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“This is Sticking with Them:” Professor Explores Benefits of Model-Based Learning Nov 9, 2019 highlights & learning & university & systems

Through model-based learning, students use diagrams as a way to think about and reason with systems—and to think about how complex systems interact and change.

Model-based learning” seems like a reframing of classic teaching practices, but it’s nonetheless a powerful reframing. Emphasizing the model—and encourage students to test and iterate their models—is catchy. It’s also deliberately organizational—it requires students to organize and structure their thinking about a given system, often visually.

There is a seemingly myriad of terms to describe people who interact with models Nov 19, 2018 highlights & learning & science There is a seemingly myriad of terms to describe people who interact with models. Just a few terms that are currently in usage include researchers, data scientists, machine learning researchers, machine learning engineers, data engineers, infrastructure engineers, DataOps, DevOps, etc. Both Miner and Presser commented upon and agreed that before any assignment of any term, the work itself existed previously. Presser defines data engineering as embodying the skills to obtain data, build data stores, manage data flows including ETL, and provide the data to data scientists for analysis. Presser also indicated that data engineers at large enterprise organizations also have to be well versed in “cajoling” data from departments that may not, at first glance, provide it. Miner agreed and indicated that there is more thought leadership around the definition of data science versus data engineering which contributes to the ambiguity within the market. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/
Van Horn and Perona open with a brilliant one-liner: the world is long-tailed Oct 4, 2018 highlights & learning & global Van Horn and Perona open with a brilliant one-liner: the world is long-tailed. The diagram above shows analysis from Deep Learning Analytics, the #2 team placing in the iNaturalist 2018 competition. Part of that challenge was how many of the classes to be learned had few data points for training. That condition is much more “real world” than the famed ImageNet – with an average of ~500 instances per class – which helped make “deep learning” a popular phrase. The aforementioned sea change from Lange, Jonas, et al., addresses the problem of reducing data demands. I can make an educated guess that your enterprise ML use cases resemble iNaturalist more than ImageNet, and we need to find ways to produce effective models which don’t require enormous labeled data sets. — https://blog.dominodatalab.com/themes-and-conferences-per-pacoid-episode-2/
At AI SF, Danny Lange presented how to train puppies: “On the road to artificial general intelligence” — game simulations Unity3D plus reinforcement learning used to train virtual puppies to play “fetch” and other skills. Oct 4, 2018 highlights & learning

At AI SF, Danny Lange presented how to train puppies: On the road to artificial general intelligence” — game simulations Unity3D plus reinforcement learning used to train virtual puppies to play fetch” and other skills. Building on this, Danny described several forms of learning inspired by biology, which go beyond deep learning. He showed examples of virtual puppies for:

Imitation Learning: e.g., see https://bit.ly/2zvYH51 (start 0:15)
Curriculum Learning: start with an easy problem, then make learning challenges progressively harder
Curiosity-driven Exploration: gets beyond problems which random exploration would never reach, i.e., agents don’t get stuck in a room (saddle points) because they want to explore other rooms

https://blog.dominodatalab.com/themes-and-conferences-per-pacoid-episode-2/
Interpretability is needed when auxiliary criteria are not met and questions about bias, trust, safety, ethics, and mismatched objectives arise Sep 25, 2018 highlights & learning Interpretability is needed when auxiliary criteria are not met and questions about bias, trust, safety, ethics, and mismatched objectives arise. Kim and Doshi-Velez “argue that the need for interpretability stems from incompleteness in the problem formalizing, creating a fundamental barrier to optimization and evaluation” for example, “incompleteness that produces some kind of unquantified bias”. — https://blog.dominodatalab.com/make-machine-learning-interpretability-rigorous/
Education is delivery; learning is discovery. Sep 8, 2018 highlights & education & learning
Learning how to live sustainably in an always-online society is mostly about learning where your limits are, and learning how much connection you can handle before it’s time to withdraw Sep 5, 2018 highlights & learning Learning how to live sustainably in an always-online society is mostly about learning where your limits are, and learning how much connection you can handle before it’s time to withdraw. Knowing when to log off is the main skill to master — and this applies IRL, too, because while it’s easy to understand why you feel drained after random accounts brigade your Twitter mentions, it’s harder to recognize when the people around you become draining themselves. But more often it’s simpler than that: the fact that there’s a society-wide expectation to be constantly available means there’s no escape from the insistent pings and buzzes that accompany human connection, from friends to enemies to lovers and everything in between. And now we have more — and more persistent — friendships than ever, mediated by Facebook and Twitter and Instagram, which means that the alerts come more frequently than ever. The human brain has not evolved as quickly as its technology has; we are not built for this much connection, though we have, by and large, adapted. — https://www.theverge.com/2018/9/2/17805138/finding-silence-online-is-difficult-but-the-pursuit-is-worthwhile
NAF adopted the Success Factors platform which includes a robust learning management system to serve as their technological solution for myNAFTrack Aug 15, 2018 highlights & learning NAF adopted the Success Factors platform which includes a robust learning management system to serve as their technological solution for myNAFTrack. Like other platform networks New Tech Network and Summit Learning, NAF members will benefit from college and career ready modules, as well as networking opportunities through the platform’s search feature and groups. — https://www.forbes.com/sites/tomvanderark/2018/08/13/career-academy-giant-naf-gets-an-upgrade-expands-access-to-work-based-learning/
No one has ever asked for release from research. Jul 18, 2017 highlights & learning

In universities, this requires a rebalancing from the current emphasis on research to teaching. (A dean at one of Ontario’s more highly ranked universities told me recently that virtually every day there is a request to grant teaching release to a professor, yet no one has ever asked for release from research.)” — http://blog-en.heqco.ca/2017/07/harvey-p-weingarten-the-evolution-of-learning-outcomes-now-comes-the-exciting-part/

The huge triumph of [deep learning] has been figuring out that as long as you can pose a problem in a differentiable way and you can obtain a sufficient amount of data, you can efficiently tackle it with a function approximator that can be optimized with first order methods - from that, flows everything Jun 20, 2016 learning & highlights The huge triumph of [deep learning] has been figuring out that as long as you can pose a problem in a differentiable way and you can obtain a sufficient amount of data, you can efficiently tackle it with a function approximator that can be optimized with first order methods - from that, flows everything. — Great concise description of what deep learning actually is from Fede_V on Hacker News (e.g., not a catch-all big-data-will-save-the-world thing).
The university of the 21st century has three roles: to create knowledge, to share that knowledge, and to use and apply it. → Nov 3, 2015 education & learning
The digital economy doesn’t solve everything. → Nov 2, 2015 social & public & learning
Courses → Oct 14, 2015 science & learning
Does sleep-learning really work? → Oct 2, 2015 learning
Nobody is average, every student deserves personalized learning — Changemaker Education → Aug 23, 2015 education & learning
Educating Data | MIT Technology Review → Aug 12, 2015 education & learning & futures & tech
You might be using Twitter wrong (because I was) → Jul 9, 2013 social & learning
Contemplative Education and Extra/Co-Curricular Programming → Aug 15, 2012 education & learning & articles