Change is the only constant. And it is technology that is at the forefront of driving change at the modern day workplace. Although machine learning seems like a recent buzzword in technology, did you know it was first coined in 1959 at IBM? At that time, machine learning was defined as a way to give "computers the ability to learn without being explicitly programmed." Nowadays its potential is understood by "watching the performance of a computer program. As it iterates, one can quantify its improvement, and state whether the system is learning or not."

I was recently invited to speak at the TiECON Florida conference in Tampa Bay. I came back amazed and fascinated by the entrepreneurial activity and enthusiasm of the community in Southern Florida. Unbeknownst to me, University of South Florida (USF) is amongst the nation's Top 30 for research expenditures among public universities, according to the National Science Foundation. It has a vast amount of intellectual capital, ranking 19th out of 200 U.S. universities in research commercialization with 125 patents granted for 2017. It is also the home of USF Connect, a technology incubator that unite technology and talent with businesses.

The series opens with "Intro to Drone Pilot Training" on July 14 at the college's Fort Omaha Campus. Students will be introduced to rules and regulations needed to fly drones and will finish the course by navigating drones through an indoor obstacle course.

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The nonprofit MathandCoding, a San Francisco Bay Area-based organization that teaches coding to kids, June 11 announced it has expanded its offering to include machine learning and engineering workshops to its lessons. The organization as a whole has grown since being founded by three high school students to hold lessons at more than 25 libraries and community centers throughout the area. The success has led to co-founder Anika Cheerla saying the organization has ventured into physics, engineering and machine learning. MathandCoding started machine learning and AI for Girls initiative about a year ago to encourage middle and high school girls to learn machine learning and artificial intelligence, which Cheerla said are the future technologies sweeping all facets of life. "It was received with a lot of enthusiasm," the Indian American said in an email to India-West of the workshop, where students learn to use open databases to create models and do predications.

Topic discovery has witnessed a significant growth as a field of data mining at large. In particular, time-evolving topic discovery, where the evolution of a topic is taken into account has been instrumental in understanding the historical context of an emerging topic in a dynamic corpus. Traditionally, time-evolving topic discovery has focused on this notion of time. However, especially in settings where content is contributed by a community or a crowd, an orthogonal notion of time is the one that pertains to the level of expertise of the content creator: the more experienced the creator, the more advanced the topic. In this paper, we propose a novel time-evolving topic discovery method which, in addition to the extracted topics, is able to identify the evolution of that topic over time, as well as the level of difficulty of that topic, as it is inferred by the level of expertise of its main contributors. Our method is based on a novel formulation of Constrained Coupled Matrix-Tensor Factorization, which adopts constraints well-motivated for, and, as we demonstrate, are essential for high-quality topic discovery. We qualitatively evaluate our approach using real data from the Physics and also Programming Stack Exchange forum, and we were able to identify topics of varying levels of difficulty which can be linked to external events, such as the announcement of gravitational waves by the LIGO lab in Physics forum. We provide a quantitative evaluation of our method by conducting a user study where experts were asked to judge the coherence and quality of the extracted topics. Finally, our proposed method has implications for automatic curriculum design using the extracted topics, where the notion of the level of difficulty is necessary for the proper modeling of prerequisites and advanced concepts.

Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-tuning many hyper-parameters. Quasi-Newton approaches based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update typically do not require manually tuning hyper-parameters but suffer from approximating a potentially indefinite Hessian with a positive-definite matrix. Hessian-free methods leverage the ability to perform Hessian-vector multiplication without needing the entire Hessian matrix, but each iteration's complexity is significantly greater than quasi-Newton methods. In this paper we propose an alternative approach for solving ML problems based on a quasi-Newton trust-region framework for solving large-scale optimization problems that allow for indefinite Hessian approximations. Numerical experiments on a standard testing data set show that with a fixed computational time budget, the proposed methods achieve better results than the traditional limited-memory BFGS and the Hessian-free methods.

Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically, relying on past students' data. In particular, the Hint Factory (Barnes & Stamper, 2008) recommends edits that are most likely to guide students from their current state towards a correct solution, based on what successful students in the past have done in the same situation. Still, the Hint Factory relies on student data being available for any state a student might visit while solving the task, which is not the case for some learning tasks, such as open-ended programming tasks. In this contribution we provide a mathematical framework for edit-based hint policies and, based on this theory, propose a novel hint policy to provide edit hints in vast and sparsely populated state spaces. In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states. Because the space of possible weighted averages is continuous, we call this approach the Continuous Hint Factory. In our experimental evaluation, we demonstrate that the Continuous Hint Factory can predict more accurately what capable students would do compared to existing prediction schemes on two learning tasks, especially in an open-ended programming task, and that the Continuous Hint Factory is comparable to existing hint policies at reproducing tutor hints on a simple UML diagram task.

Interpretability of the underlying AI representations is a key raison d'\^{e}tre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of learners' cognition and emotions for the purpose of supporting human learning and teaching. Over thirty years of research in ITS (also known as AI in Education) produced important work, which informs about how AI can be used in Education to best effects and, through the OLM research, what are the necessary considerations to make it interpretable and explainable for the benefit of learning. We argue that this work can provide a valuable starting point for a framework of interpretable AI, and as such is of relevance to the application of both knowledge-based and machine learning systems in other high-stakes contexts, beyond education.

According to a new market research report "AI in Education Market by Technology (Deep Learning and ML, NLP), Application (Virtual Facilitators and Learning Environments, ITS, CDS, Fraud and Risk Management), Component (Solutions, Services), Deployment, End-User, and Region - Global Forecast to 2023", published by MarketsandMarkets, the global market to grow from $537.3 Million in 2018 to $3,683.5 Million by 2023, at a Compound Annual Growth Rate (CAGR) of 47.0% during the forecast period. The AI technology is playing a crucial role in enhancing and improving teachers' and students' knowledge. Additionally, the increasing adoption of the AI technology for various applications in the education sector and growing need for multilingual translators integrated with the AI technology are expected to drive the growth of the AI in education market. The Natural Language Processing (NLP) technology segment is expected to grow at a higher CAGR during the forecast period. In the education sector, the Natural Language Processing (NLP) technology is playing a crucial role to synthesize the educational data for generating the final output.