Amazon has deployed over 750,000 robots to its fulfillment centers over the last decade or so, but now there's a new, shall we say, more sensitive addition. The company has announced Vulcan, its first robot with a sense of touch. It's one in a series of new robots introduced today at Amazon's Delivering the Future event in Germany. Vulcan uses force feedback sensors to monitor how much it's pushing or holding on to an object and, ideally, not damage it. "In the past, when industrial robots have unexpected contact, they either emergency stop or smash through that contact. They often don't even know they have hit something because they cannot sense it."

Amazon has developed a new warehouse robot that uses touch to rummage around shelves to find the right product to ship to customers. The robot, called Vulcan, is a meaningful step towards making robots less sausage-fingered compared to human beings. Honing robots' tactile abilities further may allow them to take on more fulfillment and manufacturing work in the years ahead. Aaron Parness, Amazon's director of robotics AI who led the development of Vulcan, explains that touch sensing helps the robot push items around on a shelf and identify what it's after. "When you're trying to stow [or pick] items in one of these pods, you can't really do that task without making contact with the other items," he says.

In this era of exoplanet characterisation with JWST, the need for a fast implementation of classical forward models to understand the chemical and physical processes in exoplanet atmospheres is more important than ever. Notably, the time-dependent ordinary differential equations to be solved by chemical kinetics codes are very time-consuming to compute. In this study, we focus on the implementation of neural networks to replace mathematical frameworks in one-dimensional chemical kinetics codes. Using the gravity profile, temperature-pressure profiles, initial mixing ratios, and stellar flux of a sample of hot-Jupiters atmospheres as free parameters, the neural network is built to predict the mixing ratio outputs in steady state. The architecture of the network is composed of individual autoencoders for each input variable to reduce the input dimensionality, which is then used as the input training data for an LSTM-like neural network. Results show that the autoencoders for the mixing ratios, stellar spectra, and pressure profiles are exceedingly successful in encoding and decoding the data. Our results show that in 90% of the cases, the fully trained model is able to predict the evolved mixing ratios of the species in the hot-Jupiter atmosphere simulations. The fully trained model is ~1000 times faster than the simulations done with the forward, chemical kinetics model while making accurate predictions.

Yet "Metal: Hellsinger" is not just about keeping time while slaughtering bloodthirsty fiends; it also offers room for improvisation through its array of weapons. Take Paz, a fire-belching talking skull that essentially functions as your pistol -- a gun that lets you fire shots consistently and rapidly, but deals minimal damage per shot. Unlike other guns in your arsenal, Paz doesn't need reloading, which makes it a perfect weapon for learning to shoot demons to the beat of the game's heavy metal accompaniment. Subsequent levels will unlock more weapons, such as Persephone, the game's version of a shotgun, and Vulcan, a heavy, sluggish crossbow that fires bolts that deal area damage, devastating enemies where the bolts land. Persephone deals infinitely more damage, but is slightly more challenging to wield as it requires some time to reload, and its lower firing rate means you can, at best, only shoot the gun once every two beats; Vulcan has an even lower fire rate.

Steiner Tree Problem (STP) in graphs aims to find a tree of minimum weight in the graph that connects a given set of vertices. It is a classic NP-hard combinatorial optimization problem and has many real-world applications (e.g., VLSI chip design, transportation network planning and wireless sensor networks). Many exact and approximate algorithms have been developed for STP, but they suffer from high computational complexity and weak worst-case solution guarantees, respectively. Heuristic algorithms are also developed. However, each of them requires application domain knowledge to design and is only suitable for specific scenarios. Motivated by the recently reported observation that instances of the same NP-hard combinatorial problem may maintain the same or similar combinatorial structure but mainly differ in their data, we investigate the feasibility and benefits of applying machine learning techniques to solving STP. To this end, we design a novel model Vulcan based on novel graph neural networks and deep reinforcement learning. The core of Vulcan is a novel, compact graph embedding that transforms highdimensional graph structure data (i.e., path-changed information) into a low-dimensional vector representation. Given an STP instance, Vulcan uses this embedding to encode its pathrelated information and sends the encoded graph to a deep reinforcement learning component based on a double deep Q network (DDQN) to find solutions. In addition to STP, Vulcan can also find solutions to a wide range of NP-hard problems (e.g., SAT, MVC and X3C) by reducing them to STP. We implement a prototype of Vulcan and demonstrate its efficacy and efficiency with extensive experiments using real-world and synthetic datasets.

Project Halo is a multistaged effort, sponsored by Vulcan Inc, aimed at creating Digital Aristotle, an application that will encompass much of the world's scientific knowledge and be capable of applying sophisticated problem solving to answer novel questions. Vulcan envisions two primary roles for Digital Aristotle: as a tutor to instruct students in the sciences and as an interdisciplinary research assistant to help scientists in their work. As a first step towards this goal, we have just completed a six-month pilot phase designed to assess the state of the art in applied knowledge representation and reasoning (KR&/R). Vulcan selected three teams, each of which was to formally represent 70 pages from the advanced placement (AP) chemistry syllabus and deliver knowledge-based systems capable of answering questions on that syllabus. The evaluation quantified each system's coverage of the syllabus in terms of its ability to answer novel, previously unseen questions and to provide human- readable answer justifications.

"They were trying to run their operation from that physical board," says Ted Schmitt, principal business development manager for conservation technology at Vulcan, the Seattle-based philanthropic tech company founded by Microsoft cofounder Paul Allen (who died on October 15), which partnered with the park to help it move to the company's digital system, called EarthRanger, in April. "They all know that poaching goes up during a full moon, for obvious reasons," says Schmitt. "But what they don't know, and they all expect, is that there are patterns like that latent in the data that they just can't pull out. That's the promise of machine learning…it's going to let them be proactive." The machine learning is still in early stages of development, but some analytic tools are already in use. A new heat map feature, for example, first tested at Grumeti Game Reserve in Tanzania and Liwonde National Park in Malawi, showed that most incidents were happening near the borders of each park, so rangers could focus on those areas with the highest risk.

Paul Allen has made a name for himself as a co-founder of Microsoft, a supporter of artificial intelligence research and a contributor to causes such as wildlife conservation -- so it only makes sense that the Seattle-area billionaire wants to use machine learning to further his philanthropic goals. His latest contribution comes through the Seattle-based Vulcan Machine Learning Center for Impact, or VMLCI. "Its mission will be to apply the tools of machine learning and AI for good," Bill Hilf, CEO of Paul Allen's Vulcan Inc., said today in a tweet. VMLCI's strategy meshes with the mission of the Allen Institute for Artificial Intelligence, whose motto is "AI for the Common Good." The center aims to forge collaborative partnerships with corporations, academic institutions and other organizations to help connect folks working on social and environmental causes with the machine-learning resources they need.

Chance Constrained Markov Decision Processes maximize reward subject to a bounded probability of failure, and have been frequently applied for planning with potentially dangerous outcomes or unknown environments. Solution algorithms have required strong heuristics or have been limited to relatively small problems with up to millions of states, because the optimal action to take from a given state depends on the probability of failure in the rest of the policy, leading to a coupled problem that is difficult to solve. In this paper we examine a generalization of a CCMDP that trades off probability of failure against reward through a functional relationship. We derive a constraint that can be applied to each state history in a policy individually, and which guarantees that the chance constraint will be satisfied. The approach decouples states in the CCMDP, so that large problems can be solved efficiently. We then introduce Vulcan, which uses our constraint in order to apply Monte Carlo Tree Search to CCMDPs. Vulcan can be applied to problems where it is unfeasible to generate the entire state space, and policies must be returned in an anytime manner. We show that Vulcan and its variants run tens to hundreds of times faster than linear programming methods, and over ten times faster than heuristic based methods, all without the need for a heuristic, and returning solutions with a mean suboptimality on the order of a few percent. Finally, we use Vulcan to solve for a chance constrained policy in a CCMDP with over $10^{13}$ states in 3 minutes.

Artificial Intelligence (AI) has been hailed to be the next saviour of the NHS, with new robotic overlords saving thousands of lives from cancer-related deaths over the next 15 years. AI has suddenly become panacea, solving everything from self-driving cars to detecting fake news - but what changed so fast? On one hand, not much: most of the hype that people are selling as "AI" was only a few years ago termed "machine learning", a change in part due to a previous desire to distance humans from something that was indistinguishable from one. On the other hand, a few specific advancements have aligned, computational power has increased and data is being generated at an unfathomable rate, with more created in the 2017 than in the history of mankind. In other words, it isn't the ideas or the technologies that are new, but rather the opportunities.