machine learning

14 posts
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

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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...

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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...

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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

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How artificial intelligence can augment our own

There’s another essay on Distill by Shan Carter and Michael Nielsen. They describe and demonstrate how one might use artificial intelligence to augment human intelligence. Our essay begins with a survey of recent technical work hinting at artificial intelligence augmentation, including work on generative interfaces – that is, interfaces which can be used to explore and visualize generative machine learning models. Such interfaces develop a kind of cartography of generative...

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Machine learning demo with your webcam and GIFs

The Teachable Machine from Støj, Use All Five, and Google is a fun experiment that lets you “teach” your computer. Your webcam is used as an input device, and using deeplearn.js, you can make three classifications that change the output. Use different hand gestures, faces, or movements to signal differences, and you can see probabilities change in real-time. It’s hard to believe this stuff runs so smoothly in the browser...

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Machine learning to find spy planes

Last year, BuzzFeed News went looking for surveillance flight paths from the FBI and Homeland Security. Peter Aldhous describes how they did it. They used machine learning — a random forest algorithm to be more specific — to find the spy planes, which as you might expect tended to circle around more than normal flights. Tags: BuzzFeed, flights, machine learning

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Minimizing discrimination in machine learning

From Google Research, a look at how discrimination in machine learning can lead to poor results and what might be done to combat: Here we discuss “threshold classifiers,” a part of some machine learning systems that is critical to issues of discrimination. A threshold classifier essentially makes a yes/no decision, putting things in one category or another. We look at how these classifiers work, ways they can potentially be unfair,...

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Visual collection of bird sounds

Different species of birds make different sounds. However, the sounds are so quick and compressed that it can be tough to pick out what is what. So Kyle McDonald, Manny Tan, and Yotam Mann created a “fingerprint” for each bird song and used machine learning to classify. Through the visual browser, you can play sounds and search for bird types. Similar sounds are closer to each other. Tags: birds, Google,...

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Visual connections between art pieces

This is neat. A Google Arts & Culture Experiment, X Degrees of Separation shows a path of visual connections between two art pieces of your choosing. It’s like Six Degrees of Kevin Bacon but with art, computer vision, and machine learning. Tags: art, Google, machine learning, vision

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