Welcome to our April 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent conferences and events, and much more. In this edition we cover a diverse range of topics including AI ethics, education, music, GPT-Neo, and Westworld. Marija Slavkovik wrote this very interesting retrospective on the AAAI symposium on implementing AI ethics. The aim of the symposium was to "facilitate a deeper discussion on how intelligence, agency, and ethics may intermingle in organizations and in software implementations." Another ethics conference on the horizon is the AAAI/ACM conference on artificial intelligence, ethics, and society, scheduled for 19-21 May.

My course will be your complete guide to the theory and applications of supervised & unsupervised machine learning and predictive modeling using the R-programming language. Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY MACHINE LEARNING & PREDICTIVE MODELS (K-means, Random Forest, SVM, logistic regression, etc) in R (many R packages incl. This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (classification & regressions) and unsupervised clustering techniques. Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based Data Science and Machine Learning domain. In this age of big data, companies across the globe use R to analyze big volumes of data for business and research.

JADS organizes this educational program in collaboration with EAISI part of TU/e. The program combines a practical approach, working through example AI projects thereby showing how to successfully execute an AI project, with building a solid understanding of the fundamental principles underlying #machinelearning. This newly developed educational program is aimed at management and senior professionals who recognize the opportunities of Data Science and AI.

In the priciest office block in the central province of Henan's Luoyang city, some two-dozen people stare at blurry street photos on their computer screens, carefully drawing squares around vehicles and pedestrians. Purple for bikes, green for humans, baby blue for three-wheelers," said employee Liu Yajing of the data labeling firm Intellect Growth Technology. Her work is part of an autonomous vehicle project. The data labels will help train a driverless car to identify and avoid hitting other vehicles or humans. Liu opens the next fuzzy photo of the same street corner, taken from a different angle. She highlights a car, a bike and a pedestrian. Data labeling is repetitive work, but it's the starting point for most artificial intelligence applications. China's state council issued plans for the country to be a leader in AI by 2030, which includes preferential policies and tax breaks for local firms. Ding Yijun said his path to become one of the investors in the data labeling firm was sheer luck. "Some people in online chat groups said we could earn some extra money [data labeling]," Ding said. The people identified themselves as contractors of the Chinese tech giant Baidu, which was a claim Ding and his friends thought could be a scam since they were in a third-tier city like Luoyang. Still, they took a leap of faith and accepted some freelance projects. "The business at first was based on trust.

Artificial Intelligence (AI) has made a massive impact in the modern world. The use of AI at any level has proved to be fantastic. It automated a significant number of tasks, reducing human effort and has led everyone to believe that there is even more to come. Infigon Futures is a company that empowers the lives of individuals who are seeking educational and career goals to help them make decisions for a brighter future. Speaking with Analytics Insight, Omkar Patil, CEO, Infigon Futures, provides insight into how the education and career planning platform is helping individuals ranging from 11 to 30 in age and what solutions the company offers to improve their lives significantly.

One of the common problems with learning image processing is the high cost of software. In this course, I entirely use open source software including the Google Earth Engine Python API and Colab. All sample data and script will be provided to you as an added bonus throughout the course. Jump in right now and enroll.

It is a central hub for learning in Teams with AI that recommends the right content at the right time. With Viva Learning, employees can easily discover and share training courses, and managers get all the tools to assign and track the completion of courses to help foster a learning culture. Individuals can build their own training environments and track their progress on courses. Team leaders and supervisors also have the option to assign specific learning tasks to individual members of staff. Hence, it's a great way to keep your team growing in any environment. It is a central hub for learning in Teams with AI that recommends the right content at the right time.

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

This paper presents a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks; every week consists of lectures, practical sessions, and quiz assignments. Three weeks out of 12 are followed by Kaggle-style coding assignments. Our course intends to serve multiple purposes: (i) familiarize students with the core concepts and methods in NLP, such as language modeling or word or sentence representations, (ii) show that recent advances, including pre-trained Transformer-based models, are built upon these concepts; (iii) introduce architectures for most demanded real-life applications, (iv) develop practical skills to process texts in multiple languages. The course was prepared and recorded during 2020, launched by the end of the year, and in early 2021 has received positive feedback.

In this article, we show and discuss our experience in applying the flipped classroom method for teaching Conditional Random Fields in a Natural Language Processing course. We present the activities that we developed together with their relationship to a cognitive complexity model (Bloom's taxonomy). After this, we provide our own reflections and expectations of the model itself. Based on the evaluation got from students, it seems that students learn about the topic and also that the method is rewarding for some students. Additionally, we discuss some shortcomings and we propose possible solutions to them. We conclude the paper with some possible future work.