Existing Markov Chain Monte Carlo (MCMC) methods are either based on general-purpose and domain-agnostic schemes which can lead to slow convergence, or hand-crafting of problem-specific proposals by an expert. We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties. First, we propose an efficient likelihood-free adversarial training method to train a Markov chain and mimic a given data distribution. Then, we leverage flexible volume preserving flows to obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to train efficient Markov chains to sample from a prescribed posterior distribution by iteratively improving the quality of both the model and the samples. A-NICE-MC provides the first framework to automatically design efficient domain-specific MCMC proposals. Empirical results demonstrate that A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of deep neural networks, and is able to significantly outperform competing methods such as Hamiltonian Monte Carlo.

This is the second installment in a three-part series exploring the impact of artificial intelligence (NYSE:AI) on investment management. I want to thank the speakers at the AI and the Future of Financial Services Forum, hosted by CFA Institute and CFA Society Beijing, for inspiring this series. The first installment offered a primer on the AI technologies that are relevant to investment professionals. Artificial intelligence (AI) is coming to the investment world. With the help of deep learning techniques, AI researchers have made significant strides in natural language processing (NLP), speech recognition, and image recognition.

Google has quietly secured a contract to work on the Defense Department's new algorithmic warfare initiative, providing assistance with a pilot project to apply its artificial intelligence solutions to drone targeting. The military contract with Google is routed through a Northern Virginia technology staffing company called ECS Federal, obscuring the relationship from the public. The contract, first reported Tuesday by Gizmodo, is part of a rapid push by the Pentagon to deploy state-of-the-art artificial intelligence technology to improve combat performance. Google, which has made strides in applying its proprietary deep learning tools to improve language translation, and vision recognition, has a cross-team collaboration within the company to work on the AI drone project. The team, The Intercept has learned, is working to develop deep learning technology to help drone analysts interpret the vast image data vacuumed up from the military's fleet of 1,100 drones to better target bombing strikes against the Islamic State.

User-defined communities are an essential component of many web platforms, where users express their ideas, opinions, and share information. However, despite their positive benefits, online communities also have the potential to be breeding grounds for conflict and anti-social behavior. Here we used 40 months of Reddit comments and posts (from January 2014 to April 2017) to examine cases of intercommunity conflict ('wars' or'raids'), where members of one Reddit community, called "subreddit", collectively mobilize to participate in or attack another community. We discovered these conflict events by searching for cases where one community posted a hyperlink to another community, focusing on cases where these hyperlinks were associated with negative sentiment (e.g., "come look at all the idiots in community X") and led to increased antisocial activity in the target community. We analyzed a total of 137,113 cross-links between 36,000 communities.

After reading this research paper, I'd buy into an ICO for the IcoRating Coin for sure. But on a serious note, this is both well-timed and invaluable. While many people that invested around November (hopefully) learned their lesson on doing their own research and investigation into those same sources that your ML system analyzes (white paper, team member Linkden pages, Github repos, the website itself... etc), this is undoubtedly impressive work and should assist in analyzing the neverending onslaught of offerings going forward.

Financial services jobs go in and out of fashion. In 2001 equity research for internet companies was all the rage. In 2006, structuring collateralised debt obligations (CDOs) was the thing. In 2010, credit traders were popular. In 2014, compliance professionals were it.

A new deep-learning algorithm studies aerial photographs after fires to identify damage. How it works: From satellite images taken before and after the California wildfires of 2017, researchers created a data set of buildings that were either damaged or left unscathed. The results: They tweaked a pre-trained ImageNet neural network and got it to spot damaged buildings with an accuracy of up to 85 percent. Why it matters: After a disaster, pinpointing the hardest-hit areas could save lives and help with relief efforts. The researchers also released the data set to the public, which could improve other research that requires satellite images, like conservation and developmental aid work.

It's impossible to read about the future of healthcare without encountering two pixilated vowels that, together, represent the hopes and fears of an industry seeking more intelligent solutions. Though the field of artificial intelligence (AI) has been around since 1956, it has made precious few contributions to medical practice. Only recently has the hype of machine-based learning begun to merge with reality. What Is Artificial Intelligence, Really? Confusion surrounding AI – its applications in healthcare and even its definition – remains widespread in popular media.

Acclaimed physicist, author and educator Stephen Hawking has claimed that artificial intelligence may cause the end of civilization as we know it. While I don't think we're headed for a dire, Terminator-like state, there's little doubt that artificial intelligence has the capacity to change the world. That change first affected industrial and mechanical industries by assisting in mass production, but today businesses of all types are discovering the benefits of artificial intelligence in the workplace. Leading organizations are employing artificial intelligence to work alongside employees for more effective and efficient results. Artificial intelligence capabilities can be grouped into three main categories: cognitive computing, machine learning and deep learning.

The advertising world loves big, shiny, techy things. Agency and client ears perk right up when they hear about virtual reality kiosks, gadget-filled activations and holograms of dead rock stars. But then there are the tech innovations that sound a bit, or a lot, less sexy. Deep learning is a subset of machine learning that essentially teaches computers to find patterns in sounds, images and other data. And while that may not seem like much fun to your average social marketer or copywriter, the tech giants--the Facebooks, Apples, Googles, Netflixes, Microsofts and Baidus of the world--are investing massive sums of money in it.