So the question now is: can one teach the core concepts of modern machine learning even to middle schoolers? The first thing I discuss is something that doesn't really need all the fanciness of modern neural-net machine learning: it's recognizing what languages text fragments are from: Kids (and other people) can sort of imagine (or discuss in a classroom) how something like this might work--looking words up in dictionaries, etc. Suffice it to say that after discussing explicitly trained functions like TextRecognize and ImageIdentify, I start discussing "unsupervised learning", and things like clustering in feature space. Wolfram Notebook system that lets us put all these pieces together--all these pieces are critical to making it possible to bring modern machine learning to people like middle schoolers.
Tutoring STEM subjects is financially lucrative; they're in-demand skills, and kids and parents are thinking ahead to college majors. Though it depends on experience, location, and demand, it's not uncommon for STEM tutors to make anywhere from $25 to $75 an hour. Since O'Connor is knowledgeable about the industry, today she's sharing some advice about getting started in the tech tutoring space. That is most simply because a STEM-tutor typically stays with a student for the full amount of time they are working on the subject -- a full school year or even multiple years -- while SAT-prep or other type of test-prep tutors work with students for at most six months prior to a test.
Called SPHERES (Synchronized Position Hold Engage and Reorient Experimental Satellites), these robots have spent about 600 hours participating in an enormous variety of experiments, including autonomous formation flying, navigation and mapping, and running programs written by middle school students in team competitions. But beyond serving as a scientific platform, SPHERES weren't designed to do anything especially practical in terms of assisting the astronauts or flight controllers, and it's time for a new generation of robotic free fliers that's fancier, more versatile, and will be a big help for the humans on the ISS. From the beginning, Astrobee was intended to be much more than a successor to SPHERES: It's a completely new platform, designed from scratch to operate autonomously and safely on board the ISS. Astrobee's computing system has three layers of processors inside: one low level, one mid level, and one high level.
Learning how to create, test, and revise models is a central skill in scientific reasoning. We argue that qualitative modeling provides an appropriate level of representation for helping middle-school students learn to become modelers. We describe Vmodel, a system we have created that uses visual representations and that enables middle-school students to create qualitative models. We discuss the design of the visual representation language, how Vmodel works, and evidence from school studies that indicate it is successful in helping students.