This is an age of artificial intelligence (AI) driven automation and autonomous machines. The increasing ubiquity and rapidly expanding potential of self-improving, self-replicating, autonomous intelligent machines has spurred a massive automation driven transformation of human ecosystems in cyberspace, geospace and space (CGS). As seen across nations, there is already a growing trend towards increasingly entrusting complex decision processes to these rapidly evolving AI systems. From granting parole to diagnosing diseases, college admissions to job interviews, managing trades to granting credits, autonomous vehicles to autonomous weapons, the rapidly evolving AI systems are increasingly being adopted by individuals and entities across nations: its government, industries, organizations and academia (NGIOA). Individually and collectively, the promise and perils of these evolving AI systems are raising serious concerns for the accuracy, fairness, transparency, trust, ethics, privacy and security of the future of humanity -- prompting calls for regulation of artificial intelligence design, development and deployment.

Artificial intelligence (AI) is one of the most hyped terms in the 21st century, and yet one of the most misunderstood. Very often, when talking about AI, we like to automatically couple it with other terms such as Machine Learning, Deep Learning, and Neural Networks. This makes it sound like over 90% of AI is this kind of statistical algorithm that only PhDs can understand. While automated learning and classification algorithms are vital to the development of an artificially intelligent system, they only serve as enablers of true intelligence. These algorithms are necessary to develop intelligence, but not sufficient.

As a disruptive technology, artificial intelligence has the potential to impact every sector and education is no exception. While innovation in AI is still climbing the hype curve, we're already seeing its use cases begin to proliferate. Education Secretary Damian Hinds' call for innovative technologies such as AI to be implemented to relieve pressure on teachers is the latest recognition of the potential benefits which can be gained. By deploying AI and automation solutions to non-teaching tasks such as marking, admin and planning, time will be freed up for the UK's educators to focus on the'human' side of teaching. The increase in hands-on time in the classroom will not only benefit those in education, but also raise teachers' job satisfaction by allowing them to focus on the more fulfilling aspects of the job.

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

We are interested in a framework of online learning with kernels for low-dimensional but large-scale and potentially adversarial datasets. Considering the Gaussian kernel, we study the computational and theoretical performance of online variations of kernel Ridge regression. The resulting algorithm is based on approximations of the Gaussian kernel through Taylor expansion. It achieves for $d$-dimensional inputs a (close to) optimal regret of order $O((\log n)^{d+1})$ with per-round time complexity and space complexity $O((\log n)^{2d})$. This makes the algorithm a suitable choice as soon as $n \gg e^d$ which is likely to happen in a scenario with small dimensional and large-scale dataset.

This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR), and summarises the status quo of applying artificial intelligence (AI) techniques. We first analyse the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu and the curriculum. Then, we transform the relationship model into the perception-planning-action model for exploring what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting human-robot teaching-learning process and AI-assisted educational techniques, illustrating the design guidelines and future research perspectives in intelligent tutoring robots.

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.

We are motivated by large scale submodular optimization problems, where standard algorithms that treat the submodular functions in the \emph{value oracle model} do not scale. In this paper, we present a model called the \emph{precomputational complexity model}, along with a unifying memoization based framework, which looks at the specific form of the given submodular function. A key ingredient in this framework is the notion of a \emph{precomputed statistic}, which is maintained in the course of the algorithms. We show that we can easily integrate this idea into a large class of submodular optimization problems including constrained and unconstrained submodular maximization, minimization, difference of submodular optimization, optimization with submodular constraints and several other related optimization problems. Moreover, memoization can be integrated in both discrete and continuous relaxation flavors of algorithms for these problems. We demonstrate this idea for several commonly occurring submodular functions, and show how the precomputational model provides significant speedups compared to the value oracle model. Finally, we empirically demonstrate this for large scale machine learning problems of data subset selection and summarization.

Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields of statistical science. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). Popular example of scoring rule for continuous outcomes is the continuous ranked probability score (CRPS). We consider the case where several competing methods produce online predictions in the form of probability distribution functions. In this paper, the problem of combining probabilistic forecasts is considered in the prediction with expert advice framework. We show that CRPS is a mixable loss function and then the time independent upper bound for the regret of the Vovk's aggregating algorithm using CRPS as a loss function can be obtained. We present the results of numerical experiments illustrating the proposed methods.

Packet classification is a fundamental problem in computer networking. This problem exposes a hard tradeoff between the computation and state complexity, which makes it particularly challenging. To navigate this tradeoff, existing solutions rely on complex hand-tuned heuristics, which are brittle and hard to optimize. In this paper, we propose a deep reinforcement learning (RL) approach to solve the packet classification problem. There are several characteristics that make this problem a good fit for Deep RL. First, many of the existing solutions are iteratively building a decision tree by splitting nodes in the tree. Second, the effects of these actions (e.g., splitting nodes) can only be evaluated once we are done with building the tree. These two characteristics are naturally captured by the ability of RL to take actions that have sparse and delayed rewards. Third, it is computationally efficient to generate data traces and evaluate decision trees, which alleviate the notoriously high sample complexity problem of Deep RL algorithms. Our solution, NeuroCuts, uses succinct representations to encode state and action space, and efficiently explore candidate decision trees to optimize for a global objective. It produces compact decision trees optimized for a specific set of rules and a given performance metric, such as classification time, memory footprint, or a combination of the two. Evaluation on ClassBench shows that NeuroCuts outperforms existing hand-crafted algorithms in classification time by 18% at the median, and reduces both time and memory footprint by up to 3x.