially intelligent super-troll. It needs just a few words to prompt a rant hundreds of words long on almost any topic and its creators say it may be too dangerous to release to the public because of potential misuse. However, these fears are overblown.

OpenAI, the artificial intelligence research company founded by tech heavyweights including Elon Musk and Peter Thiel, says it's developed the most advanced language-processing algorithm so far. Sample outputs suggest that the AI system is an extraordinary step forward, producing text rich with context, nuance and even something approaching humor. It's so good, in fact, that OpenAI says it's not releasing its code to the public because its researchers are scared it could be misused, according to a new blog post. The algorithm, GPT-2, was trained on some 8 million web pages, according to the new research. Given a prompt, GPT-2 is tasked with predicting the next word based how those words have been used on the websites it read.

A team of researchers who have built an artificially-intelligent writer say they are withholding the technology as it might be used for "malicious" purposes. OpenAI, based in San Francisco, is a research institute backed by Silicon Valley luminaries including Elon Musk and Peter Thiel. It shared some new research on using machine learning to create a system capable of producing natural language, but in doing so the team expressed concern the tool could be used to mass-produce convincing fake news. Which, to put it another way, is of course also an admission that what its system puts out there is unreliable, made-up rubbish. Still, when it works well, the results are impressively realistic in tone - which is why I've shared a sample of it below.

AI is getting better and better at writing convincing material, and that's leading its creators to wonder whether they should release the technology in the first place. Elon Musk's OpenAI has developed an algorithm that can generate plausible-looking fake news stories on any topic using just a handful of words as a starting point. It was originally designed as a generalized language AI that could answer questions, summarizing stories and translating text, but researchers soon realized that it could be used for far more sinister purposes, like pumping out disinformation in large volumes. As a result, the team only plans to make a "simplified version" of its AI available to the public, according to MIT Technology Review. The technology thankfully has some rough edges at the moment. It frequently writes stories that are either plagiarized or are only cohesive on the surface, and only occasionally hits the jackpot.

In 2015, car-and-rocket man Elon Musk joined with influential startup backer Sam Altman to put artificial intelligence on a new, more open course. They cofounded a research institute called OpenAI to make new AI discoveries and give them away for the common good. Now, the institute's researchers are sufficiently worried by something they built that they won't release it to the public. The AI system that gave its creators pause was designed to learn the patterns of language. It does that very well--scoring better on some reading-comprehension tests than any other automated system.

Artificial intelligence (AI) systems' rapid advances are continually crossing rows off the list of things humans do better than our computer compatriots. AI has bested us at board games like chess and Go, and set astronomically high scores in classic computer games like Ms. Pacman. More complex games form part of AI's next frontier. While a team of AI bots developed by OpenAI, known as the OpenAI Five, ultimately lost to a team of professional players last year, they have since been running rampant against human opponents in Dota 2. Not to be outdone, Google's DeepMind AI recently took on--and beat--several professional players at StarCraft II.

Learning the generative model, i.e., the underlying data generating distribution, based on large amounts of data is one the fundamental task in machine learning and statistics [46].Recent advances in deep generative models have provided novel techniques for unsupervised and semi-supervised learning, with broad application varying from image synthesis [44], semantic image editing [60], image-to-image translation [61] to low-level image processing [29]. Implicit deep generative model is a powerful and flexible framework to approximate the target distribution by learning deep samplers [38] including Generative adversarialnetworks (GAN) [16] and likelihood based models, such as variational auto-encoders (VAE) [23] and flow based methods [11], as their main representatives. The above mentioned implicit deep generative models focus on learning a deterministic or stochastic nonlinear mapping that can transform low dimensional latent samples from referenced simple distribution to samples that closely match the target distribution. GANs build a minmax two player game between the generator and discriminator. During the training, the generator transforms samples from a simple reference distribution into samples that would hopefully to deceive the discriminator, while the discriminator conducts a differential two-sample test to distinguish the generated samples from the observed samples. The objective of vanilla GANs amounts to the Jensen-Shannon (JS) divergence between the learned distribution and target distributions. The vanilla GAN generates sharp image samples but suffers form the instability issues [3]. A myriad of extensions to vanilla GANs have been investigated, both theoretically or empirically, in order to achieve a stable training and high quality sample generation.

"Generative A.I. systems are making a wide range of creative skills more accessible. Creative.AI co-founder Samim A Winiger gives a quick introduction to creativity in relation to machine learning and AI. Today it is possible to generate virtual worlds with billions of unique planets. But how do we think about creativity and how do we build systems for it? One example Samim gives is a music mood agent he worked on for a contemporary band.

I've been spending weeks trying to get an actor-critic reinforcement learning model to work with the OpenAi Pendulum environment, but I haven't been able to solve it, yet. The critic (value) model is predicting the value well and its loss is low. The actor, however, is predicting actions all over the place with it's mean (mu) and variance (sigma) totally not aligned with what they should be. If I limit the mean using a tanh activation then the sigma will keep going up towards infinity. I've tried different activation functions, initializers, and hyper-parameters, but nothing seems to work.