On a visit to China this summer, I was asked by a local journalist if I thought their country's education and training system was ready for artificial intelligence (AI) and the fourth industrial revolution (4IR). This got me thinking about what it means for any country – let's say the UK – to be AI-ready in educational terms. In this country, there is increasing discussion and debate around the use of technology, including AI, in teaching and learning. Most teachers know the technology exists, but perhaps not necessarily how it can help them in their everyday work. Need to know: What is the fourth industrial revolution?

The G7 Summit is done. It is done for the year after a weekend of personality clashes, fires, uninvited guests and meagre discussions covering issues like inequality, climate change, digital economy, nuclear proliferation and the future of African development. Rejigging the format, host nation France invited democracies that have regional influence, which made India a participant alongside Australia, South Africa and Chile. Indian Prime Minister Narendra Modi addressed the G7 on two key issues - climate change and digital transformations - which testify to India's importance in tackling these issues. For countries like India, whose economy is deeply entwined with globalisation and its effects, G7 talks on matters like digital economy, climate change, oceans and biodiversity and inequality serve as an opportunity to take stock on domestic policies covering these issues and how to retool them.

Hokkaido University launched the Center for Human-Nature, Artificial Intelligence, and Neuroscience, or CHAIN, in July 2019. It will conduct interdisciplinary research and education at the intersection of humanities, artificial intelligence, and neuroscience. The inaugural symposium was held at the university's Sapporo Campus on July 23rd to unravel its vision and ambitious plans for research and graduate-level education. At the symposium, Professor Shigeru Taguchi, the Director of CHAIN, said, "Recent developments in neuroscience and artificial intelligence have made it possible for scientists to tackle problems that have been traditionally explored in humanities, such as consciousness, emotion, and self," explaining the ever-increasing demand for the integration of humanities and science. "We would like to open up new directions in understanding'what human beings are'."

Businesses and consumers are experiencing a gap in perception when it comes to the use of AI. While almost two-thirds (63%) of businesses believe that chatbots and virtual assistants make it easier for customers to get their issues resolved, only one-third (33%) of consumers agree, according to a customer experience benchmark report from NICE inContact. There is an even bigger gap when it comes to businesses believing that customers would like to use their virtual assistants (such as Amazon Alexa/ECHO or Google Home) to interact with them. While 68% of businesses carry this perception – and despite ownership of such devices continuing to increase in the US – only 30% of customers actually want to interact with businesses using them. Even so, businesses are continuing to look to the future when it comes to voice ordering.

While machine learning today is dominated by deep neural network research, in the 1990s neural approaches were not recognized as reliable for real-world applications. Back then, researchers put their efforts into kernel methods and support vector machines (SVM). One of the most notable and respected contributors to kernel methods and SVM is John Shawe-Taylor, a professor at University College London (UK) and Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from neural networks to machine learning and graph theory. Shawe-Taylor has published over 300 papers with over 42000 citations.

Two new studies from MIT suggest that mindfulness -- the practice of focusing one's awareness on the present moment -- can enhance academic performance and mental health in middle schoolers. The researchers found that more mindfulness correlates with better academic performance, fewer suspensions from school, and less stress. "By definition, mindfulness is the ability to focus attention on the present moment, as opposed to being distracted by external things or internal thoughts. If you're focused on the teacher in front of you, or the homework in front of you, that should be good for learning," says John Gabrieli, the Grover M. Hermann Professor in Health Sciences and Technology, a professor of brain and cognitive sciences, and a member of MIT's McGovern Institute for Brain Research. The researchers also showed, for the first time, that mindfulness training can alter brain activity in students.

How artificial intelligence is changing the workplace is starting to get the attention of congressional lawmakers at a time when some employment attorneys are sounding alarms about the need for legislation. The House Education and Labor Committee plans to hold hearings on machine learning's impact on workers and their jobs after Congress returns from recess in September. However, while a hearing is usually a precursor to legislation, employers using AI-based tools and tech companies developing those programs probably don't need to worry about new bills anytime soon. The focus on Capitol Hill remains on trying to understand what the effect of artificial intelligence on workers could be. The rise of algorithms and machine learning technology is already changing the way people work.

In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, wastewater transport, and urbanization are included. In addition to the occurrence of anthropogenic contamination, the presence of some contaminants (e.g., methane, salt, and sulfate) of natural origin is not uncommon. Therefore, scientists sometimes find it difficult to identify the sources of contaminants in the coupled natural and human systems. In this paper, we propose a technique to simultaneously conduct source detection and prediction, which outperforms other approaches in the interdisciplinary case study of the identification of potential groundwater contamination within a region of high-density shale gas development.

Parametric shape optimization aims at minimizing an objective function f(x) where x are CAD parameters. This task is difficult when f is the output of an expensive-to-evaluate numerical simulator and the number of CAD parameters is large. Most often, the set of all considered CAD shapes resides in a manifold of lower effective dimension in which it is preferable to build the surrogate model and perform the optimization. In this work, we uncover the manifold through a high-dimensional shape mapping and build a new coordinate system made of eigenshapes. The surrogate model is learned in the space of eigenshapes: a regularized likelihood maximization provides the most relevant dimensions for the output. The final surrogate model is detailed (anisotropic) with respect to the most sensitive eigenshapes and rough (isotropic) in the remaining dimensions. Last, the optimization is carried out with a focus on the critical dimensions, the remaining ones being coarsely optimized through a random embedding and the manifold being accounted for through a replication strategy. At low budgets, the methodology leads to a more accurate model and a faster optimization than the classical approach of directly working with the CAD parameters.

Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. Steam flood injection, a widely used enhanced oil recovery technique, uses thermal and gravitational potential to mobilize and dilute heavy oil in situ to increase oil production. In contrast to traditional steam flood simulations based on principles of classic physics, we introduce here an approach using cutting-edge machine learning techniques that have the potential to provide a better way to describe the performance of steam flood. We propose a workflow to address a category of time-series data that can be analyzed with supervised machine learning algorithms and IoT. We demonstrate the effectiveness of the technique for forecasting oil production in steam flood scenarios. Moreover, we build an optimization system that recommends an optimal steam allocation plan, and show that it leads to a 3% improvement in oil production. We develop a minimum viable product on a cloud platform that can implement real-time data collection, transfer, and storage, as well as the training and implementation of a cloud-based machine learning model. This workflow also offers an applicable solution to other problems with similar time-series data structures, like predictive maintenance.