In industry as well as education as well as academics we see a growing need for knowledge on how to apply machine learning in software applications. With the educational programme ICT & AI at Fontys UAS we had to find an answer to the question: "How should we educate software engineers to become AI engineers?" This paper describes our educational programme, the open source tools we use, and the literature it is based on. After three years of experience, we present our lessons learned for both educational institutions and software engineers in practice.

In this paper, we consider a novel variant of the multi-armed bandit (MAB) problem, \emph{MAB with cost subsidy}, which models many real-life applications where the learning agent has to pay to select an arm and is concerned about optimizing cumulative costs and rewards. We present two applications, \emph{intelligent SMS routing problem} and \emph{ad audience optimization problem} faced by a number of businesses (especially online platforms) and show how our problem uniquely captures key features of these applications. We show that naive generalizations of existing MAB algorithms like Upper Confidence Bound and Thompson Sampling do not perform well for this problem. We then establish fundamental lower bound of $\Omega(K^{1/3} T^{2/3})$ on the performance of any online learning algorithm for this problem, highlighting the hardness of our problem in comparison to the classical MAB problem (where $T$ is the time horizon and $K$ is the number of arms). We also present a simple variant of \textit{explore-then-commit} and establish near-optimal regret bounds for this algorithm. Lastly, we perform extensive numerical simulations to understand the behavior of a suite of algorithms for various instances and recommend a practical guide to employ different algorithms.

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL methods using knowledge-infused learning, which is one of the strategies. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches, and illustrate it with examples from natural language processing for healthcare and education applications.

Joaquin Oliver died in the 2018 Parkland shooting, but recently, he urged people to vote in the 2020 election. Oliver's parents used A.I. to have their dead son encourage people to vote for officials who support gun control, as an extension of the nonprofit they run, Change the Ref. Technologists 3D-printed Oliver's image and created a video of him speaking out against gun violence, which his parents could take to protests around the country. In the video, Oliver's likeness says "I mean, vote for me. Parkland victim Joaquin Oliver urged people to vote in a video that used artificial intelligence to imagine what he'd look like today. Oliver was killed in 2018 in the MSD high school shooting. The video of Oliver, titled "Unfinished Vote," used deepfake technology from Lightfarm Studios. For deepfakes--images and videos generated using A.I.--of celebrities, influencers, or politicians, or more typical public figures, the production team would usually have thousands of images and videos with which to train the A.I. But in the case of a teenager who was more famous after death than during life, the technologists didn't have much material to work with. Instead, they created a single image of his face using three different photographs. Still, the video is convincing enough. The uncanny valley effect comes after the fact, once you realize that the young man in the video who is speaking to you is not actually speaking. On Thursday, shortly before Halloween and in the wake of a much derided, viral tweet exposing her immense wealth and privilege, Kim Kardashian West reacted to Kanye West's surprise gift to her: a hologram of her dead father. In an Instagram post, Kardashian West wrote, "For my birthday, Kanye got me the most thoughtful gift of a lifetime.

Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

B2B buyers today expect a customized and seamless buying experience backed by value-added insights from the seller. A recent study shows that 89% of business buyers rank the experience provided by a company at the same level as its products and services, and 82% are ready to pay more for a great experience. Given this reality, artificial intelligence (AI) and machine learning (ML) have the potential to transform sales forever, as they offer organizations the power to build strategic, adaptive sales processes that can be tailored to enrich each customer's buying experience. As a McKinsey research shows, companies that have pioneered the use of AI in sales have seen benefits, including an increase in leads and appointments of more than 50%, cost reductions of 40% to 60% and call time reductions of 60% to 70%. Prospecting is one of the most challenging parts of the sales process.

In the coming years, there won't be a single industry in the world untouched by Machine Learning. A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change. You probably already use apps many times each day that rely upon Machine Learning techniques. So why stay in the dark any longer? There are many courses on Machine Learning already available.

What if you could build a character that could learn while it played? Think about the types of gameplay you could develop where the enemies started to outsmart the player. This is what machine learning in games is all about. In this course, we will discover the fascinating world of artificial intelligence beyond the simple stuff and examine the increasingly popular domain of machines that learn to think for themselves. In this course, Penny introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph.D in game character AI and over 25 years experience working with games and computer graphics.

The graph represents a network of 7,204 Twitter users whose tweets in the requested range contained "(Artificial Intelligence) OR #AI", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 01 November 2020 at 04:57 UTC. The requested start date was Thursday, 29 October 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 0-hour, 45-minute period from Monday, 26 October 2020 at 15:20 UTC to Wednesday, 28 October 2020 at 16:05 UTC.

Testing always opens a lot of questions, which cases to test? what are the edge cases? Which testing platform to use..etc. But when it comes to testing microservices, the level of complexity is raised up a notch. As they often cater to massive batches of data that are very varied in their nature. Besides, microservices architecture requires the data to be passed around between components (MQ, DataBase…), which can cause erosion and damages that are hard to detect -- i.e.: when the data streams into MQ between microservices and gets rounded, or casting error occurs which causes "silent" issues.