When I was studying philosophy in university I would find works like Henry Kyburg's Recent Work in Inductive Logic to be invaluable (sadly behind a paywall today). Today what I am seeing are literature reviews conducted (in the first instance) by search and filtering algorithms. These seem to me a clumsy and inaccurate way to survey research data (even if they are all the rage in the social sciences). I prefer to judge the relevance of a paper personally. But there's so. many. papers. So in a world without Henry Kyburg, something like this article summarizer might be useful. It's a technology that has been around for 20 years (I remember NRC reserachers introducing me to it when I first started with them) but it's finally coming into its own.
This is a nice resource from Google with some really practical advice on how to design for human interactions with AI. For example, here's some really solid advice: "AI-powered systems can adapt over time. Prepare users for change—and help them understand how to train the system." The Guide also covers user needs, data sources, feedback and coltrol, explanability, and how to handle AI errors, among other topics.
After making the point that "innovation is fifteen different things to fifteen different people," Harold Jarche offers a nice overview of the concept of innovation and how it has evolved in recent years. At the core, though, this remains true (and contrary to the perspective of managerialists everywhere): "Innovation is like democracy, it needs people to be free within the system in order to work. Empowering knowledge artisans to use their own cognitive tools creates an environment of experimentation, instead of adherence to established processes.... There is no innovation assembly line." So many managers (including my own) don't understand this.
Class blogging hasn't disappeared; it has become mainstream. This list is I am sure nothing like a complete list of all the class blocgs around the world. But it's a nice selection of them, and there's a space for you to add your own to the list. The spreadsheet is dividied by grade, subject and type.
Most of us aren't building machine learning interfaces in educational technology, but it's still useful to stay up-to-date with the terminology (a short list begins this article) and best practices for machine learning implementations. The guide is also a helpful checklist for evaluating machine learning proposals. Finally, if you are implementing machine learning, it's best to "do machine learning like the great engineer you are, not like the great machine learning expert you aren’t."
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Copyright 2019 Stephen Downes Contact: email@example.comThis work is licensed under a Creative Commons License.