Voices of Open Source
See the following -
Celebrating 25 Years of Open Source at the COSCUP Conference in Taiwan
Navigating uncharted waters often leads to intriguing discoveries. Imagine immersing yourself in a realm that commemorates a quarter-century of Open Source accomplishment. Invited by Open Source Initiative (OSI) to reflect upon the 25 years of Open Source at COSCUP, a conference in Taiwan that focuses on coders, users and promoters of Open Source, I threw myself into these waters by proposing a review of history that is not unique around the globe, taking my perspective from South America and Europe to Asia, where I had never before ventured. You can read a full transcript of my talk here and check my critical take on the topic. After all, to review is to be able to identify where we failed and to be able to proceed from there.
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How The OSI Checks If New Licenses Comply With The Open Source Definition
Earlier this month, we announced completion of the project to review the list of Approved Licenses. The Open Source community needs a resource to confidently and easily identify OSI-approved licenses, and now we have it. This approval registry offers a comprehensive and authoritative listing of all licenses so organizations know that the license they choose for their project allows their software to be freely used, modified, shared and monetized in compliance with the Open Source Definition. But how do we check the compliance of new licenses with the Open Source Definition? The License Review Working Group was formed to examine ways to improve the license review process, with the stated purpose of evaluating or reevaluating:
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How To Make App Stores Friendly To Open Source
Microsoft recently seemed to propose that Open Source software didn’t belong in the Windows app store. Excuse me? After the news broke, Giorgio Sardo, Microsoft’s General Manager of the Microsoft Store, argued on Twitter that it wasn’t Microsoft’s intent. “We absolutely want to support developers distributing successful OSS apps. In fact, there are already fantastic OSS apps in the Store! The goal of this policy is to protect customers from misleading listings.” Predictably, confusion results. And the kerfuffle over FairEmail and the Google Play Store earlier this year is a good example of how this sort of confusion is not entirely new, leading to questions about intent. I’ve talked with developers and business managers about their experience in preparing software packages for commercial app stores. Universally, everyone reports having issues with app stores’ packaging. These include...
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The Cyber Resilience Act Introduces Uncertainty And Risk Leaving Open Source Projects
What might happen if the uncertainty persists around who is held responsible under the Cyber Resilience Act (CRA)? The global Open Source community is averse to legal risks and generally lacks access to counsel, so it’s very possible offers of source code will simply be withdrawn rather than seeking to resolve the uncertainty. The CRA rightly addresses the need for commercial suppliers to protect their customers from exploits and cyber attacks. But legislators have exposed the open development of software itself to the regulations rather than just the for-profit use of Open Source artifacts in the marketplace. They are incorrectly assuming that Dirk Riehle’s terminology calling single-company projects “commercial Open Source” means it’s possible to use the “commerciality” of an application to distinguish single-company activity from community projects, and by using the concepts of proprietary software to then define boundaries.
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To Trust Artificial Intelligence, It Must Be Open And Transparent. Period.
Machine learning has been around for a long time. But in late 2022, recent advancements in deep learning and large language models started to change the game and come into the public eye. And people started thinking, “We love Open Source software, so, let’s have Open Source AI, too.” But what is Open Source AI? And the answer is: we don’t know yet. Machine learning models are not software. Software is written by humans, like me. Machine learning models are trained; they learn on their own automatically, based on the input data provided by humans. When programmers want to fix a computer program, they know what they need: the source code. But if you want to fix a model, you need a lot more: software to train it, data to train it, a plan for training it, and so forth. It is much more complex. And reproducing it exactly ranges from difficult to nearly impossible.
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