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UDL in Action in College Online Courses
- With the multiple means of presentation, engagement and expressions
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Jobs for the future
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http://dataaspirant.com/2014/09/19/supervised-and-unsupervised-learning/
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Google and Coursera launch a new machine learning specialization | TechCrunch
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The Risk of Machine-Learning Bias (and How to Prevent It)
"But while machine-learning algorithms enable companies to realize new efficiencies, they are as susceptible as any system to the “garbage in, garbage out” syndrome. In the case of self-learning systems, the type of “garbage” is biased data. Left unchecked, feeding biased data to self-learning systems can lead to unintended and sometimes dangerous outcomes"
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The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning
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you can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.
So instead of hard coding software routines with specific instructions to accomplish a particular task, machine learning is a way of “training” an algorithm so that it can learnhow.
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Want to reach the world's poorest? Design for dumb phones | Devex
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What Is The Difference Between Artificial Intelligence And Machine Learning?
- Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.
- Machine Learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. In some cases, they can even compose their own music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece
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What Is The Difference Between Artificial Intelligence And Machine Learning?
- Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
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- In Brazil, researchers used a Mixed Initiative Social Media Analysis (MIXMA) to study the relationship between social protest and citizen trust based on sentiment analysis of twitter activity during the 2014 World Cup
- In a study of policy options to reduce criminality among ex-combatants in Colombia, a ML ensemble was used to improve the credibility of propensity score matching by allowing for inclusion of 100+ control variables
- In India, ML algorithms on tax data can more systematically identify ‘suspicious’ firms to target for physical audits
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Machines fighting poverty? – CEGA – Medium
- If, like most smallholder farmers in the developing world, you live in a data sparse environment (without smartphones, market surveys, and government census records) you’re literally off the radar. So what can AI tell us about the life of a person living in extreme poverty? A lot, as it turns out.
- We’re learning which economic measurement challenges are amenable to automation, and which aren’t.
- there are some thorny technical and ethical challenges to address, and a need for infrastructure that supports AI for “public good.”
- AI is a valuable new part of the policy toolkit. One durable outcome of the AI revolution, from an economic development perspective, will be its impact on economics and other policy-relevant research
- In some cases it is improving the credibility and performance of traditional estimators, which will have long-term benefits for evidence-based policy-making
- Algorithms can attenuate social biases, not just reinforce them.
- These biases, drawn from training sets, are faithfully reflected in the models we build.
- We need more research to characterize bias in training data and explore how it affects the performance of learning models.
- Over time, we should be able to design models that reduce bias, rather than exacerbate it.
- AI can make government policies and practices more transparent. While decisions driven by algorithms are often opaque, they can be more traceable than the millions of disaggregated, undocumented decisions made by individual judges, social workers, and program planners.
- Domain expertise is key, and it’s still missing from too many AI-for-good projects. Social scientists with development subject matter expertise are more likely to think critically about the ground truth data used to train models.
- These interdisciplinary intersections are needed for the design of robust, reliable algorithms that can be trusted to deliver essential government services.
- The world is dynamic. Learning models need to update.
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Microcredentials Transforming MOOC Positioning and Higher Education Models | The EvoLLLution
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The Mainstreaming of Alternative Credentials in Postsecondary Education | The EvoLLLution
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Microcredentials, MicroMasters, and Nanodegrees: What’s the Big Idea? | The EvoLLLution
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How Everyone Benefits from Badging: A Guide to Mainstreaming Digital Credentials | The EvoLLLution
Saturday, June 9, 2018
Weekly Sporto bookmarks (weekly)
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