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.

Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology. Deep learning has achieved some very impressive accomplishments of late. I won't review them here, but chances are you already know about them anyhow. Given these high-profile successes, one could forgive the uninitiated (be they laymen or tech-savvy individuals) for the casual confounding of terms such as "artificial intelligence" and "deep learning," among others.

While DeepMind innovates in many fields, games like StarCraft and Go before that, demonstrate a computer's ability to be intuitive. In this case, intuition means that the computer is able to act unconsciously, non-rationally, and quickly, surpassing ordinary processing to deeply understand the information and the situation at hand. Given that these games have a nearly infinite number of moves, DeepMind's successes show that the AI is aware of its environment and of other players.

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.

The creators of a revolutionary AI system that can write news stories and works of fiction – dubbed "deepfakes for text" – have taken the unusual step of not releasing their research publicly, for fear of potential misuse. OpenAI, an nonprofit research company backed by Elon Musk, Reid Hoffman, Sam Altman, and others, says its new AI model, called GPT2 is so good and the risk of malicious use so high that it is breaking from its normal practice of releasing the full research to the public in order to allow more time to discuss the ramifications of the technological breakthrough. At its core, GPT2 is a text generator. The AI system is fed text, anything from a few words to a whole page, and asked to write the next few sentences based on its predictions of what should come next. The system is pushing the boundaries of what was thought possible, both in terms of the quality of the output, and the wide variety of potential uses.

The past year has been rich in events, discoveries and developments in AI. It is hard to sort through the noise to see if the signal is there and, if it is, what is the signal saying. This post attempts to get you exactly that: I'll try to extract some of the patterns in the AI landscape over the past year. And, if we are lucky, we'll see how some of the trends extend into the near future. Make no mistake: this is an opinion piece. I am not trying to establish some comprehensive record of accomplishments for the year. I am merely trying to outline some of these trends. Another caveat: this review is US-centric. A lot of interesting things are happening, say, in China, but I, unfortunately, am not familiar with that exciting ecosystem.

Can those memories serve to enhance retention of learnings and help-built connections or patterns we could not do in a conscious state? What is also intriguing is the work being done in projects like "Deep Dream" which uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like hallucinogenic appearance in the deliberately over-processed images. We may indeed be in the cusp of the "next big thing". But this is not new, AI pretty much has been at the forefront of all major conversations pertaining to technology over the past few years. From self-driving cars to intuitive virtual assistants, AI has been turning every science-fiction trope into reality.

The main goal of this study is to investigate the robustness of graph-based Deep Learning (DL) models used for Internet of Things (IoT) malware classification against Adversarial Learning (AL). We designed two approaches to craft adversarial IoT software, including Off-the-Shelf Adversarial Attack (OSAA) methods, using six different AL attack approaches, and Graph Embedding and Augmentation (GEA). The GEA approach aims to preserve the functionality and practicality of the generated adversarial sample through a careful embedding of a benign sample to a malicious one. Our evaluations demonstrate that OSAAs are able to achieve a misclassification rate (MR) of 100%. Moreover, we observed that the GEA approach is able to misclassify all IoT malware samples as benign.