In a matter of years, the technology industry has embedded itself in virtually every aspect of modern life, and MBA programs are scrambling to catch up. The demand for business leaders with technical knowhow is not lost on both MBA students and business schools. In short: there is now overwhelming demand for MBA programs that sell the hard stuff in addition to the traditional leadership, management, quantity analysis package. Tech MBAs have increasingly become lucrative propositions for students who want to understand how to employ artificial intelligence (AI), for instance, within the context of a consulting gig. And master's programs are getting involved too. Smith School of Business at Queen's University, Canada, has just launched a Master of Management in Artificial Intelligence (MMAI), the first program of its kind in North America.

At its heart, AI is computer programming that learns and adapts. It can't solve every problem, but its potential to improve our lives is profound. At Google, we use AI to make products more useful--from email that's spam-free and easier to compose, to a digital assistant you can speak to naturally, to photos that pop the fun stuff out for you to enjoy. Beyond our products, we're using AI to help people tackle urgent problems. A pair of high school students are building AI-powered sensors to predict the risk of wildfires.

We consider a basic problem at the interface of two fundamental fields: submodular optimization and online learning. In the online unconstrained submodular maximization (online USM) problem, there is a universe $[n]=\{1,2,...,n\}$ and a sequence of $T$ nonnegative (not necessarily monotone) submodular functions arrive over time. The goal is to design a computationally efficient online algorithm, which chooses a subset of $[n]$ at each time step as a function only of the past, such that the accumulated value of the chosen subsets is as close as possible to the maximum total value of a fixed subset in hindsight. Our main result is a polynomial-time no-$1/2$-regret algorithm for this problem, meaning that for every sequence of nonnegative submodular functions, the algorithm's expected total value is at least $1/2$ times that of the best subset in hindsight, up to an error term sublinear in $T$. The factor of $1/2$ cannot be improved upon by any polynomial-time online algorithm when the submodular functions are presented as value oracles. Previous work on the offline problem implies that picking a subset uniformly at random in each time step achieves zero $1/4$-regret. A byproduct of our techniques is an explicit subroutine for the two-experts problem that has an unusually strong regret guarantee: the total value of its choices is comparable to twice the total value of either expert on rounds it did not pick that expert. This subroutine may be of independent interest.

Dyna is an architecture for reinforcement learning agents that interleaves planning, acting, and learning in an online setting. This architecture aims to make fuller use of limited experience to achieve better performance with fewer environmental interactions. Dyna has been well studied in problems with a tabular representation of states, and has also been extended to some settings with larger state spaces that require function approximation. However, little work has studied Dyna in environments with high-dimensional state spaces like images. In Dyna, the environment model is typically used to generate one-step transitions from selected start states. We applied one-step Dyna to several games from the Arcade Learning Environment and found that the model-based updates offered surprisingly little benefit, even with a perfect model. However, when the model was used to generate longer trajectories of simulated experience, performance improved dramatically. This observation also holds when using a model that is learned from experience; even though the learned model is flawed, it can still be used to accelerate learning.

The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate geometric properties of Complete Intersection Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model building. An advanced neural network classifier and SVM are employed to (1) learn Hodge numbers and report a remarkable improvement over previous efforts, (2) query for favourability, and (3) predict discrete symmetries, a highly imbalanced problem to which the Synthetic Minority Oversampling Technique (SMOTE) is applied to boost performance. In each case study, we employ a genetic algorithm to optimise the hyperparameters of the neural network. We demonstrate that our approach provides quick diagnostic tools capable of shortlisting quasi-realistic string models based on compactification over smooth CICYs and further supports the paradigm that classes of problems in algebraic geometry can be machine learned.

What are some underdeveloped areas in computer science research right now (2018)? Over the past few decades, computer science research, either in industry or academia, has led to ground breaking technology innovations such as the internet, which continues to change our lives. In the post-Moore's Law era, advances in cloud computing affected so many sub-areas of computer science like operating systems and database systems. Furthermore, solid state drives (SSDs) changed the way we design storage systems, which were previously tailored for the mechanical hard drive (HDD). Recently, quantum computing promises lightning-speed calculations as opposed to classic electronics-based computers.

Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. The focus is on practical skills - all the activities will be done in Python 3, a modern programming language used throughout astronomy.

Machine learning and AI are reshaping the industries. Gartner predicts that the global enterprise value derived from AI will total $1.2 trillion in 2018, which is a 70% increase from 2017. By 2022, the number will reach $3.9 trillion [1]. From IBM Watson to Google Alpha Go, enterprises have made great strides in AI research in the last couple of years. While almost all executives believe AI is the key driver of growth and success [2], they are still in the early stage of applying the technology to their businesses.

This is also an excellent book.

This is in furtherance of DSN's drive for accelerated applications of artificial intelligence to solve social and business problems through a strategic partnership between the academic community, industry, and local and international technology hubs similar to the highly successful Stanford University-Silicon Valley model. DSN is committed to supporting Nigerian students and encouraging them to turn their academic research into innovative start-ups or social enterprises aligned with millennial developmental goals. DSN has a particular interest in solving local problems in the fields of health, education, agriculture and financial inclusion. The Hub will be the community centre for data science enthusiasts and experts to fraternize, learn, share ideas and participate in local and international projects. DSN also plans to use the Hub for face-to-face masterclasses, industry meet-ups, and dial-in conferences with its over 100 mentors from across the globe.