From the article this seems much less bad than the headline might imply. This idea predates trump and it does not eliminate scientific review of proposals. That said, whether the cost savings itself is worth what might be a diminishment in review quality is hard to say. I can only comment from the NSF side of things to say that peer review of proposals is a mixed bag and will unavoidably run into human error and individual predispositions regarding scientific importance so maybe this isn't a bad approach to try.
There have been several of these deep dives into manipulated speed runs and they seldom disappoint. Since we are on HN, I'll mention another mathy one that was summarized by Matt Parker: https://www.youtube.com/watch?v=8Ko3TdPy0TU
That's good advice in that context and speaks to the difference in standards of evidence between normal expository or persuasive writing in school and scientific writing. In a scientific paper if you assert something it's important to flag to the reader what kind of claim this is, eg something demonstrated by other research, something that directly follows from your evidence, or something that could possibly be true given prior knowledge and your evidence.
This is good stuff but I'll say that it isn't as comprehensive as all that. These studies and findings are almost entirely focused on simple recall knowledge (isolated facts, vocabulary). That's an important part of learning certainly and it informs research on learning higher order concepts but it's not the full story.
Just to name one example, folks might look into research on conceptual change theory (eg Chi or Posner). This theory helps explain why a concept like electricity is so challenging to learn. The reason, in brief, being that naive conceptions make a category error and think of electricity as a thing rather than a process. And this theory then informs instructional practice. Specifically, teachers should be aware of difficult concepts and should design activities that force students to confront the contradictions between their naive models and more accurate/complete ones.
Mickie Chi also has fascinations research on active learning (ICAP) and related work on the effectiveness of peer learning.
Thanks for your example of the category error here. Are there other cases though? For example, sometimes I doubt my understanding of gradient descent because it is very hard to implement in code on data and show error reducing over time (writing from scratch). But in some examples of a few nodes I can calculate it perfectly. I can do the Maths manually.
What error might I be making? For the future, is there a list of types of errors?
This is gonna be me if or when my quite functional dumb LG from 2012 ever gives up the ghost. I just don't see the appeal of smart TVs when that functionality can be outsourced to a cheaper modular device.
I think the author probably exaggerates her point a bit (fair enough; she's an opinion writer), but I do find it appealing to imagine that OpenStreetMap would have broad buy-in and would lead to more innovation and diversity in the digital maps marketplace.
OpenStreetMap does not provide native apps, but there are plenty of (navigation) apps that provide routing based on OpenStreetMap data, my favorites being https://www.magicearth.com/ for car navigation, https://cycle.travel/ for bicycle touring and https://organicmaps.app/ for everything else (also completely offline).
It is one of my favorites. I actually came to it via "where wizards stay up late", which I would also recommend although it doesn't have the depth and insight of dream machine.
Wizards for me falls flat (kinda like "soul of a new machine" that's often recommended but which I found a complete waste of time) while the Dream Machine, Dealers of Lightning [1] and Norbert Wiener's biography [2] are all essential reading.
"The Soul of a New Machine" is not so much a foundational book but a snapshot of the computer industry and might be more interesting to someone who had no idea of the industry as I did when it was published in 1981. For me it was a fascinating glimpse into the tech industry that I would soon join, albeit in software not hardware.
Yeah. I think if I read Dream Machine first I would have enjoyed Wizards less. But some of the history was new to me which meant that I enjoyed Wizards for those aspects and then Dream Machine for the detailed look into why it happened.
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