I recently started a new newsletter focus on AI education and already has over 50,000 subscribers. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Understanding the behavior of artificial intelligence(AI) agents is one of the pivotal challenges of the next decade of AI. Interpretability or explainability are some of the terms often used to describe methods that provide insights about the behavior of AI programs.

Virginia Dignum is a Professor in the Dept. of Computing Science of Umeå University, where she leads the Social and Ethical Artificial Intelligence research group. Prior to that she was an Associate Professor in the Faculty of Technology, Policy and Management of Delft University of Technology. She received a PhD in 2004 from Utrecht University, before that she worked for 12 years in consultancy and system development in the areas of expert systems and knowledge management. Her research focuses on the complex interconnections and interdependencies between people, organizations, and technology. Prof. Dignum is actively involved in international initiatives on policy and strategy guidelines for AI research and applications, she is a member of the European Commission High-Level Expert Group on Artificial Intelligence, the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, the TU Delft Design for Values Institute, the European forum AI4People, the Responsible Robotics Foundation, the Dutch AI Alliance on AI (ALLAI-NL), and the ADA-AI Foundation.

This year's award was presented to Sanmi Koyejo, University of Illinois, Urbana-Champaign. This includes scalable, distributed, and robust machine learning, and metric elicitation; selecting more effective machine learning metrics via human interaction, primarily applied to ML fairness. His applied research includes applications to cognitive neuroimaging, healthcare, and biomedical imaging. Some recent work has concentrated on generative models for X-rays and fMRI, and risk-scoring and prediction models. You can see a list of recent publications here.

At the cutting edge of the music industry, innovators like Verphoria are continuously pushing the boundaries of human/machine collaboration in creation as well as business.

The College of Engineering Awards acknowledge the extraordinary efforts of the college's teaching and research assistants, staff and faculty members. Sam Burden is an expert in sensorimotor control and hybrid systems and their application to robotics, neuroengineering and cyber-physical systems. He is a founding co-director of the Laboratory for Amplifying Movement and Performance (AMP Lab), where his research focuses on developing mathematical and computational modeling tools to enable collaborative learning and control between humans and machines. As a first-generation college graduate and UW engineering alum, Burden is committed to broadening participation in engineering, a goal he pursues in his role as the first DEI coordinator for the ECE department, where he works to define and implement the department's diversity, equity and inclusion goals through the formation of an advisory committee and partnering with other department leaders on strategic planning, funding, hiring and recruiting. He is the recipient of an ARO Young Investigator Award, WRF Early Faculty Award and an NSF CAREER Award.

What exactly is digital art? These and other questions will be answered in this article. Introduction: Digital Art is a transition from traditional media such as painting on canvas or on paper to creating an image, photo, etc., digitally using a computer monitor. While traditional art is a creative process, digital art is a technology that can be easily manipulated and modified by anyone to create artwork. Digital art can be superimposed on top of traditional artwork in many different ways to make the combination more interesting.

Learning is a lifelong process. It starts when we're babies and follows us into old age. Education is essential to our development and to how we see the world. The desire for knowledge starts at a young age through an exploration of one's surroundings, followed by formal education and beyond. Throughout life, we learn to retain information in a certain way and whatever your preferred style is, it's crucial to understand why it works for you. If you understand the basics, you can improve and build on them to further your knowledge.

Human resource (HR) departments have long been integral to organizational success, and they're likely to remain that way for decades to come. But the nature of HR is likely to evolve with new technologies, research, and trends. For starters, we might see HR directing the charge in remodeling the form of the ordinary workforce. Increasingly, consumers and employers alike are valuing diversity and inclusion; firms are working harder to ensure a mixture of individuals from other backgrounds are included in any way levels of the company. Later on, this will develop into a much bigger priority. However, this is a slight change when compared with another generation of labor management.

Geoffrey Hinton is a legendary computer scientist. When Hinton, Yann LeCun, and Yoshua Bengio were given the 2018 Turing Award, considered the Nobel prize of computing, they were described as the "Godfathers of artificial intelligence" and the "Godfathers of Deep Learning." Naturally, people paid attention when Hinton declared in 2016, "We should stop training radiologists now, it's just completely obvious within five years deep learning is going to do better than radiologists." The US Food and Drug Administration (FDA) approved the first AI algorithm for medical imaging that year and there are now more than 80 approved algorithms in the US and a similar number in Europe. Yet, the number of radiologists working in the US has gone up, not down, increasing by about 7% between 2015 and 2019.

Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: "Are cows more likely to lie down the longer they stand?"). This challenging task has unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines.