Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from large set of previously inapplicable resources such as internet videos. In this paper, we propose both a general framework for IfO approaches and propose a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We demonstrate that this approach performs comparably to classical imitation learning approaches (which have access to the demonstrator's actions) and significantly outperforms existing imitation from observation methods in high-dimensional simulation environments.

In the previous article, we talked about the nature of human fears in relation to artificial intelligence (AI). We also outlined two important themes to address in the upcoming challenges of the AI era: the societal, and decision-making aspects of AI systems. In our new article I expand on the theme of the impact on society, and I share our opinion on possible steps we should take to anticipate and adjust to it. McKinsey analysts estimate the automation potential for all economic sectors to be around 50%. This means that around half of all the activities people in the world's workforce are paid to do today could potentially be automated with currently available technologies.

Blackboard, chalks, textbooks and ink pens are slowly becoming a thing of a past. It is time to embrace technology where digital facilities coupled with tech-savvy teachers are enriching students' learning experiences. A little glimpse into the dynamic digital world is indicative of how technology has given a whole new meaning to education. Education with the help of technology has crossed borders and has opened up a world of opportunities for students. From easy sharing of information to collaboration with the help of email and cloud applications to instant access to learning programs anytime, anywhere -- here is how technology will alter the education sector in 2018.

The education ministry plans to establish a system that allows students at some 50 high schools to take university courses and earn credits, with the aim of developing human resources in view of expected advances in artificial intelligence technologies. Advanced differential equation, data mining and other subjects in the mathematics and science fields will be covered by the planned version of the advanced placement system, which is used in the United States and other countries, according to ministry officials. The ministry is set to choose at least one high school from each of Japan's 47 prefectures over the next decade or so for the program. The planned initiative was included in a report compiled by a ministry panel discussing education policies for coming generations. Through the system, the ministry hopes to allow highly motivated high school students with excellent academic performances to receive even higher levels of education after they advance to university.

Imagine a world where smart pantries sense when you are running out of your favourite food and order more of it, without you lifting a finger. Where intelligent robots roam your supermarket, ever at your service. Where dynamic food pricing changes minute-to-minute depending on the weather outside, or what the store down the road is offering. Amazon workers'refuse' to build tech for US immigration It may sound like a seismic shift in our food retail world, but these technological frontiers are real and the food sector is gearing up in a big way. What is less certain is what impact such changes will have on our health.

Computational tools to support this through proof checking, automatic theorem proving, and computer algebra are well-established, though they require formal, computationally explicit, content as input. However, the existing mathematical literature, particularly informal mathematical dialogues, and expository texts, is opaque to such systems, which cannot currently handle the variety of activities typically involved in producing such knowledge and proofs, such as, for example, exposition and argument that concerns making conjectures, forming concepts, and discussing examples and counterexamples. Our goal is to bridge this gap through devising an expressive modelling language that is closely related to the way mathematics is actually done. Our approach to modelling such content is inspired by the general-purpose argument modelling formalism Inference Anchoring Theory (IAT), introduced by Reed and Budzynska (2010). As its name suggests, IAT anchors logical inferences in discourse. IAT has been applied to mediation (Janier and Reed, 2017), debates (Budzynska et al, 2014b), and to paradoxes in ethotic argumentation (Budzynska, 2013), along with other real-world dialogues (Budzynska et al, 2013).

The Internet contains various categories of materials, some of which can be used as educational resources. These educational resources can be retrieved for content creation for use in Adaptive eLearning Systems (AES). Adaptive eLearning is a branch of eLearning that provides educational materials and resources based on the learners' needs. The adaptive learning system is not limited to delivering materials in a personalised manner to the learner; it also adapts in terms of interaction with learners and maintains learners' preferences. Artificial Intelligence plays a key role in adaptive eLearning by providing a personalised learning experience.

Will you smell the robot in the room? Might documentaries explore the situation of bioprinted human organ transplantations on the black market? Will virtual reality cause a worldwide obesity epidemic? The Medical Futurist shares the weirdest ideas about how healthcare might look a hundred years from now. Let's peek into a dystopic future of healthcare.

I got excellent feedback after taking part in the panel discussion, and since it was not recorded I thought I would write a blog post on some of what I discussed, my response to the core questions and some other thoughts I had on the topic. Artificial Intelligence (AI) is often thought of as being new areas that are currently hard to automate, difficult problems to solve using computers, and ultimately replacing humans jobs. Yet we have been using a form of AI or Machine Learning (ML) since the 1950's as Artificial neural network, later on adopted by businesses in 1970's initially as decision support systems, and later evolving into data mining, business intelligence, analytics & insights, and more recently data science. What has changed is that the sales and marketing teams are now involved, and sometimes even overpromising on what is possible! Yet there has also been an increase in computing power, storage capacity with massive datasets collected from a larger number of sources, and open source data science code, packages and tutorials that are readily available.

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