We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural networks in active learning experiments where labels are scarce. Furthermore, the model does not require a validation data set for early stopping to control over-fitting. Our model can be viewed as an instance of empirical distribution regression weighted locally by network connectivity. We further motivate the intuitive construction of the model with a Bayesian linear model interpretation where the node features are filtered by an operator related to the graph Laplacian. The method can be easily implemented by adapting off-the-shelf scalable variational inference algorithms for Gaussian processes.

In this paper, we present a robotic navigation algorithm with natural language interfaces, which enables a robot to safely walk through a changing environment with moving persons by following human instructions such as "go to the restaurant and keep away from people". We first classify human instructions into three types: the goal, the constraints, and uninformative phrases. Next, we provide grounding for the extracted goal and constraint items in a dynamic manner along with the navigation process, to deal with the target objects that are too far away for sensor observation and the appearance of moving obstacles like humans. In particular, for a goal phrase (e.g., "go to the restaurant"), we ground it to a location in a predefined semantic map and treat it as a goal for a global motion planner, which plans a collision-free path in the workspace for the robot to follow. For a constraint phrase (e.g., "keep away from people"), we dynamically add the corresponding constraint into a local planner by adjusting the values of a local costmap according to the results returned by the object detection module. The updated costmap is then used to compute a local collision avoidance control for the safe navigation of the robot. By combining natural language processing, motion planning, and computer vision, our developed system is demonstrated to be able to successfully follow natural language navigation instructions to achieve navigation tasks in both simulated and real-world scenarios. Videos are available at https://sites.google.com/view/snhi

I learned a few things from reading an excerpt from Yuval Noah Harari's book, 21 Lessons for the 21st Century, published in the October issue of The Atlantic. One is that it took a Google machine-learning program just four hours to teach itself and master chess, once the pinnacle of centuries of human intellectual effort, easily defeating the top-ranked computer chess engine in the world. Another is that artificial intelligence systems may be inherently anti-democratic and anti-human. New heights of computing power and data processing make it more efficient to centralize systems in authoritarian governments, Harari says, and will render humans increasingly irrelevant. "By 2050," he writes, "a useless class might emerge, the result not only of a shortage of jobs or a lack of relevant education but also of insufficient mental stamina to continue learning new skills."

What is it, where is it now, where is it going? Artificial Intelligence holds significant promise to revolutionise our educational systems, but are our educational systems ready for a revolution? In this article, published in Ireland's Yearbook of Education 2017-2018, Brett Becker explores current advances of AI in education and discusses how AI is likely to affect our education systems in the years ahead. Very few subjects in science and technology today are causing as much excitement, and as much misconception, as Artificial Intelligence (AI). It seems that everyone from Obama to Putin and Bezos to Zuckerburg are commenting on both the possibilities and the problems that AI could bring to humanity.

Cheat sheets for machine learning are plentiful. Quality, concise technical cheat sheets, on the other hand... not so much. A good set of resources covering theoretical machine learning concepts would be invaluable. Shervini Amidi, graduate student at Stanford, and Afshine Amidi, of MIT and Uber, have created just such a set of resources. The VIP cheat sheets, as Shervini and Afshine have dubbed them (Github repo with PDFs available here), are structured around covering key top-level topics in Stanford's CS 229 Machine Learning course, including: You can visit Shervini's CS 229 resource page or the Github repo for more information, or can download the cheat sheets from the direct download links above.

Jeanne Meister believes that HR should take the lead in shepherding artificial intelligence (AI) software into the workplace, transforming the human resources function and enhancing the employee experience. Meister is the founding partner of Future Workplace, a New York City-based HR executive network and research firm dedicated to the future of learning and working. Future Workplace has created an online course featuring Hilton, IBM, Intel, GE and others who are using AI and realizing business benefits across the organization. She sat down with SHRM Online to discuss why and how CHROs and their teams should start learning about and crafting a strategy for how to best leverage artificial intelligence for HR. SHRM Online: Why should HR pioneer the development of AI strategy in their organizations?

Toddlers use the same gestures as chimpanzees and gorillas showing they really are just'tiny apes', claim researchers. One to two year olds use 52 limb and body movements to communicate - nine in ten of which are observed in great apes. This is a crucial stage of development when infants are on the cusp of learning language, say Scottish scientists behind the findings. Toddlers use the same gestures as chimpanzees and gorillas showing they really are just'tiny apes', claim researchers. Senior author Dr Catherine Hobaiter, of the School of Psychology and Neuroscience at St Andrews University, said: 'Wild chimpanzees, gorillas, bonobos and orangutans all use gestures to communicate their day-to-day requests.

On May 16, via a video link, US President Donald Trump "addressed" a conference in Tianjin from Washington and floored the audience with his almost flawless Chinese. Trump highlighted the big leaps made by artificial intelligence or AI, but what impressed the audience more was the US president's tone - his Chinese intonations, inflections and pitch were near perfect. Well, as it transpired, the voice was not really Trump's, after all, but that of an AI-enabled voice technology developed by iFlytek Co Ltd. And, for the record, unlike his granddaughter, Trump hardly knows any Chinese. The iFlytek technology demonstrated its speech synthesis capability - it can produce an unbelievably human-like voice.

Deep learning has created a perfect dichotomy. On the one hand, we have data science practitioners raving about it, and every one and their colleague jumping in to learn and make a career out of this supposedly game-changing technology in analytics. And then there is everyone else wondering what the buzz is all about. With a multitude of analytics technologies projected as the panacea to business' problems, one wonders what this additional'cool thing' is all about. For people on the business side of things, there are no easy avenues to get a simple and intuitive understanding.

I've written a number of times in the past about the growing use of Artificial Intelligence in the drug discovery process, whether it's in terms of identifying molecules for analysis or predicting potential side effects. A recent paper from a team at the University of North Carolina at Chapel Hill Eshelman School of Pharmacy suggests that AI can now go one step further and design new drug molecules from scratch. Their method, which is known as Reinforcement Learning for Structural Evolution (ReLeaSE), consists of two neural networks that the researchers refer to as the teacher and the student. The teacher component of the system understands the syntax and linguistic rules behind the chemical structures of around 1.7 million biologically active molecules. The student component they learn from these in order to propose molecules that can be used in new medicines.