machine learning

1 posts
Machine learning to find a recipe for a baked good that’s half cake and half cookie

Last year, around the time when people were baking a lot of things, Sarah Robinson used machine learning to find a recipe for a “cakie”: Like many people, I’ve been entertaining myself at home by baking a ton and talking about my sourdough starter as if it were a real person. I’m pretty good at following recipes, but I decided I wanted to take things one step further and understand...

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Blob Opera is a machine learning model you can make music with

David Li, in collaboration with Google Arts and Culture, made a fun experiment to play with: We developed a machine learning model trained on the voices of four opera singers in order to create an engaging experiment for everyone, regardless of musical skills. Tenor, Christian Joel, bass Frederick Tong, mezzo‑soprano Joanna Gamble and soprano Olivia Doutney recorded 16 hours of singing. In the experiment you don’t hear their voices, but...

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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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