Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students' computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware-enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course.

Mean-Field Game (MFG) serves as a crucial mathematical framework in modeling the collective behavior of individual agents interacting stochastically with a large population. In this work, we aim at solving a challenging class of MFGs in which the differentiability of these interacting preferences may not be available to the solver, and the population is urged to converge exactly to some desired distribution. These setups are, despite being well-motivated for practical purposes, complicated enough to paralyze most (deep) numerical solvers. Nevertheless, we show that Schr\"odinger Bridge - as an entropy-regularized optimal transport model - can be generalized to accepting mean-field structures, hence solving these MFGs. This is achieved via the application of Forward-Backward Stochastic Differential Equations theory, which, intriguingly, leads to a computational framework with a similar structure to Temporal Difference learning. As such, it opens up novel algorithmic connections to Deep Reinforcement Learning that we leverage to facilitate practical training. We show that our proposed objective function provides necessary and sufficient conditions to the mean-field problem. Our method, named Deep Generalized Schr\"odinger Bridge (DeepGSB), not only outperforms prior methods in solving classical population navigation MFGs, but is also capable of solving 1000-dimensional opinion depolarization, setting a new state-of-the-art numerical solver for high-dimensional MFGs. Our code will be made available at https://github.com/ghliu/DeepGSB.

The goal of Artificial Life research, as articulated by Chris Langton, is "to contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be" (1989, p.1). The study and pursuit of open-ended evolution in artificial evolutionary systems exemplifies this goal. However, open-ended evolution research is hampered by two fundamental issues; the struggle to replicate open-endedness in an artificial evolutionary system, and the fact that we only have one system (genetic evolution) from which to draw inspiration. Here we argue that cultural evolution should be seen not only as another real-world example of an open-ended evolutionary system, but that the unique qualities seen in cultural evolution provide us with a new perspective from which we can assess the fundamental properties of, and ask new questions about, open-ended evolutionary systems, especially in regard to evolved open-endedness and transitions from bounded to unbounded evolution. Here we provide an overview of culture as an evolutionary system, highlight the interesting case of human cultural evolution as an open-ended evolutionary system, and contextualise cultural evolution under the framework of (evolved) open-ended evolution. We go on to provide a set of new questions that can be asked once we consider cultural evolution within the framework of open-ended evolution, and introduce new insights that we may be able to gain about evolved open-endedness as a result of asking these questions.

You can follow me on Linkedin! Note: There are different angles to answer an interview question. The author of this newsletter does not try to find a reference that answers a question exhaustively. Rather, the author would like to share some quick insights and help the readers to think, practice and do further research as necessary. Source of video/answers: Stanford CS224N: NLP with Deep Learning Winter 2019 Lecture 8 -- Translation, Seq2Seq, Attention by Dr. Abby See Natural Language Processing with Attention Models by Deeplearning.ai

The concept of machine learning is essentially the same as what it sounds like; it refers to the concept that various forms of technology, such as computers and tablets, can learn something based on programming and other data. Although it has the appearance of an idea from the far future, most people now make regular use of this level of technology. One particularly useful application of this is speech recognition. The technology is utilized by virtual assistants such as Siri and Alexa to do tasks such as reciting reminders, answering queries, and carrying out requests. As machine learning becomes increasingly popular, an increasing number of individuals are deciding to specialize in the field as machine learning engineers.

AI is no longer exclusively for digitally native companies like Amazon, Netflix, or Uber. Unsurprisingly, Gartner predicts that more than 75% of organizations will shift from piloting AI technologies to operationalizing them by the end of 2024 -- which is where the real challenges begin. There is a chasm between ML and MLOps that can be tricky to scale. Getting one or two AI models into production is different from running an entire enterprise or product on AI. And as AI is scaled, problems can (and often do) scale, too.

Introduction to Machine Learning is a front row seat to help beginners unravel the curious mystery behind machine learning. What you'll learn in Machine Learning for Absolute Beginners - Level 1 If you're a complete beginner to the world of Robotics, This course will teach you all the basic fundamentals you'll need. Introduction to Machine Learning is a front row seat to help beginners unravel the curious mystery behind machine learning. You can use this course to gain knowledge of basic machine learning concepts in preparation for, or alongside, more advanced courses. Machine Learning is the study of algorithms that improve their performance P at some task T with experience E. As Herbert Simon once said, "Learning is any process by which a system improves performance from experience."

Artificial Intelligence (AI) has a pivotal role in many K-12 educational systems, providing benefits for both students and teachers. To best utilize AI's potential, it is key for governments to implement policies conducive to AI's adoption within classrooms. We discuss the benefits and limitations AI provides to education as well as the steps needed to responsibly use AI in education in the future. AI has contributed massively to making the American educational landscape much stronger and more stable. Through the use of AI in schools, learning has become much more accessible to all groups.

Have you started, and stopped learning some part of data science and data analytics a bunch of times? Maybe you can build a classification model and you can kind of explain how random forests work, but your portfolio isn't much more than copies of the tutorial assignments? Are you curious about Deep Learning, neural networks, and all things artificial intelligence, but unsure of where to get started? Then you and I have a lot in common. Today, we'll be exploring Fast.ai's course, Practical Deep Learning, A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.

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