Welcome to PyTorch: Deep Learning and Artificial Intelligence! Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence. Is it possible that Tensorflow is popular only because Google is popular and used effective marketing? Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems? It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR).

BEGIN ARTICLE PREVIEW: Test, Measurement & Analytics WHITEPAPERS How a powertrain manufacturer optimized its welding process using advanced image algorithms from a platform that extracts key features from images, analyzes them, and informs MES decisions in near real-time. How auto-manufacturers can apply ML & AI algorithms to enhance image analytics on their factory floor and to ensure higher product quality? Discover the next generation visual inspection in our new case study. In this case study , you will learn about: Current limitations of image inspection in the manufacturing industry. The O+ end-to-end solution, which brings machine learning and deep learning to image analysis in the production line. How a powertrain manufacturer deployed OptimalPlus’ software on the edge and utilized its image analysis capabilities to optimize the welding process. Despite its great potentia

Experts say that artificial intelligence (AI) is likely to have a bigger impact on health care than anything the field has experienced in our lifetime. During the COVID-19 pandemic, we're seeing how AI is helping clinicians screen for, and diagnose, COVID-19; it's aiding researchers' efforts to develop new drugs and vaccines. Though some health care workers worry that AI tools could replace their jobs, in fact AI is driving innovation, process improvements and better patient outcomes. It's enhancing providers' ability to care for more patients safely. The American Hospital Association and Microsoft now offer a free, one-hour course, for continuing education credits, to guide health care teams through key considerations and specific actions for AI's responsible and strategic implementation.

The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL based on planning in an abstract space of higher-order skills. We learn the skills in an unsupervised manner using intrinsic rewards and plan over the learned skills using a learned dynamics model. Moreover, our framework permits skill discovery even from offline data, thereby reducing the need for excessive real-world interactions. We demonstrate empirically that LiSP successfully enables long-horizon planning and learns agents that can avoid catastrophic failures even in challenging non-stationary and non-episodic environments derived from gridworld and MuJoCo benchmarks.

Thompson sampling (TS) has emerged as a robust technique for contextual bandit problems. However, TS requires posterior inference and optimization for action generation, prohibiting its use in many internet applications where latency and ease of deployment are of concern. We propose a novel imitation-learning-based algorithm that distills a TS policy into an explicit policy representation by performing posterior inference and optimization offline. The explicit policy representation enables fast online decision-making and easy deployment in mobile and server-based environments. Our algorithm iteratively performs offline batch updates to the TS policy and learns a new imitation policy. Since we update the TS policy with observations collected under the imitation policy, our algorithm emulates an off-policy version of TS. Our imitation algorithm guarantees Bayes regret comparable to TS, up to the sum of single-step imitation errors. We show these imitation errors can be made arbitrarily small when unlabeled contexts are cheaply available, which is the case for most large-scale internet applications. Empirically, we show that our imitation policy achieves comparable regret to TS, while reducing decision-time latency by over an order of magnitude.

In many academic settings, medical students start their scientific work already during their studies. Like at our institution, they often work in interdisciplinary teams with more or less experienced (postgraduate) researchers of pharmaceutical sciences, natural sciences in general, or biostatistics. All of them should be taught good research practices as an integral part of their education, especially in terms of statistical analysis. This includes reproducibility as a central aspect of modern research. Acknowledging that even educators might be unfamiliar with necessary aspects of a perfectly reproducible workflow, I agreed to give a lecture series on reproducible research (RR) for medical students and postgraduate pharmacists involved in several areas of clinical research. Thus, I designed a piloting lecture series to highlight definitions of RR, reasons for RR, potential merits of RR, and ways to work accordingly. In trying to actually reproduce a published analysis, I encountered several practical obstacles. In this article, I focus on this working example to emphasize the manifold facets of RR, to provide possible explanations and solutions, and argue that harmonized curricula for (quantitative) clinical researchers should include RR principles. I therefore hope these experiences are helpful to raise awareness among educators and students. RR working habits are not only beneficial for ourselves or our students, but also for other researchers within an institution, for scientific partners, for the scientific community, and eventually for the public profiting from research findings.

This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.

Reinforcement Learning (RL) possesses immense potential and is doubtless one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered as a game-changer in Data Science, particularly after observing the winnings of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not restricted to games. The progress in Reinforcement Learning, especially during the last few years, has been sensational. RL is everywhere now, ranging from resource management to chemistry, from healthcare to finance, and from Recommender Systems to more advanced applications in stock prediction.

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from

I recently started to follow an exciting and mind-bending philosophy online course at MIT called Minds and Machines. The course is a thorough, rigorous 12 Weeks Learning Path introduction to contemporary philosophy of mind, exploring consciousness, reality, artificial intelligence (AI), and more. It is definitively one of the most in-depth philosophy courses available online that I ever frequented. The first effect of starting study philosophy at Massachusetts Institute of Technology is that I'm asking more challenging questions… the second effect is that I'm writing more about those questions. I'm in this moment, exploring the relationship between the mind and the body, the capacity of computers to think, the way we perceive reality, and the perspective of the existence of a science of consciousness. As a first result, I've started to pay particular attention to one specific question that definitively has a lot to relate to my daily work as an AI expert: what is intelligence?