machine learning

2 posts
Red-blue electoral map and the green-gray in satellite imagery

For NYT’s The Upshot, Tim Wallace and Krishna Karra looked at how the red-blue electoral map relates to the green and gray color spectrum in satellite imagery: The pattern we observe here is consistent with the urban-rural divide we’re accustomed to seeing on traditional maps of election results. What spans the divide — the suburbs represented by transition colors — can be crucial to winning elections. It’s part of why...

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Tic-Tac-Toe the Hard Way is a podcast about the human decisions in building a machine learning system

From Google’s People + AI Research team, David Weinberger and Yannick Assogba build a machine learning system that plays Tic-Tac-Toe. They discuss the choices, not just the technical ones, along the way in the ten-part podcast series: A writer and a software engineer engage in an extended conversation as they take a hands-on approach to exploring how machine learning systems get made and the human choices that shape them. Along...

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Park sounds before and during the pandemic

With lockdown orders arounds the world, places that we’re allowed to go sound different. The MIT Senseable City Lab looked at this shift in audio footprint through the lens of public parks: Using machine learning techniques, we analyze the audio from walks taken in key parks around the world to recognize changes in sounds like human voices, emergency sirens, street music, sounds of nature (i.e., bird song, insects), dogs barking,...

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Machine learning to make a dictionary of words that do not exist

Thomas Dimson trained a model to generate words that don’t exist in real life and definitions for said imaginary words. If you didn’t tell me the words were machine-generated, I’d believe a lot of them were actual parts of the English dictionary. Tags: machine learning, Thomas Dimson, words

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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...

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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

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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

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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...

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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...

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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

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