We present ongoing research to develop novel option discovery methods for complex domains that are represented as Object-Oriented Markov Decision Processes (OO-MDPs) (Diuk, Cohen, and Littman, 2008). We describe Portable Multi-policy Option Discovery for Automated Learning (P-MODAL), an initial framework that extends Pickett and Barto's (2002) PolicyBlocks approach to OO-MDPs. We also discuss future work that will use additional representations and techniques to handle scalability and learning challenges.

Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide accurate and robust positional information. In ultrasound applications, typically only two-dimensional data from a linear array are available, and as such obtaining accurate positional estimation in three dimensions is non-trivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed ultrasound image. The obtained estimate is then combined with a Kalman filtering approach that utilises positioning estimates obtained in previous time-frames to improve localisation robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical ultrasound imaging setup. Accurate and robust positional information is provided in real-time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1mm for simulated data and a mean error of 0.2mm for experimental data. Three-dimensional localisation is most accurate for elevational distances larger than 1mm, with a maximum distance of 5mm considered for a 25mm aperture.

Cybersecurity used to exist in the realm of science fiction. Think retinal scans from Star Trek. I cut my teeth on Star Trek and I'm still holding out for a Tricorder during my lifetime. In the present, we live a hyper-connected life, with technology informing everything we do. We use smart devices like phones and watches.

A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."

The system consists of an overall agent architecture and five components within the architecture. The five components are (1) goaldirected learning (GDL), a decisiontheoretic method for selecting learning goals; (2) probabilistic bias evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals; (3) uniquely predictive theories (UPTs) and probability computation using independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories; (4) a probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories; and (5) a decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA's initial learning goal is just An autonomous agent must be able to select biases (Mitchell 1980) for new learning tasks as they arise. PBE uses probabilistic background knowledge and a model of the system's expected learning performance to compute the expected value of learning biases for each learning goal. The resulting expected discounted future accuracy is used as the expected value of the bias.

When robots come for our jobs, the first people to fall will be those working in retail and fast food restaurants as well as the ubiquitous secretaries who are an indispensable part of the corporate world. It may not happen overnight but slowly, machines are gaining on man's turf and in a decade or two, about 50% of jobs in existence today will have gone the way of dinosaurs, or in this case, automation, according to Henrik Lindberg, chief technology officer at Swedish fintech company Zimpler. Using data from a comprehensive employment report from University of Oxford, Lindberg drew up a monochrome chart, reproduced by Visual Capitalist, that illustrates a society that is increasingly relying on robots. "As computers get better at, for example, perception--think self-driving car--those services jobs are likely next up to be replaced by machines," said Jeff Desjardins, an editor of Visual Capitalist. In the chart above, the black field shows jobs that will disappear with automation while the white represents those that are projected to survive.

Jane Wang started out as an applied physicist modeling the complex network dynamics of memory systems in the brain before moving into experimental cognitive neuroscience as a postdoc at Northwestern. Since joining DeepMind two years ago, her non-machine learning background has equipped her with a unique set of tools and perspectives for tackling the hardest AI problems. "It's exhilarating to formulate theories of human brain function as powerful deep reinforcement learning models that can solve similarly complex tasks," she shares. Though Wang has been successful without a formal AI background, she's concerned the steep learning curve and hypercompetitive atmosphere of AI research can discourage diverse participation. "Although competitiveness drives the field forward, it also discourages those who wish to work in more inclusive, cooperative environments," she warns.

Hey, you know that I'm big supporter of women in tech. I'm actually partnering with Women Who Tech for the 4th Women Startup Challenge at Google and focused on women-led ventures in artificial intelligence (AI), augmented reality (AR), and virtual reality (VR). Note: if you haven't applied you have until December 12th to get your applications in. The winner will receive $50,000 as a cash grant, and $15,000 in probono legal services by global law firm Paul Hastings, LLP. All finalists will join join Samsung for a special startup showcase and reception featuring the 10 finalists.

The results of Tuesday's US election shocked many--including pollsters and campaign insiders. As a result, many have begun questioning the data and methods behind predictions, wondering what went wrong. But not everyone got it wrong: An AI tool created by an Indian startup in Mumbai in 2004 has correctly predicted the last three US elections, including this one. By collecting and analyzing 20 million social media data points, MogIA, developed by Sanjiv Rai, has used sentiment to determine political outcomes. And social media has proven to have a powerful impact on candidates' popularity.

With Google DeepMind's recent success in mastering the game of Go, Tesla's advances in autonomous driving capabilities, and voice recognition systems like Amazon's Alexa taking off, interest in AI and machine learning have reached an all-time high. Those living in the AI world in the 1980s remember what has been referred to as an "AI winter"--a time when the inflated expectations resulted in a "crash," and funding began to dry up. While it's unlikely that the current enthusiasm in AI will wane, some worry that huge attention, and expectations, about AI could have negative side effects. Some also worry about how AI is equated with machine learning--or even, more specifically, deep learning, which is a narrow subset of AI. So, what happened in the '80s?