Data Science is an interdisciplinary field that uses scientific methods, algorithms to extract clean information from raw data for the formulation of actionable insights. The Data Science field is growing so rapidly, and revolutionizing so many industries. Data Science has incalculable benefits in business, research, and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights, and making our lives more convenient.

We explore different ways of implementing temporal constraints expressed in an extension of Answer Set Programming (ASP) with language constructs from dynamic logic. Foremost, we investigate how automata can be used for enforcing such constraints. The idea is to transform a dynamic constraint into an automaton expressed in terms of a logic program that enforces the satisfaction of the original constraint. What makes this approach attractive is its independence of time stamps and the potential to detect unsatisfiability. On the one hand, we elaborate upon a transformation of dynamic formulas into alternating automata that relies on meta-programming in ASP. This is the first application of reification applied to theory expressions in gringo. On the other hand, we propose two transformations of dynamic formulas into monadic second-order formulas. These can then be used by off-the-shelf tools to construct the corresponding automata. We contrast both approaches empirically with the one of the temporal ASP solver telingo that directly maps dynamic constraints to logic programs. Since this preliminary study is restricted to dynamic formulas in integrity constraints, its implementations and (empirical) results readily apply to conventional linear dynamic logic, too.

In my time on this earth I've pledged allegiance to many masters. Their words--always measured--tend to echo at critical junctures. Whenever I face down an unforeseen attack, whenever my enemies reveal themselves, the sage advice of my betters bubbles up from my deepest brain fold, reminding me to press [square] to perform a quick attack. Obviously, I need this information. I'm dead without it, and whatever game I'm playing is decidedly less fun if I have to die a dozen times for the knowledge.

One possible definition of reinforcement learning (RL) is a computational approach to learning how to maximize the total sum of rewards when interacting with an environment. While a definition is useful, this tutorial aims to illustrate what reinforcement learning is through images, code, and video examples and along the way introduce reinforcement learning terms like agents and environments. As a previous post noted, machine learning (ML), a sub-field of AI, uses neural networks or other types of mathematical models to learn how to interpret complex patterns. Two areas of ML that have recently become very popular due to their high level of maturity are supervised learning (SL), in which neural networks learn to make predictions based on large amounts of data, and reinforcement learning (RL), where the networks learn to make good action decisions in a trial-and-error fashion, using a simulator. RL is the tech behind mind-boggling successes such as DeepMind's AlphaGo Zero and the StarCraft II AI (AlphaStar) or OpenAI's DOTA 2 AI ("OpenAI Five").

The linear algebra essential to data science, machine learning, and artificial intelligence is often overlooked as most introductory courses fail to display the big picture.

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.

The increased integration of autonomous robots in industrial settings requires not only that industrial robots display more "human-like" behaviors for efficient human-robot collaboration (HRC) but also regulations for safety and ethical viewpoint in the working environment. On the one hand, we need to build robots that can act autonomously in a given and sometimes unpredictable environment. On the other hand, we want the same robots to cooperate with humans to reach a common goal in a safe environment. In this workshop, we want to address some of the issues that are relevant for HRC in industrial settings, including its "human factors" such as lack of trust and adaptability to autonomous machines in collaborative tasks, accountability (legal rights and obligations), and especially the sentiment that robots are "stealing jobs." Whereas some hope that in this HRC endeavor (supposed to boost industrial and economic growth) humans will have a supervisory role, thus decreasing their physical and cognitive efforts, some others are concerned that autonomous robots will have a negative impact on our economic growth and pose a negative challenge for our culture.

The automotive industry has undergone a massive evolution in the past two centuries. In just 200 years, traveling has moved from bullock carts to autonomous cars. If we compare this time frame and the pace of change with other sectors, it will surely leave us in awe. Moving ahead, humankind is expecting a big turnaround in the self-driving car industry. A forecast suggests that by 2025, there will be approximately 8 million autonomous vehicles or semi-autonomous cars on the road.

Today, artificial intelligence innovation is all over the place. From online shopping to the data used for scholastic tasks. From sectors like automotive, manufacturing to finance and healthcare, AI has become an integral part of human life. Particularly with regards to financial investment, in addition to the fact that artificial intelligence allows you to settle on the right choices by looking over the feasible options available, but also opens unlimited opportunities for monetary escalation. In this segment, you will learn about the top AI stocks in 2022 to invest in.

Machine Learning is a hot topic! Python Developers who understand how to work with Machine Learning are in high demand. But how do you get started? Maybe you tried to get started with Machine Learning, but couldn't find decent tutorials online to bring you up to speed, fast. Maybe the information you found was too basic, and didn't give you the real-world Machine learning skills using Python that you needed.