Welcome to our biweekly selection of Free and Open machine learning news. Created using our own opinionated selection and summary algorithm. FOSS machine learning is crucial for everyone. Machine Learning is a complex technology. So keep it simple.
1 Introducing the Model Card Toolkit for Easier Model Transparency Reporting
The Model Card Toolkit (MCT) streamlines and automates generation of Model Cards [1], machine learning documents that provide context and transparency into a model’s development and performance. Integrating the MCT into your ML pipeline enables the sharing model metadata and metrics with researchers, developers, reporters, and more.
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2 A power tool for experimenting with GPT-3
Explorer is a power tool for GPT-3 experimentation with full history, sharing, and the community’s best-practices built-in. If you’re just getting started with GPT-3 or don’t want to build out your own boilerplate codebase, try the hosted version: Explorer. The source is available here.
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3 CUI-based Tree Visualizer for Universal Dependencies
I love simple. This is a simple dependency visualizer for spaCy, UniDic2UD, Stanza, NLP-Cube, etc. It is a CUI-based Tree Visualizer for Universal Dependencies and Immediate Catena Analysis.
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4 InvoiceNet: Deep neural network to extract intelligent information from invoice documents
Nice FOSS application of DNN. I think you should never ever scrap information from PDFs. There is always a simpler solution. But the world of E-invoicing is filled with ancient technologies…
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5 Py-AutoML: low-code machine learning library in Python
Py-AutoML is an open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment. Build upon Keras. I know low-code is too often a misleading marketing buzz word. But low and no-code solutions do appear to have a magic attraction.
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6 The Building Blocks of an AI Strategy
AI without ethics is a recipe for disaster. The final AI strategy pillar is ethics, which is not necessarily a common component of technology strategy. Because AI is not a regular technology, the AI strategy needs to be approached differently than regular technology strategy. It is possible that an AI ethics office will need to be created within organizations to oversee AI activities, establish and implement ethical AI guidelines, and hold the organization accountable for its ethical practices. An AI strategy needs to be built on a solid foundation to survive the strong winds of change.
7 A Simulation Suite for Tackling Applied Reinforcement Learning Challenges
In “Challenges of Real-World Reinforcement Learning”, we identify and discuss nine different challenges that hinder the application of current RL algorithms to applied systems. There are still considerable challenges to deploying RL to real-world products and systems. While the initial set of RWRL suite features and experiments provide a starting point for closing the gap between the current state of RL and the challenges of applied systems, there is still much work to do. Check the code here.
8 Amazon’s ML University is making its online courses available to the public
Great news for FOSS ML of course. AWS is a larger player so this is a good step in the open direction.
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9 Keep Up: The first book written by an AI
GPT-3 is technology to follow. Great experiment. Most of the machine learning world was of the opinion that language was going to be one of the last problems we solve. Check the current outcome…
10 Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?
Since 2009, the deep learning revolution, which was triggered by the introduction of ImageNet, has stimulated the synergy between Machine Learning (ML)/Deep Learning (DL) and Software Engineering (SE). Critical reviews have emerged that suggest that ML/DL should be used cautiously. But ML is the holy grail for SE. So interesting article for every software engineer who is afraid for losing his job not only due to covid-19, but also due to progress made with DL.
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The FOSS Machine Learning News Blog is a brief overview of open machine learning news from all over the world. Free and Open machine learning means that everyone must be able to develop, test and play and deploy machine learning solutions. Read and share the FOSS ML Guide! And remember:You are invited to join the Free and Open Machine Learning open collaboration project.