Enshrouded is a beautiful combination of Minecraft, Skyrim and resource gathering that makes it at least three games in one. My daughter told me I would love it and I ignored her for too long. I've tackled Elden Ring, but much prefer the often gentler combat of Enshrouded. It sometimes makes me feel like an elite fighter, then other times kicks my arse in precisely the right measures. Its real joy is the flexibility to spend your time doing whatever tickles your fancy. I'll spend a few hours growing crops to make a cake or smelting metals for better armour, then knock off a few quests to unlock new materials and weapons.

As machine learning (ML) matures in both healthcare and other fields, there is a need to process large datasets for model training, especially with the rise of data-hungry foundation models [Hoffmann et al., 2024]. To meet growing data needs, sophisticated and efficient tooling [Google, PyTorch, HuggingFace] have been developed to help scale analysis to large datasets. However, many of these tools have been difficult to use in research involving electronic health record (EHR) data due to its unique nested event stream data structure [McDermott et al., 2023] consisting of a collection of subjects (also generally referred to as patients), where each subject contains a sequence of discrete time-stamped events with associated per-event data. Figure 1 illustrates this event stream structure for an example subject. This event stream data structure is poorly handled by existing data processing tools that are optimized for tabular data, images, or text. These differences have forced healthcare ML researchers to build their own data processing pipelines [Yang et al., 2023, Gupta et al., 2022, Tang et al., 2020, McDermott et al., 2023] for handling EHR data, which tend to be very inefficient in terms of memory, CPU, and disk usage. In this work, we help with these inefficiency issues by introducing meds_reader, an open-source Python package that can be used for building fast and efficient EHR ML processing pipelines. We demonstrate the benefits of meds_reader by using it to reimplement labeling and featurization within two existing EHR processing pipelines, achieving 10-100x improvements in CPU, memory, and disk usage.

Spider-Man 2 was even better than the original. Not knowing who the antagonists were going to be was truly exciting, and that feeling of swooping through the streets of New York City was even more exhilarating! The side missions were full adventures with their own cutscenes and unique objectives. The performers were all superb and the twists and turns of the plot were exciting. It has to be Tears of the Kingdom. I was never a Nintendo kid – always Sega – and I bounced off of Breath of the Wild in 2017 and haven't touched my Switch since.

Dataiku announced it raised $200 million in a Series F round this week at a $3.7 billion valuation. This latest valuation is a 20% decline from its August 2021 valuation of $4.6 billion, perhaps reflecting the changing macroeconomic tide. The company's latest round was led by a led by new investor, Boston-based Wellington Management, who seems confident in Dataiku's future: "Dataiku's proven track record, management team, growth trajectory, and customer roster, positions the company to scale AI to new heights. We are pleased to partner and contribute to their impressive journey," Matt Witheiler, consumer/technology sector lead, at Wellington Management, said in a release. "Dataiku has taken a leadership position helping enterprises put massive datasets to work at unprecedented speed and creating a culture of AI focused on delivering compounding business results."

The strength of the text is in the large number of examples and the step by step explanation of each topic as it is introduced. It is compiled in a way that allows distance learning, with explicit solutions to set problems freely available online. The miscellaneous exercises at the end of each chapter comprise questions from past exam papers from various universities, helping to reinforce the reader's confidence. Also included, generally at the beginning of sections, are short historical biographies of the leading players in the field of linear algebra to provide context for the topics covered. The dynamic and engaging style of the book includes frequent question and answer sections to test the reader's understanding of the methods introduced, rather than requiring rote learning.

Sun 21 Jan 2018 12.37 EST Last modified on Sun 21 Jan 2018 17.00 EST Journalism is largely collaboration: reporters with sources, writers and editors, lawyers advising publishers, producers with distributors, and audiences feeding back their knowledge. Rapid development of artificial intelligence means journalists are likely to collaborate more and more with machines that think. The word itself, machines, feels so industrial era, but "robots" feels too limited. Humans are busy building brains, if not yet minds. So my shorthand for now is AI.

You are cordially invited to become a member of the AI Community's principal scientific society: Both these facts run counter to other connectionist models but easily fit SDM. Sparse Distributed Memory will be of interest to anyone doing research in neural models or brain physiology. As the theory is refined, the book will also be of interest to those trying to find applications for neural models. Finally, it will be fascinating to anyone who is even slightly curious about human intelligence and how it might arise from the brain. Terry Rooker is a graduate student at the Oregon Graduate Institute.

Case-based reasoning (CBR) is becoming a viable real-world technology. First, it fragments each CBR system across many chapters, making it difficult to get the big picture of how the system works and obscuring the interrelatedness of the system's parts. In addition, having each chapter draw its examples from multiple systems adds a certain context-switching overhead: Each time a system is introduced (or reintroduced), the book must set the context anew, and the reader must recall the details of the system. A second drawback to the unified framework is that although it has fairly broad coverage, it is still biased toward those systems that fit it best. As a result, important work sometimes gets only a cursory mention in the book.

In this respect, what Pearl seems to have accomplished sometimes looks like a formalism in search of an interpretation without which the truth or the falsity of his claims is often impossible to assess. If the conceptions upon which his view is based do indeed conform to one or another of the traditional Bayesian models, moreover, then the very idea of a probability-based heuristic confronts a number of difficult problems of its own with respect to the distribution of probabilities to sets of alternative hypotheses, paths, or solutions, relative to the proposed refinements of those alternative hypotheses, paths, or solutions.6 These considerations suggest that traditional conceptions should not be taken for granted, especially if we assume that this is what Pearl intends by his observation that "Probability theory is today our primary (if not the only) language for formalizing concepts such as "average" and "likely," and therefore it is the most natural language for describing those aspects of (heuristic) performance that we seek to improve" (p. On general theoretical grounds, I think, there are excellent reasons to suppose that (a)-(f) are fundamental problems in AI science and that an extensional probabilistic analysis of this sort simply cannot lead to their effective solutions. In order to understand the traditional approach, however, this book is recommended with the reservations implied above, namely, that the author has omitted basic definitions that might not be familiar to some readers, and that serious difficulties seem to confront the theoretical framework he apparently endorses, where these difficulties are especially severe from an epistemological perspective.

The book might also supply points of interest, although not always dependable instruction, to social scientists, philosophers, and psychologists. Thornton describes his book as a research memorandum "in keeping with the technicolour spirit of our times" and also owns to "importing various devices from the pop-science genre" (Preface, pp. His pop-science offerings include "light relief through a concoction of dialogues, anecdotes, and other forms of non-scientific material" (Preface, p. 2) of which the more historical chapters make the best reading. Here, departures from strict accuracy are offset by the liveliness of Thornton's accounts of Kepler's work (chapter 3) and Turing's part in breaking the German wartime Enigma code (chapter 6). He is less successful with Hume's demonstration of the fallibility of the inductive process. The old philosophical concern was that theories inductively inferred from sampled facts cannot be guaranteed true for every single future fact that might be ...