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.

You've been taking MOOCs and reading a bunch of textbooks, but now what do you do? Getting a job in data science can seem daunting. The best way to showcase your skills is with a portfolio. This shows employers that you can use the skills you've been learning. Data scientists can expect to spend up to 80% of the time on a new project cleaning data.

Suppose you could merely imagine a computation, and a digital prostheses, an extension of your biological brain, would turn it into code that instantly realizes what you had in mind. Imagine looking at an image, dataset or set of equations and wanting to analyze and explore its meaning as an artistic whim or part of a scientific investigation. I don't mean you would use an existing software suite to produce a standard visualization, but rather you would make use of an extensive repository of existing code to assemble a new program analogous to how a composer draws upon a repertoire of musical motifs, themes and styles to construct new works, and tantamount to having a talented musical amanuensis who, in addition to copying your scores, takes liberties with your prior work, making small alterations here and there and occasionally adding new works of its own invention, novel but consistent with your taste and sensibilities. Perhaps the interaction would be wordless and you would express your objective by simply focusing your attention and guiding your imagination, the prostheses operating directly on patterns of activation arising in your primary sensory, proprioceptive and associative cortex that have become part of an extensive vocabulary that you now share with your personal digital amanuensis. Or perhaps it would involve a conversation conducted in subvocal, unarticulated speech in which you specify what it is you want to compute and your assistant asks questions to clarify your intention and the two of you share examples of input and output to ground your internal conversation in concrete terms. More than thirty years ago, Charles Rich and Richard Waters published an MIT AI Lab technical report [68] entitled The Programmer's Apprentice: A Research Overview. Whether they intended it or not, it would have been easy in those days for someone to misremember the title and inadvertently refer to it as "The Sorcerer's Apprentice" since computer programmers at the time were often characterized as wizards and most children were familiar with the Walt Disney movie Fantasia, featuring music written by Paul Dukas inspired by Goethe's poem of the same name

Despite the success of neural networks (NNs), there is still a concern among many over their "black box" nature. Why do they work? Here we present a simple analytic argument that NNs are in fact essentially polynomial regression models. This view will have various implications for NNs, e.g. providing an explanation for why convergence problems arise in NNs, and it gives rough guidance on avoiding overfitting. In addition, we use this phenomenon to predict and confirm a multicollinearity property of NNs not previously reported in the literature. Most importantly, given this loose correspondence, one may choose to routinely use polynomial models instead of NNs, thus avoiding some major problems of the latter, such as having to set many tuning parameters and dealing with convergence issues. We present a number of empirical results; in each case, the accuracy of the polynomial approach matches or exceeds that of NN approaches. A many-featured, open-source software package, polyreg, is available.

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.

One of the top universities in China, Tsinghua University, established an institute of Artificial Intelligence (AI) on Thursday, June 28. The newly-established institute, according to a report by Chinese news outlet Netease Technology, aims to make fundamental innovations in both theories and key technologies of AI, and broaden the influence of Tsinghua University. Professor Zhang Bo from the school's Department of Computer Science and Technology, who is also an academician of the Chinese Academy of Sciences, will be the head of the institute. Chinese computer scientist Andrew Chi-Chih Yao, who won the most prestigious award in computer science -- the Turing Award, in 2000, will be the director of the institute's academic committee. Additionally Jeffrey Dean, head of Google.ai--Google's

Ngee Ann Polytechnic and Centre for Finance, Technology and Entrepreneurship (CFTE) are about to launch AI in Finance (AIF), the first online programme for finance professionals. Despite the growing hype around Artificial Intelligence (AI), many finance professionals are still unfamiliar with the impact it will have on their industry. Ngee Ann Polytechnic (NP), one of Singapore's leading institutes of higher learning, is partnering with London-based Centre for Finance, Technology and Entrepreneurship (CFTE) to launch the first online course to showcase AI applications and use cases in the banking industry. "AI is a technological driving force that no industry can ignore. Some studies estimate that about 50 per cent of today's tasks would be assisted by AI in the next 20 years. With Singapore and London gaining recognition as leading fintech hubs of the world, it is timely for NP and CFTE to launch an industry-led course that provides finance professionals and others a practical guide to AI." "You will also see that a lot more can be automated in future. If you want to keep your job, you need to question both what your role will be in this automated future, and what impact artificial intelligence will have on your area of the business."

On the windy afternoon of March 17, 2017, I opened my mailbox and saw a white envelope from the San Francisco Unified School District. The envelope contained a letter assigning my younger daughter to a middle school. This letter was a big deal; San Francisco's public schools range from excellent to among the worst in the state, and kids are assigned to them through a lottery. The last time we put her name into the lottery, for kindergarten, she was assigned to one of the lowest-performing schools in California. Then we got a break: A private school offered a big discount on tuition.