Finland knows it doesn't have the resources to compete with China or the United States for artificial intelligence supremacy, so it's trying to outsmart them. "People are comparing this to electricity – it touches every single sector of human life," says Nokia chairman Risto Siilasmaa. From its foundations as a pulp mill 153 years ago, Nokia is now one of the companies helping to drive a very quiet, very Finnish AI revolution. Last May, the small Scandinavian country announced the launch of Elements of AI, a first-of-its-kind online course that forms part of an ambitious plan to turn Finland into an AI powerhouse. To date, more than 130,000 people have completed the course.

On Feb. 14, 2018, a gunman killed 17 people at Marjory Stoneman Douglas High School in Parkland, Florida. The incident was the deadliest high school shooting in U.S. history, and in the year since, various tech companies across the nation have ramped up efforts to use artificial intelligence to prevent similar tragedies -- and they claim the systems are flagging many violent incidents before they happen. A new story by USA Today details several of the companies offering services that use AI to prevent school shootings. Bark's AI monitors students' text messages, emails, and social media accounts for signs of cyberbullying, drug use, depression, and other possible safety concerns, sending automatic alerts to officials in more than 1,100 school districts when it notes something suspicious.

A human brain is capable of continual learning by nature; however the current mainstream deep neural networks suffer from a phenomenon named catastrophic forgetting (i.e., learning a new set of patterns suddenly and completely would result in fully forgetting what has already been learned). In this paper we propose a generic learning model, which regards a learning system as a fiber bundle. By comparing the learning performance of our model with conventional ones whose neural networks are multilayer perceptrons through a variety of machine-learning experiments, we found our proposed model not only enjoys a distinguished capability of continual learning but also bears a high information capacity. In addition, we found in some learning scenarios the learning performance can be further enhanced by making the learning time-aware to mimic the episodic memory in human brain. Last but not least, we found that the properties of forgetting in our model correspond well to those of human memory. This work may shed light on how a human brain learns.

Abstract--The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantages inanalyzing large-scale networks. However, most existing NRL methods are designed to preserve the local topology of a network, they fail to capture the global topology. To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network. Then, an existing NRL method is used to learn node embeddings for each compressed network. Finally, the node embeddings of the input network are obtained by concatenating the node embeddings from all compressed networks. Empirical studies for link prediction on five real-world datasets demonstrate the advantages of HSRL over state-of-the-art methods. I. INTRODUCTION The science of networks has been widely used to understand thebehaviours of complex systems.

Deep reinforcement learning has seen great success across a breadth of tasks such as in game playing and robotic manipulation. However, the modern practice of attempting to learn tabula rasa disregards the logical structure of many domains and the wealth of readily-available human domain experts' knowledge that could help ``warm start'' the learning process. Further, learning from demonstration techniques are not yet sufficient to infer this knowledge through sampling-based mechanisms in large state and action spaces, or require immense amounts of data. We present a new reinforcement learning architecture that can encode expert knowledge, in the form of propositional logic, directly into a neural, tree-like structure of fuzzy propositions that are amenable to gradient descent. We show that our novel architecture is able to outperform reinforcement and imitation learning techniques across an array of canonical challenge problems for artificial intelligence.

Beginning with the seminal work of Hannan [1957], researchers have been interested in algorithms that use random perturbations to generate a distribution over available actions. Kalai and Vempala [2005] showed that the perturbation idealeads to efficient algorithms for many online learning problems with large action sets. Due to the Gumbel lemma [Hazan et al., 2017], the well known exponential weights algorithm [Freund and Schapire, 1997] also has an interpretation as a perturbation based algorithm that uses Gumbel distributed perturbations. There have been several attempts to analyze the regret of perturbation based algorithms with specific distributions such as Uniform, Double-exponential, dropout and random walk (see, e.g., [Kalai and Vempala, 2005, Kujala and Elomaa, 2005, Devroye et al., 2013, Van Erven et al., 2014]). These works provided rigorous guarantees but the techniques they used did not generalize to general perturbations.

The first time I mentioned chatbots (or bot-based technology) on this blog was back in 2016 in a post titled "Messaging is the Past, Present, and Future." There are a lot of non-HE chatbots in operation at the moment. For example, the Transport for London TravelBot within Facebook Messenger is daily go-to for anyone who uses the Tube. And the chatbots from Duolingo are a great way to practice learning a new language. However, as with a lot of buzzword-driven technology (and this isn't a bad thing), chatbots are increasingly becoming part of the mobile-app landscape for higher education.

Online education in India is unfortunately underrated. Especially when it is touted as the future of education in our country. India's online education market is set to grow to $1.96 billion and almost 9.6 million users by 2021 from $247 million and around 1.6 million users in 2016. This year, it is believed that big data, machine learning, and data science will drive some of the top job opportunities in the country. There has already been a rapid advancement in the digital space that has led to a surge in demand for professionals skilled in the above-mentioned fields.

From introductory gen-ed classes to advanced graduate seminars, wherever classes online or on campus include more than a couple of students, we have struggled with finding ways to assure that all students are given personalized attention to meet their learning needs. This has led to differentiated learning models in which students are presented materials based on assessments conducted prior to the class. But that approach too often fails to adapt to progress during the semester and misses opportunities for exchanges and synergies among all learners. It is also most practical only when there are enough classes to support multiple sections at the differentiated levels or multiple groups within a single class. As expert systems and AI technologies have developed, the promise of personalized learning is now being tested.

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.