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Mathpix

#artificialintelligence

Mathpix is the world's first technology that can read pictures of your handwritten math. Simply take a photo, crop your equation of interest, and you instantly get solutions and plots.


Point your phone at an equation and Mathpix will solve it

Engadget

The interface looks like any standard camera app: simply drag the on-screen reticle over the equation and the app solves it and provides graph answers where appropriate. More useful is a step-by-step guide offering multiple methods to reach a solution, making this a bona fide educational tool. It uses image recognition to process problems and pings its servers to do the mathematical heavy lifting, so it likely requires an internet connection to work. Mathpix was envisioned by Stanford PhD student Nico Jimenez, who was advised by Stanford grad Paul Ferrell. The app's other developers are high schoolers Michael Lee and August Trollback, which is impressive for an app that claims to be the first to visually recognize and solve handwritten math problems.


What's the difference between analytics and statistics?

#artificialintelligence

Statistics and analytics are two branches of data science that share many of their early heroes, so the occasional beer is still dedicated to lively debate about where to draw the boundary between them. Practically, however, modern training programs bearing those names emphasize completely different pursuits. While analysts specialize in exploring what's in your data, statisticians focus more on inferring what's beyond it. Disclaimer: This article is about typical graduates of training programs that teach only statistics or only analytics, and it in no way disparages those who have somehow managed to bulk up both sets of muscles. In fact, elite data scientists are expected to be full experts in analytics and statistics (as well as machine learning)… and miraculously these folks do exist, though they are rare.


What's the difference between analytics and statistics?

#artificialintelligence

Statistics and analytics are two branches of data science that share many of their early heroes, so the occasional beer is still dedicated to a lively debate about where to draw the boundary between them. Practically, however, modern training programs bearing those names emphasize completely different pursuits. While analysts specialize in exploring what's in your data, statisticians focus more on inferring what's beyond it. Disclaimer: This article is about typical graduates of training programs that teach only statistics or only analytics, and it in no way disparages those who have somehow managed to bulk up both sets of muscles. In fact, elite data scientists are expected to be full experts in analytics and statistics (as well as machine learning)… and miraculously these folks do exist, though they are rare.


Neural Networks in Plain English – Becoming Human: Artificial Intelligence Magazine

#artificialintelligence

It's software, let's be clear on that, it's just an application. Sorry to disappoint you but it's not a physical network of brains floating in slimy green liquid, or an army of cognitively connected robots, it's just an application. In fact, it's often just part of another application, such as Snapchat. A neural network is just a particular type of application that is very good at taking a bunch of data and giving you an answer to a question. One of the most commonly used examples is of course identifying photos with cats in them.