Today a future without schools. Instead of gathering students into a room and teaching them, everybody learns on their own time, on tablets and guided by artificial intelligence.
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In this episode we talk to a computer scientist who developed an artificially intelligent TA, folks who build learning apps, and critics who wonder if all the promises being made are too good to be true. What do we gain when we let students choose their own paths? What do we lose when we get rid of schools?
Illustration by Matt Lubchansky.
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It's fascinating to see and hear the distinctive personalities of the different sorting algorithms in this 5-minute video. My favorite is the bogo sort at the end, which sounds the best but seems to do a poor job of sorting
Visualization and "audibilization" of 15 Sorting Algorithms in 6 Minutes.
Sorts random shuffles of integers, with both speed and the number of items adapted to each algorithm's complexity.
The algorithms are: selection sort, insertion sort, quick sort, merge sort, heap sort, radix sort (LSD), radix sort (MSD), std::sort (intro sort), std::stable_sort (adaptive merge sort), shell sort, bubble sort, cocktail shaker sort, gnome sort, bitonic sort and bogo sort (30 seconds of it).
Sorting videos are popular on YouTube. I like these ones that show robotos competing to sort balls from darkest to lightest:
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Snowshoe spam has a "small footprint" -- it is sent is small, semi-targeted batches intended to sit below the trigger threshold for cloud-email spam filters, which treat floods of identical (or near-identical) messages as a solid indicator of spam.
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Cops covertly buy stolen cards from underground sites to figure out where they came from, and so these sites implement security measures that try to figure out whether a purchaser is an undercover cop, and refuse to sell to them if they trip a positive result.
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MIT researchers have demonstrated an algorithm that analyzes photos of a real world scene and then generates an incredibly-effective camouflage pattern to wrap objects later placed in that location. From MIT News:
According to Andrew Owens, an MIT graduate student in electrical engineering and computer science and lead author on the new paper, the problem of disguising objects in a scene is, to some degree, the inverse of the problem of object detection, a major area of research in computer vision.
"Often these algorithms work by searching for specific cues — for example they might look for the contours of the object, or for distinctive textures." Owens says. "With camouflage, you want to avoid these cues — you don't want the object's contours to be visible or for its texture to be very distinctive. Conceptually, a cue that would be good for detecting an object is something that you want to remove.”
"Custom Camouflage" Read the rest
Earlier today, in a feature on the science behind gun policies
, I told you about how difficult it is to get reliable answers that pinpoint exactly what helps society and what hurts it. Models — computer algorithms that help us understand how complex systems work — play a role in this, but the ones used for gun research aren't very good yet. In fact, that's true about a lot of sociology fields, write the editors of the Get Stats blog. In general, our knowledge of how society works lags far behind our knowledge of the natural world
. Can that ever be fixed? Some scientists think so. Read the rest
Even when your eyeballs look
still, they aren't still. Every time your heart beats, it creates almost imperceptible changes in your skin tone as blood moves through your body. Tall buildings and construction cranes wobble slightly in the wind, even though our eyes can't usually catch them at it. Now, a team at MIT has figured out how to spot these small movements using a computer program that goes through video frame-by-frame and pixel-by-pixel, amplifying minute changes in color and motion and making them visible to us. The New York Times' Bits blog has a video with some awesome demonstrations of the system
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