Margo Seltzer, the Canada 150 Research Chair in Computer Systems at the University of British Columbia and 2023–2024 ACM Athena Lecturer, is the kind of researcher who stands out not just for her accomplishments, but for her tirelessness. After building a database software library that underpinned many first-generation Internet services, she worked on topics that range from file systems and storage to capturing and accessing data provenance. Here, she speaks with Leah Hoffmann about finding impactful research projects--and keeping up with everything that's going on in the field. The story of Berkeley DB, the database software library that you built with Keith Bostic and Mike Olson, has been told before at greater length, but let me see if I can summarize. Your work on packages such as hash and B-tree was released with Berkeley Unix as the DB 1.85 library.

Python is one of the most popular programming languages in existence. Easy to learn and easy to use, it has been around for years, so there is a large community of Python developers to support each other, and it has built up an ecosystem of libraries that allow users to drop in the functionalities they need. It does, however, come with downsides: its programs tend to run slowly, and because it is inefficient at running processes in parallel, it is not well suited to some of the latest artificial intelligence (AI) programming. Hoping to overcome those difficulties, computer scientist Chris Lattner set out to create a new language, Mojo, which offers the ease of use of Python, but the performance of more complex languages such as C or Rust. He teamed up with Tim Davis, whom he had met when they both worked for Google, to form Modular in January 2022.

Richmond, Virginia: At the Linux Plumbers Conference, the invite-only meeting for the top Linux kernel developers, ByteDance Linux Kernel Engineer Cong Wang, proposed that we use artificial intelligence (AI) and machine learning (ML) to tune the Linux kernel for the maximum results for specific workloads. Also: Rust in Linux: Where we are and where we're going next Generally speaking, the Linux kernel works just fine for most tasks. But, to get the most from it for a particular job, you must fine-tune it by setting its parameters for the best possible results. There are thousands of parameters. Even for a Linux expert, tuning them for optimal performance is a long, hard job.

Two days later on another stage, in another venue, at another developers' conference, Nadella made his second unannounced appearance of the week--this time at GitHub Universe. There Thomas Dohmke, GitHub's CEO, was showing off a new version of the company's AI programming tool, Copilot, that can generate computer code from natural language. Nadella was effusive: "I can code again!" he exclaimed. Today, Nadella will be onstage speaking to developers at Microsoft Ignite, where the company is announcing even more AI-based developer tools, including an Azure AI Studio that will let devs choose between model catalogs from not only Microsoft, but also the likes of Meta, OpenAI, and Hugging Face, as well as new tools for customizing Copilot for Microsoft 365. If it seems like Nadella is obsessed with developers, you're not wrong.

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Python is the world's most popular programming language not just because it's relatively easy to learn, but because it's used in so many applications and it's highly scalable. If you've ever wanted to learn to code, starting with Python is a great idea. And it's an even better idea now because during Deal Days, you can get The Premium Python Programming Certification Bundle for $23.97 (reg. This bundle includes ten courses from top instructors like Joe Rahl (4.6/5-star instructor rating) and Edouard Renard (4.6/5-star rating). Starting with the absolute basics of Python, you'll learn basic coding principles, explore Object-Oriented Programming (OOP), and much more as you slowly level up your skills.

Would you believe that almost all of the technology you use today is here because of a misbehaving printer? In the early 1980s, an MIT Artificial Intelligence Laboratory programmer named Richard M. Stallman (RMS) was having trouble with a notoriously unreliable printer, a Xerox 9700. He wanted to modify its software to notify users of its frequent jams. Great idea, but he couldn't get to the source code to make these changes. This ran counter to the Lab's open ethos.

Low-code has the potential to empower more people to automate tasks by creating computer programs.

Model-based diagnosis has always been conceived as set-oriented, meaning that a candidate is a set of faults, or faulty components, that explains a collection of observations. This perspective applies equally to both static and dynamical systems. Diagnosis of discrete-event systems (DESs) is no exception: a candidate is traditionally a set of faults, or faulty events, occurring in a trajectory of the DES that conforms with a given sequence of observations. As such, a candidate does not embed any temporal relationship among faults, nor does it account for multiple occurrences of the same fault. To improve diagnostic explanation and support decision making, a sequence-oriented perspective to diagnosis of DESs is presented, where a candidate is a sequence of faults occurring in a trajectory of the DES, called a fault sequence. Since a fault sequence is possibly unbounded, as the same fault may occur an unlimited number of times in the trajectory, the set of (output) candidates may be unbounded also, which contrasts with set-oriented diagnosis, where the set of candidates is bounded by the powerset of the domain of faults. Still, a possibly unbounded set of fault sequences is shown to be a regular language, which can be defined by a regular expression over the domain of faults, a property that makes sequence-oriented diagnosis feasible in practice. The task of monitoring-based diagnosis is considered, where a new candidate set is generated at the occurrence of each observation. The approach is based on three different techniques: .1/ blind diagnosis, with no compiled knowledge, .2/ greedy diagnosis, with total knowledge compilation, and .3/ lazy diagnosis, with partial knowledge compilation. By knowledge we mean a data structure slightly similar to a classical DES diagnoser, which can be generated (compiled) either entirely offline (greedy diagnosis) or incrementally online (lazy diagnosis). Experimental evidence suggests that, among these techniques, only lazy diagnosis may be viable in non-trivial application domains.

The year 1998 was a pivotal one in the history of technology: Apple's introduction of the iMac helped set the company back on the path to success after it nearly went bankrupt earlier in the decade; Google was founded by two Stanford students, Larry Page and Sergey Brin; and Microsoft introduced Windows 98, an improved version of its popular computer operating system. That May, Microsoft also became the target of a historic antitrust lawsuit lodged by the Department of Justice and twenty states, accusing it of anticompetitive behavior in two domains: attempting to maintain its monopoly in computer operating systems and trying to monopolize a new market, that of Internet browsers. At the time, residential Wi-Fi connectivity was rapidly expanding across America, and, in the quaintly titled "browser wars," Netscape Navigator, a popular browser released by Mosaic Communications Corporation in 1994, fought Microsoft's Internet Explorer for the growing class of Web-connected consumers. Microsoft, the D.O.J. alleged, had attempted to crush Netscape by making deals with Internet-service providers that prioritized Explorer access at Netscape users' expense. The trial began that fall, and included seventy-six days of testimony that took place over more than eight months, during which a government witness alleged that a Microsoft executive had pledged to "cut off Netscape's air supply" (which a Microsoft attorney denied).