This Artificial Intelligence for Business course or AI4B for short takes place in the future… Somewhere between 2030 and 2035… Executives taking this course will be helped think of the world 10-15 years into the future. This course is designed to help them integrate into strategy all the emerging technologies, and be aware that their convergence will make the next couple of decades the most disruptive ever. We will cover business scenarios that make use of emerging technologies such as AI, Machine Learning, Natural Language Processing, Computer Vision, Robotics, Drones, Augmented Reality, Virtual Reality, Blockchain, Chatbots, Driverless Systems, and Megacities. The goal is to take a trip into the future so we can figure out what new businesses are worth creating in the present and what steps need to be taken today in your existing businesses so they will thrive in this rapidly evolving environment. Another goal in addition to building your AI-muscle is developing your AI-flexibility.

Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e.g., board games, video games or autonomous vehicles. In such problems, an agent faces a sequential decision-making problem where, at every time step, it observes its state, performs an action, receives a reward and moves to a new state. An RL agent learns by trial and error a good policy (or controller) based on observations and numeric reward feedback on the previously performed action. In this chapter, we present the basic framework of RL and recall the two main families of approaches that have been developed to learn a good policy. The first one, which is value-based, consists in estimating the value of an optimal policy, value from which a policy can be recovered, while the other, called policy search, directly works in a policy space. Actor-critic methods can be seen as a policy search technique where the policy value that is learned guides the policy improvement. Besides, we give an overview of some extensions of the standard RL framework, notably when risk-averse behavior needs to be taken into account or when rewards are not available or not known.

This story was published in partnership with The Moonshot Catalog. In the late 1960s, Nobel Prize-winning economist Herbert Simon posed the following thought exercise: Imagine you are an alien from Mars visiting a college on Earth, and you spend a day observing how professors teach their students. Simon argued that you would describe the process as "outrageous." "If we visited an organization responsible for designing, building and maintaining large bridges, we would expect to find employed there a number of trained and experienced professional engineers, thoroughly educated in mechanics and the other laws of nature that determine whether a bridge will stand or fall," he wrote in a 1967 issue of Education Record. "We find no one with a professional knowledge in the laws of learning, or the techniques for applying them," he wrote. Teaching at colleges is often done without any formal training. Mimicry of others who are equally untrained, instinct, and what feels right tend to provide the guidance. As a result, teaching is, to use another building metaphor, not up to code. There are widespread beliefs about the best way to teach and learn that have been proven wrong by science, yet they persist. Reading back over a textbook or taking lecture notes with a highlighter at the ready is often done by students, for instance, but these practices have proven of limited merit, and in some cases even counterproductive in aiding recall.

Stanford's Machine Learning course taught by Andrew Ng was released in 2011. This has become a staple course of Coursera and, to be honest, in machine learning. As of this article, it has had 2,632,122 users enroll in the course. That is just enrolled in, but unknown if they have finished. It is estimated that 1% – 15% of users who start complete the course.

Toshiba Corp. has developed an artificial intelligence-based system to provide real-time video subtitles during online classes. The system transcribes teachers' speeches into subtitles, allowing students to quickly check parts they missed and review lessons afterward. Amid the new coronavirus outbreak, many universities and other educational institutions have introduced online education. Creating an environment to help students' understanding in online classes has become an important challenge. Toshiba, a major Japanese electronics and machinery maker, will conduct system verification tests at Keio University and Hosei University with the aim of putting the system into practical use in a year at the earliest.

A team of global experts compiled this list of best reinforcement courses, classes, tutorials, training, and certification programs available online. This list includes both free and paid courses to help you learn reinforcement learning. Also, it is ideal for beginners, intermediates, and experts. Offered by the University of Alberta, this reinforcement learning specialization program consists of four different courses that will help you explore the power of adaptive learning systems and artificial intelligence. In this program, you will learn how reinforcement learning solutions can help you solve real-world problems via trial-and-error interaction by implementing a complete RL solution from beginning to end.

The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in context-agnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and personalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality-specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.

This USER Workshop was convened with the goal of defining future research directions for the burgeoning intelligent agent research community and to communicate them to the National Science Foundation. It took place in Pittsburgh Pennsylvania on October 24 and 25, 2019 and was sponsored by National Science Foundation Grant Number IIS-1934222. Any opinions, findings and conclusions or future directions expressed in this document are those of the authors and do not necessarily reflect the views of the National Science Foundation. The 27 participants presented their individual research interests and their personal research goals. In the breakout sessions that followed, the participants defined the main research areas within the domain of intelligent agents and they discussed the major future directions that the research in each area of this domain should take.

This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management. Our Natural Language Expression package allows seamless integration of pattern matching, custom NLP modules, and database querying, that makes the workflows much more efficient. As a user study, we adopt this framework to an interdisciplinary undergraduate course where students with both technical and non-technical backgrounds are able to develop creative dialogue managers in a short period of time.

London (CNN Business)IBM is canceling its facial recognition programs and calling for an urgent public debate on whether the technology should be used in law enforcement. In a letter to Congress on Monday, IBM (IBM) CEO Arvind Krishna said the company wants to work with lawmakers to advance justice and racial equity through police reform, educational opportunities and the responsible use of technology. "We believe now is the time to begin a national dialogue on whether and how facial recognition technology should be employed by domestic law enforcement agencies," he said, noting that the company no longer offers general purpose facial recognition or analysis software. "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values," he added. Krishna is of Indian origin and IBM's first CEO of color.