The tab mapper is a handy little tool that will render a guitar tab file with graphic chord diagrams displayed alongside. This comes in handy for people who just don't have every single chord shape memorized. Just plug in the web site address of a valid .tab or .crd file and hit "Go". In general, the tab mapper does a better job with printer friendly URLs. If there is more than one way to play a chord, the tab mapper will choose the most common shape. To see other fingerings, click on the chord diagram and you will be taken to the chord calculator.
Original file located @ http://acm.org.
Show me scales that sound good with the chords in this song: A, Do.
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Read MoreAs digital technologies and artificial intelligence reshape global economies, the need for responsible AI governance and inclusive standards has become urgent, and digital innovations risk deepening divides or reinforcing inequities. On this topic, ACM has organized "Smart Technology, Fair Finance - The Promise and Risks of AI for Sustainable Development" at the International Conference on Financing for Development, 30 June ? 3 July 2025, Seville, Spain. The event will take place on 2 July 2025, 14:30 ? 16:00 CEST. This session will explore strategic investments in digital infrastructure, ethical AI deployment, climate-smart technologies, the risks of digital exclusion, and unintended harms when AI and digital systems are poorly governed. To register for the virtual event on 2 July, click here.
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Listen to the ByteCastsACM has relaunched Communications of the ACM (CACM) as a web-first publication, accessible to all without charge?including the entire backlog of CACM articles. First published in 1958, CACM is one of the most respected information technology magazines. The web-first model will allow ACM to publish articles more rapidly than before so that readers can keep abreast of the lightning-fast changes in the computing field. At the same time, researchers will be able to reference and cite valuable information and research from CACM articles more quickly. This marks another important milestone in ACM's ongoing transition to a fully open access publisher.
In this article from the July issue of Communications of the ACM, Michael Davies and Karthikeyan Sankaralingam state that the relationship between technology scaling and architecture is not well understood. This is especially important to understand for high-capability deep-learning (DL) chips, given the popularity of DL applications. In this article, they seek to answer whether new AI chips can be built at 12nm technology using principles of specialization that match or exceed state-of-the-art (SOTA) chips made at 5nm or lower. They also seek to understand the benefits that technology scaling provides, as well as the benefits architectural innovation can bring over SOTA AI chips.
If you regularly work with open-source code or produce software for a large organization, you're already familiar with many of the challenges posed by collaborative programming at scale. And the scale of the problem has gotten much worse. This is what led a group of researchers at MSR (Microsoft Research) to take on the task of complicated merges as a grand program-repair challenge?one they believed might be addressed at least in part by machine learning. To understand the thinking that led to this effort and then follow where that led, Erik Meijer and Terry Coatta spoke with three of the leading figures in the MSR research effort, called DeepMerge
The examples are nothing if not relatable: preparing breakfast, or playing a game of chess or tic-tac-toe. Yet the idea of learning from the environment and taking steps that progress toward a goal apparently was under-studied when 2024 ACM A.M. Turing Award recipients Andrew G. Barto and Richard S. Sutton took on the topic in the late 1970s. Eventually, their research led to the creation of reinforcement learning algorithms that sought not to recognize patterns but maximize rewards. In this Q&A from the June issue of Communications of the ACM, Barto and Sutton speak about how it all unfolded, and what?s next for the techniques that are so celebrated for their success in AlphaGo and AlphaZero.
ACM Queue?s "Research for Practice" serves up expert-curated guides to the best of computing research, and relates these breakthroughs to the challenges that software engineers face every day. In this installment, Daniel Bittman curates a collection of papers about "anything related to far-out memories." He includes more than 30 years of research, from single-address-space operating systems, to software-based distributed shared memory, to far memory offload, to single-level stores for persistent memory. The featured papers challenge assumptions about isolation, sharing and locality, transparency, and movement of memory and computation. The thread that ties all these selections together in Bittman's analysis is the topic of addressing, or how data references data.
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