machine learning

4 posts
Machine learning to erase penis drawings

Working from the Quick, Draw! dataset, Moniker dares people to not draw a penis: In 2018 Google open-sourced the Quickdraw data set. “The world’s largest doodling data set”. The set consists of 345 categories and over 15 million drawings. For obvious reasons the data set was missing a few specific categories that people enjoy drawing. This made us at Moniker think about the moral reality big tech companies are imposing...

0 0
Machine learning to steal baseball signs

Mark Rober, who is great at explaining and demonstrating math and engineering to a wide audience, gets into the gist of machine learning in his latest video: Tags: baseball, machine learning, Mark Rober

0 0
Runway ML makes machine learning easier to use for creators

Machine learning can feel like a foreign concept only useful to those with access to big machines. Runway ML aims to make machine learning easier to use for a wider audience, specifically for creators. It provides a click-and-drag interface that lets you link algorithms, import datasets, and most importantly, experiment. Looks like fun. Give it a go. Tags: machine learning

0 0
Myth of the impartial machine

In its inaugural issue, Parametric Press describes how bias can easily come about when working with data: Even big data are susceptible to non-sampling errors. A study by researchers at Google found that the United States (which accounts for 4% of the world population) contributed over 45% of the data for ImageNet, a database of more than 14 million labelled images. Meanwhile, China and India combined contribute just 3% of...

0 0
Algorithms to fix underrepresentation on Wikipedia

Wikipedia is human-edited, so naturally there are biases towards certain groups of people. Primer, an artificial intelligence startup, is working on a system that looks for people who should have an article. It’s called Quicksilver. We trained Quicksilver’s models on 30,000 English Wikipedia articles about scientists, their Wikidata entries, and over 3 million sentences from news documents describing them and their work. Then we fed in the names and affiliations...

0 0
Robot arm seeks out Waldo, using machine learning

The camera on the slightly creepy arm takes a picture of the pages in the book, the software uses OpenCV to extract faces, and the faces are passed to Google Auto ML Vision comparing the faces to a Waldo model. The result: There’s Waldo. Tags: machine learning, robot, vision, Waldo

0 0
Visual introduction to bias in machine learning

A few years ago, Stephanie Yee and Tony Chu explained the introductory facets of machine learning. The piece stood out because it was such a good use of the scrollytelling format. Yee and Chu just published a follow-up that goes into more detail about bias, intentional or not. It’s equally worth your time. (Seems to work best in Chrome.) Tags: machine learning, scrollytelling

0 0
Machine learning to estimate when bus and bike lanes blocked

Frustrated with vehicles blocking bus and bike lanes, Alex Bell applied some statistical methods to estimate the extent. Sarah Maslin Nir for The New York Times: Now Mr. Bell is trying another tack — the 30-year-old computer scientist who lives in Harlem has created a prototype of a machine-learning algorithm that studies footage from a traffic camera and tracks precisely how often bike lanes are obstructed by delivery trucks, parked...

0 0
Bot or Not: A Twitter user classifier

Michael W. Kearney implemented a classifier for Twitter bots. It’s called botornot: Uses machine learning to classify Twitter accounts as bots or not bots. The default model is 93.53% accurate when classifying bots and 95.32% accurate when classifying non-bots. The fast model is 91.78% accurate when classifying bots and 92.61% accurate when classifying non-bots. Overall, the default model is correct 93.8% of the time. Overall, the fast model is correct...

0 0
How machines learn

Hearing about machine learning and algorithms a lot recently and not sure what that means? CGP Grey explains: Tags: algorithm, explainer, machine learning

0 0