Despite what you hear about artificial intelligence, machines still can't think like a human, but in the last few years they have become capable of learning. And suddenly, our devices have opened their eyes and ears and cars have taken the wheel. Today, artificial intelligence is not as good as you hope and not as bad as you fear, but humanity is accelerating into a future that few can predict. That's why so many people are desperate to meet Kai-Fu Lee, the oracle of AI. Kai-Fu Lee is in there, somewhere, in a selfie scrum at a Beijing Internet Conference.

Cognitive scientists of the embodied cognition tradition have been providing evidence that a large part of our creative reasoning and problemsolving processes are carried out by means of conceptual metaphor and blending, grounded on our bodily experience with the world. In this talk I shall aim at fleshing out a mathematical model that has been proposed in the last decades for expressing and exploring conceptual metaphor and blending with greater precision than has previously been done. In particular, I shall focus on the notion of aptness of a metaphor or blend and on the validity of metaphorical entailment. Towards this end, I shall use a generalisation of the category-theoretic notion of colimit for modelling conceptual metaphor and blending in combination with the idea of reasoning at a distance as modelled in the Barwise-Seligman theory of information flow. I shall illustrate the adequacy of the proposed model with an example of creative reasoning about space and time for solving a classical brainteaser. Furthermore, I shall argue for the potential applicability of such mathematical model for ontology engineering, computational creativity, and problem-solving in general.

This is a not-particularly-systematic attempt to curate a handful of my favorite resources for learning statistics and machine learning. This isn't meant to be comprehensive, and in fact is still missing the vast majority of my favorite explainers. Rather, it's just a smattering of resources I've found myself turning to multiple times and thus would like to have in one place. Finally, I've added a section with links to a few miscellaneous websites that often produce great content. Of the above, the second section is both the most incomplete and the one that I am most excited about.

Every year the technology industry gathers in Las Vegas for the Consumer Electronics Show (CES), an event that often sets the agenda for the coming 12 months. This is what CES 2019 taught us. The first 5G networks are expected to begin rolling out this year, and so the next-generation connectivity technology was being mentioned everywhere at CES. Intel, Qualcomm and Samsung all spoke about harnessing the technology to not just offer faster mobile internet speeds, but also to connect more devices and appliances to each other and be able to handle more data in the process. Experts at the show also commented on the higher capacity of 5G networks being able to support the software needed to power networks of driverless cars and robots. The halls of this year's CES hinted at a world where homes, cars and even entire cities are connected to one another, with people able to use these connections to complete tasks every day.

Self-driving cars, have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward, and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.

Another potential use for AI on campus is streamlining the admissions process by making accurate forecasts and predictions. For example, colleges and universities need to be able to accurately estimate how many accepted students will enroll in each upcoming term. If institutions are not able to make accurate predictions, both over- and under-enrollment can cause serious problems. Over-enrollment requires the institution to accommodate more students than it has resources for, resulting in a reduction in quality for the students who choose to attend. Under-enrollment threatens the long-term stability of the university through the loss of potential gains.

It's easy to see the impressive rise in popularity for "machine learning" but most IT people and executives often have trouble identifying where their business might actually apply machine learning (ML) or Deep LEarning (DL) to business problems. Market leaders are using Artificial Intelligence for data analytics, predictions, targeted recommendations, and even HR. The question is, how can AI benefit your business? Well, answering this question is the main objective of this course: learn what Machine Learning is and how to use it in advantage of your business. That way, you can take advantage of this tremendous opportunity and become a successful ML entrepreneur.

Until recently, the closest I've come to understanding artificial intelligence is knowing that it powered tools in my martech stack (e.g., marketing automation, predictive lead scoring, etc.). Beyond that, I found the concept hard to grasp until Chris Penn's presentation at Content Marketing World, How to Use AI to Boost Your Content Marketing Impact. Chris, co-founder and chief innovator at Trust Insights, covered several real-world applications of AI. His examples helped transform abstract concepts into tangible use cases. Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning.

The quality of a document is affected by various factors, including grammaticality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text --- such as images, font choices, and visual layout --- we propose a joint model that combines the text content with a visual rendering of the document for document quality assessment. Experimental results over two datasets reveal that textual and visual features are complementary, achieving state-of-the-art results.

It goes without saying that artificial intelligence is changing the nature of industries from transportation to finance, and education is no different with the prospect of personalized learning quickly becoming a reality. As more and more of a student's education is experienced through a computer, data on their educational progress can be collected, leading to more personalized learning plans while assisting the teacher in identifying problem areas for students. While artificial intelligence in education might appear unnerving for some, the benefits are too great to ignore. There are few spaces in life that haven't been touched in some form by computer software. Whether it's shopping, dating, or just keeping up with old friends, everything we do seems to be mediated in some form by computers.