Robin Murphy (featured in 2013), is the Raytheon Professor of Computer Science and Engineering in Texas A & M and Director of the non-profit Humanitarian Robotics and AI Laboratory, (formerly known as Center for Robot-Assisted Search and Rescue (CRASAR). She is a distinguished Disaster Roboticist pioneering the advancement of AI and mobile robotics in unstructured and extreme environments. At CRASAR, she has been actively supplying her rescue robot since 9/11 in 2001 and has now participated in more than 30 disasters which include building collapses, earthquakes, floods, hurricanes, marine mass casualty events, nuclear accidents, tsunamis, underground mine explosions, and volcanic eruptions, in five different countries. And she has developed and taught classes in robotics for emergency response and public safety for over 1,000 members of 30 agencies from seven countries.

Distributed Training on Amazon SageMaker delivers new capabilities that can train large models up to two times faster than would otherwise be possible with today's machine learning processors Inc. company, announced nine new capabilities for its industry-leading machine learning service, Amazon SageMaker, making it even easier for developers to automate and scale all steps of the end-to-end machine learning workflow. Today's announcements bring together powerful new capabilities like faster data preparation, a purpose-built repository for prepared data, workflow automation, greater transparency into training data to mitigate bias and explain predictions, distributed training capabilities to train large models up to two times faster, and model monitoring on edge devices. Machine learning is becoming more mainstream, but it is still evolving at a rapid clip. With all the attention machine learning has received, it seems like it should be simple to create machine learning models, but it isn't. In order to create a model, developers need to start with the highly manual process of preparing the data.

Sensory feedback is essential for the control of soft robotic systems and to enable deployment in a variety of different tasks. Proprioception refers to sensing the robot's own state and is of crucial importance in order to deploy soft robotic systems outside of laboratory environments, i.e. where no external sensing, such as motion capture systems, is available. A vision-based sensing approach for a soft robotic arm made from fabric is presented, leveraging the high-resolution sensory feedback provided by cameras. No mechanical interaction between the sensor and the soft structure is required and consequently, the compliance of the soft system is preserved. The integration of a camera into an inflatable, fabric-based bellow actuator is discussed. Three actuators, each featuring an integrated camera, are used to control the spherical robotic arm and simultaneously provide sensory feedback of the two rotational degrees of freedom. A convolutional neural network architecture predicts the two angles describing the robot's orientation from the camera images. Ground truth data is provided by a motion capture system during the training phase of the supervised learning approach and its evaluation thereafter. The camera-based sensing approach is able to provide estimates of the orientation in real-time with an accuracy of about one degree. The reliability of the sensing approach is demonstrated by using the sensory feedback to control the orientation of the robotic arm in closed-loop.

Sundance Multiprocessor Technology has joined Digital Catapult's Machine Intelligence Garage business incubator, in a move that will help to deepen its expertise in the deployment of AI (artificial intelligence) and ML (machine learning) techniques across a diverse range of embedded systems applications. In addition to Sundance's embedded platforms optimised for running deep learning algorithms used for performing autonomous navigation and other computer vision applications, these companies are working on a range of applications that include video analytics for improved livestock welfare management, solutions for reducing greenhouse emissions, interactive podcasting and neural networking. Digital Catapult is the UK's advanced digital technology innovation centre and connects start-up and scaleup companies with large businesses, investors, government and public organisations, and research and academia. Its Machine Intelligence Garage aims to provide support in the AI/ML arena as well as provide access to the compute-intensive power needed by these enterprises to develop and test their models. It is delivered as part of London's CAP-AI project and is part funded through the European Regional Development Fund. "We started the Machine Intelligence Garage to address the challenges the UK's promising early stage AI and ML companies face, accelerating their growth and helping them realise their true potential by providing access to high-level computational power, relevant expertise, mentoring and networking opportunities," said Jeremy Silver, CEO of Digital Catapult.

This article contains a proposal to add coinduction to the computational apparatus of natural language understanding. This, we argue, will provide a basis for more realistic, computationally sound, and scalable models of natural language dialogue, syntax and semantics. Given that the bottom up, inductively constructed, semantic and syntactic structures are brittle, and seemingly incapable of adequately representing the meaning of longer sentences or realistic dialogues, natural language understanding is in need of a new foundation. Coinduction, which uses top down constraints, has been successfully used in the design of operating systems and programming languages. Moreover, implicitly it has been present in text mining, machine translation, and in some attempts to model intensionality and modalities, which provides evidence that it works. This article shows high level formalizations of some of such uses. Since coinduction and induction can coexist, they can provide a common language and a conceptual model for research in natural language understanding. In particular, such an opportunity seems to be emerging in research on compositionality. This article shows several examples of the joint appearance of induction and coinduction in natural language processing. We argue that the known individual limitations of induction and coinduction can be overcome in empirical settings by a combination of the the two methods. We see an open problem in providing a theory of their joint use.

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.

A new machine learning approach offers important insights into catalysis, a fundamental process that makes it possible to reduce the emission of toxic exhaust gases or produce essential materials like fabric. In a report published in Nature Communications, Hongliang Xin, associate professor of chemical engineering at Virginia Tech, and his team of researchers developed a Bayesian learning model of chemisorption, or Bayeschem for short, aiming to use artificial intelligence to unlock the nature of chemical bonding at catalyst surfaces. "It all comes down to how catalysts bind with molecules," said Xin. "The interaction has to be strong enough to break some chemical bonds at reasonably low temperatures, but not too strong that catalysts would be poisoned by reaction intermediates. This rule is known as the Sabatier principle in catalysis." Understanding how catalysts interact with different intermediates and determining how to control their bond strengths so that they are within that'goldilocks zone' is the key to designing efficient catalytic processes, Xin said. The research provides a tool for that purpose.

The U.S. property market has proven to be more resilient than you might have assumed it would be in the midst of a coronavirus pandemic, and today a startup that's built a computer vision tool to help owners assess and fix those properties more easily is announcing a significant round of funding as it sees a surge of growth in usage. Hover -- which has built a platform that uses eight basic smartphone photos to patch together a 3D image of your home that can then be used by contractors, insurance companies and others to assess a repair, price out the job and then order the parts to do the work -- has raised $60 million in new funding. The Series D values the company at $490 million post-money, and significantly, it included a number of strategic investors. Three of the biggest insurance companies in the U.S. -- Travelers, State Farm Ventures and Nationwide -- led the round, with building materials giant Standard Industries, and other unnamed building tech firms, also participating. Past financial backers Menlo Ventures, GV (formerly Google Ventures) and Alsop Louie Partners, as well as new backer Guidewire Software, were also in this round.

This is a directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library. If you have a high-quality tutorial or project to add, please open a PR.

Chemical kinetics consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions for the construction of surrogate models to solve ordinary differential equations (ODEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multiobjective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We probe the limits at which kinetic parameters can be retrieved as a function of knowledge about the chemical system states over time, and assess the robustness of the methodology with respect to statistical noise. This surrogate approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data.