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.
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.
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: Read the rest
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. Read the rest
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. Read the rest
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.”