Artificial Intelligence, machine learning, and other technologies are changing the world in which we live and work in some subtle, and not-so-subtle, ways. And we're diving into just a few of them in this podcast series produced in association with Darktrace. One of the most visible places where analytics based on AI and machine learning are working their way into our popular awareness is in the realm of professional sports. From the virtual lines drawn on a football field to show the line of scrimmage and first-down markers to Major League Baseball stat casts predicting the probability of successful base stealing, AI has become part of how we consume sports. In this episode, Ars editors Sean Gallagher and Lee Hutchinson talk with Tim Wade, vice president at NTT's Advanced Technology Group, about how NTT provides AI-based analytics for the Tour de France, the iconic 21-stage cycling competition.

Student dropout prediction provides an opportunity to improve student engagement, which maximizes the overall effectiveness of learning experiences. However, researches on student dropout were mainly conducted on school dropout or course dropout, and study session dropout in a mobile learning environment has not been considered thoroughly. In this paper, we investigate the study session dropout prediction problem in a mobile learning environment. First, we define the concept of the study session, study session dropout and study session dropout prediction task in a mobile learning environment. Based on the definitions, we propose a novel Transformer based model for predicting study session dropout, DAS: Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment. DAS has an encoder-decoder structure which is composed of stacked multi-head attention and point-wise feed-forward networks. The deep attentive computations in DAS are capable of capturing complex relations among dynamic student interactions. To the best of our knowledge, this is the first attempt to investigate study session dropout in a mobile learning environment. Empirical evaluations on a large-scale dataset show that DAS achieves the best performance with a significant improvement in area under the receiver operating characteristic curve compared to baseline models.

How we live and the resources we have at our fingertips has created an imperative for organizations to change the way we work. This evolution has a direct impact on when we work, how we work and what technology we use. Robots and high tech may not be invading your workplace, but artificial intelligence (AI) is influencing in ways you may not even recognize. There are disruptive forces making change necessary and in the best interest of every employer and employee. What those changes are and how you can use them to the advantage of your organization should be on every leader's mind.

See the full roster of training sessions and tutorials at the Artificial Intelligence Conference in San Jose, September 9-12, 2019. In our recent surveys AI Adoption in the Enterprise and Machine Learning Adoption in the Enterprise, we found growing interest in AI technologies among companies across a variety of industries and geographic locations. Our findings align with other surveys and studies--in fact, a recent study by the World Intellectual Patent Office (WIPO) found that the surge in research in AI and machine learning (ML) has been accompanied by an even stronger growth in AI-related patent applications. Patents are one sign that companies are beginning to take these technologies very seriously. Get a free trial today and find answers on the fly, or master something new and useful.

Bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset, then combines the predictions from all models. Random forest is an extension of bagging that also randomly selects subsets of features used in each data sample. Both bagging and random forests have proven effective on a wide range of different predictive modeling problems. Although effective, they are not suited to classification problems with a skewed class distribution. Nevertheless, many modifications to the algorithms have been proposed that adapt their behavior and make them better suited to a severe class imbalance. In this tutorial, you will discover how to use bagging and random forest for imbalanced classification.

The issue of diversity in STEM fields is something which has been spoken about for some time, albeit until recently on a smaller scale. Is the issue of diversity, starting to be recognised at a greater scale in STEM fields? Has there been a shift in the increase in vocalising the discussion and creation of movements and organisations with the sole focus of addressing the concerns of those directly affected. Although increasingly spoken about, what are we doing to address diversity and how can we practically address the issue currently present? We asked some of our friends in STEM for their practical solutions.

The future of artificial intelligence (AI) is here: self-driving cars, grocery-delivering drones and voice assistants like Alexa that control more and more of our lives, from the locks on our front doors to the temperatures of our homes. For example, should an autonomous vehicle swerve into a pedestrian or stay its course when facing a collision? These questions plague technology companies as they develop AI at a clip outpacing government regulation, and have led Seattle University to develop a new ethics course for the public. Launched last week, the free, online course for businesses is the first step in a Microsoft-funded initiative to merge ethics and technology education at the Jesuit university. Seattle U senior business-school instructor Nathan Colaner hopes the new course will become a well-known resource for businesses "as they realize that [AI] is changing things," he said.

Link: Artificial Intelligence AI - Simply Explained for Beginners Coupon code / udemy Fundamentals of agent and multi-agent systems, neural networks, deep learning, machine learning & computer vision New by Axel Mammitzsch What you'll learn You will learn to understand the structure and design of modern artificial intelligence systems. You will learn to distinguish between strong and weak AI. You will learn what "Deep Learning" is. You will learn what "Deep Learning" is. What is the structure of a problem.

The decision tree algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. The split points of the tree are chosen to best separate examples into two groups with minimum mixing. When both groups are dominated by examples from one class, the criterion used to select a split point will see good separation, when in fact, the examples from the minority class are being ignored. This problem can be overcome by modifying the criterion used to evaluate split points to take the importance of each class into account, referred to generally as the weighted split-point or weighted decision tree. In this tutorial, you will discover the weighted decision tree for imbalanced classification.

All 1,200 employees of insurer Prudential Singapore will be able to learn how to better use data in their daily work through a new online certification programme. More than 170 staff have signed up for the free course in machine learning since it launched on Feb 1, said Prudential yesterday. Machine learning is a branch of artificial intelligence (AI) that focuses on developing computer programmes that can learn from new data so as to perform tasks without needing explicit instructions from humans. Participants will be trained by data scientists from the public and private sectors, and will take scenario-based assessments to show that they can apply what they have learnt. The 30-hour course is called Machine Learning for Humans and follows a foundational course called AI in Finance, which was taken by over 200 Prudential staff last year.