One of the hottest topics the last few years or so have been around AI in all it's forms, everything from being simple female-named chatting tools, to domesday predictions AI that will kill us all, or just make us fat and obsolete in the workplace. So far what we've seen and be able to play with has mostly been in the form of chat bots, helping us navigate through over complicated websites or get some very limited customer service help. November 6, 2001 "Treehouse of Horror XII" was aired, with Pierce Brosnan starring as Ultrahouse 3000. An smart building AI that becomes attracted to Marge and decides to get rid of Homer, attempting to kill him by dumping him into the dining room table's garbage disposal. Alas containing many of the common fears of what an AI eventually will do to us.

As much as we love to fawn over artificial intelligence (AI), it's still not great at recognizing and parsing natural language. That's why Google is open sourcing its new language parsing model for English, which it calls'Parsey McParseface.' Before you even ask, the name has no meaning. When Google was trying to figure out what to call its language parsing technology, someone suggested Parsey McParseface; it's a bit like Apple's Liam, which has no clever backstory either. The overall AI model model is called SyntaxNet (please make your SkyNet jokes now); 'ol Parsey is just for English. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

The new work, which the researchers are presenting this week at the International Conference on Robotics and Automation, builds on a long sequence of papers on origami robots from the research group of Daniela Rus, the Andrew and Erna Viterbi Professor in MIT's Department of Electrical Engineering and Computer Science. "It's really exciting to see our small origami robots doing something with potential important applications to health care," says Rus, who also directs MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "For applications inside the body, we need a small, controllable, untethered robot system. It's really difficult to control and place a robot inside the body if the robot is attached to a tether." Joining Rus on the paper are first author Shuhei Miyashita, who was a postdoc at CSAIL when the work was done and is now a lecturer in electronics at the University of York, in England; Steven Guitron, a graduate student in mechanical engineering; Shuguang Li, a CSAIL postdoc; Kazuhiro Yoshida of Tokyo Institute of Technology, who was visiting MIT on sabbatical when the work was done; and Dana Damian of the University of Sheffield, in England.

A pill that unfolds into a little robot could one day give parents everywhere a little more peace of mind. Once swallowed, it can open up inside a person's stomach, crawling across the stomach wall to retrieve a single-cell button battery, and even patch wounds. This is no small thing. In the US every year, over 3,500 incidents of swallowed button batteries are reported in the US, and most cases of battery swallowing involve toddlers. Although most of these batteries are safely digested, sometimes they can leak and cause tissue burns, bleeding, and death.

Researchers have developed a computer system that can train dogs without the help of a human. The team from North Carolina State University developed a harness which contains an array of sensors to monitor posture and body language. Using a small built-in computer to transmit data, the system can give out rewards for correct behaviour by releasing a treat from a nearby dispenser. The team from North Carolina State University developed a harness which contains an array of sensors to monitor posture and body language. Researchers from North Carolina State University developed a harness which contains an array of sensors to monitor posture and body language.

Brian Lesko and Dan Sherman hate the idea of driverless cars, but for very different reasons. Lesko, 46, a business-development executive in Atlanta, doesn't trust a robot to keep him out of harm's way. "It scares the bejeebers out of me," he says. Sherman, 21, a mechanical-engineering student at the University of Minnesota, Twin Cities, trusts the technology and sees these vehicles eventually taking over the road. But he dreads the change because his passion is working on cars to make them faster.

Good afternoon, my name is --, and I'll be your conference coordinator today. I would like to welcome everyone to NVIDIA (NASDAQ: NVDA) Financial Results Conference Call. All lines have been placed on mute. After the speakers' remarks, there will be a question-and-answer period. Participants to register for question by pressing one followed by the four on your telephone. This is conference is being recorded Thursday, May 12, 2016. I would now like to turn the call over to Arnab Chanda Vice President of Investor Relations at NVIDIA. With me on the call today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that today's call is being webcast live on NVIDIA's Investor Relations website. It is also being recorded. You can hear a replay by telephone until May 19, 2016. The webcast will be available for replay up until next quarter's conference call to discuss Q2 financial results. The content of today's call is NVIDIA's property. It cannot be reproduced or transcribed without our prior written consent. During the course of this call, we may make forward-looking statements based on current expectations.

Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing with large data sets. Here, we derive some simple results which we have found useful for speeding up the learning stage in the GPR algorithm, and especially for performing Bayesian model comparison between different covariance functions. We apply our techniques to both synthetic and real data and quantify the speed-up relative to using nested sampling to numerically evaluate model evidences.

We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with potentially infinite state-space. Here we propose a novel efficient algorithm for joint state / parameter posterior sampling in population Markov Jump processes. We introduce a class of pseudo-marginal sampling algorithms based on a random truncation method which enables a principled treatment of infinite state spaces. Extensive evaluation on a number of benchmark models shows that this approach achieves considerable savings compared to state of the art methods, retaining accuracy and fast convergence. We also present results on a synthetic biology data set showing the potential for practical usefulness of our work.

Bayesian Optimisation (BO) is a technique used in optimising a $D$-dimensional function which is typically expensive to evaluate. While there have been many successes for BO in low dimensions, scaling it to high dimensions has been notoriously difficult. Existing literature on the topic are under very restrictive settings. In this paper, we identify two key challenges in this endeavour. We tackle these challenges by assuming an additive structure for the function. This setting is substantially more expressive and contains a richer class of functions than previous work. We prove that, for additive functions the regret has only linear dependence on $D$ even though the function depends on all $D$ dimensions. We also demonstrate several other statistical and computational benefits in our framework. Via synthetic examples, a scientific simulation and a face detection problem we demonstrate that our method outperforms naive BO on additive functions and on several examples where the function is not additive.