I'm not (quite) dead and intend to go back to posting stuff every now and then. Last July, I've also started a new job, as an assistant professor in the Department of Statistics at Harvard University, after having spent two years in Oxford. At some point, I might post something on the cultural difference between the European English and American communities of statisticians. In the coming weeks, I'll tell you all about a new paper entitled Coupling of Particle Filters, co-written with Fredrik Lindsten and Thomas B. Schön from Uppsala University in Sweden. We are excited about this coupling idea because it's simple and yet brings massive gains in many important aspects of inference for state space models (including both parameter inference and smoothing).

Craft beer could be the next unlikely beneficiary of the artificial intelligence revolution. London-based IntelligentX Brewing Company has revealed its new AI Beer range. These are beers that will be improved over time using advanced algorithms. The brewer is employing an online feedback system (via a Facebook Messenger bot) to obtain data on how well-liked its Pale, Amber, Black and Golden beers are by customers. The company will then employ "complex machine learning algorithms," combining reinforcement learning and bayesian optimisation, to search for trends among this feedback and tune the recipes accordingly. "Because our A.I. is constantly reacting to user feedback, we can brew beer that matches what you want, more quickly than anyone else can," says IntelligentX Brewing Company.

Because we'll need something tasty to swill when our robot overlords finally come into their full artificial intelligence, a company in the UK is attempting to figure out if robots can help humans brew a better beer. While there won't be robots stirring batches of wort or sorting hops, artificial intelligence will play a big part in London-based firm IntelligentX's plan to brew beer, CNET reports. Here's how it'd work: consumers would try one of the company's four beers -- Amber AI, Black AI, Golden AI and Pale AI ---- and then weigh in via Facebook chat bot on the experience. That feedback will be fed to an algorithm called Automated Brewing Intelligence, or ABI, which will use the information to make changes to the next batch. Reinforcement learning and a process called bayesian decision making will teach the AI about the brewing experience.

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Project Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. Microsoft today announced that they are making it available for everyone on GitHub via an open-source license. This project was formerly known as Project AIX and has now been renamed Project Malmo. Minecraft is ideal for artificial intelligence research for the same reason it is addictively appealing to the millions of fans who enter its virtual world every day. Unlike other computer games, Minecraft offers its users endless possibilities, ranging from simple tasks like walking around looking for treasure to complex ones like building a structure with a group of teammates.

"Really, we're building collision avoidance for industrial drones," said Alexander Harmsen, CEO and co-founder of Iris Automation. "We see this huge need for industrial drones for mining exploration, pipeline inspection, agricultural surveying, forestry, or even package delivery." Without a way to avoid mid-air collisions, drones risk crashing into a Cessna, a flock of geese or a 747. Worst case scenario: a drone gets sucked into a jet engine causing catastrophic engine failure as high-velocity bits of metal penetrate fuel tanks, hydraulic lines and the cabin. Iris Automation's solution is an AI computer that blends real-time images and 3D maps to track incoming objects.

Intelligence, defined as the ability to acquire knowledge and skills. Intelligence for the longest time possible is associated with the human brain. Artificial intelligence is basically defined as intelligence that is originating from machines. Most computer applications only make existing processes and functions faster and maybe more efficiently but cannot create new duties altogether. However, artificial intelligence has already challenged this notion.

Word embeddings have been shown to be useful across state-of-the-art systems in many natural language processing tasks, ranging from question answering systems to dependency parsing. (Herbelot and Vecchi, 2015) explored word embeddings and their utility for modeling language semantics. In particular, they presented an approach to automatically map a standard distributional semantic space onto a set-theoretic model using partial least squares regression. We show in this paper that a simple baseline achieves a +51% relative improvement compared to their model on one of the two datasets they used, and yields competitive results on the second dataset.

In July 2016 was the first case where Police officers in Dallas, United States, used a robot to kill an armed suspect during a Black Lives Matter protest. The device was not autonomous, but in the future it could be. And although there are many cases of remote warfare within militaries, such as the case with drones, this was the first occasion where such technology was used in public. There are real concerns around artificial intelligence causing chaos like the scenarios depicted in Hollywood movies such as Terminator, Robocop, Iron Man and iRobot in the future.

Remember last summer's influx of convolutional neural network art, which took the form of hallucinogenic-like DeepDream images, like the one above? Prompted by a blog post and code release by a team of Google engineers, haunting composite generation--also known as inceptionism, as a nod to the movie-related internet meme "we need to go deeper"--became the poster child for artificial neural networks. In "A Neural Algorithm of Artistic Style" Leon Gatys, Alexander Ecker and Matthias Bethge describe it as a system that "uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images." Imagine your vacation photos rendered in the style of Pablo Picasso, or Leonardo da Vinci's Mona Lisa painted in the style of Vincent Van Gogh's Starry Night. You can see that example directly, in "Machines and Metaphors," a blog post by artist and programmer Gene Kogan.