AI-generated candy hearts

Continuing her annual tradition, Janelle Shane trained various AI models to generate two-word-all-caps love messages for those chalky Valentine’s Day candy hearts. So deep. So profound.

See also Shane’s experiment with generating hearts for somewhat creepy results, as AI often likes to do.

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Living in Data

I’m also looking forward to Jer Thorp’s Living in Data, which comes out later this year but is available for pre-order now:

In this provocative book, Thorp brings his work as a data artist to bear on an exploration of our current and future relationship with data, transcending facts and figures to find new, more visceral ways to engage with data. Threading a data story through hippo attacks, glaciers, and school gymnasiums; around colossal rice piles and over active mine fields, Living in Data keeps humanity front and center. Thorp reminds us that the future of data is still wide open; that there are stories to be told about how data can be used, and by whom. Accompanied by informative and poetic illustrations, Living in Data not only redefines what data is, but re-imagines how it might be truly public, who gets to speak its language, and how, using its power, new institutions and spaces might be created to serve individuals and communities. Timely and inspiring, this book gives us a path forward: one where it’s up to all of us to imagine a more just and participatory data democracy.

When I started FlowingData, Statistics and data almost always seemed highly technical and accessible to only a few. A few years later, understanding data was like a novelty that more people wanted to play with but still didn’t quite know the implications of what they were looking at. These days, spurred on by last year especially, interpreting data is an essential skill.

Over the next few years, Thorp’s perspective on how we live with these new streams, individually and as a society, will only grow more important.

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The Art of Statistics

While we’re on the topic of Statistics books for the general public, David Spieglhalter’s The Art of Statistics: How to Learn from Data is also on my reading list.

In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems — it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and — perhaps even more importantly — we learn how to responsibly interpret the answers we receive.

I was waiting for the book to come to North America, and apparently it did in 2019. I’m so behind in my reading, but I declare 2021 as the year I take my attention span back.

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The Data Detective

Tim Harford has a new book coming out tomorrow called The Data Detective: Ten Easy Rules to Make Sense of Statistics.

Today we think statistics are the enemy, numbers used to mislead and confuse us. That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics—we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often “the only way of grasping much of what is going on around us.” If we can toss aside our fears and learn to approach them clearly—understanding how our own preconceptions lead us astray—statistics can point to ways we can live better and work smarter.

Added to the list.

If you’re outside North America, look for How To Make The World Add Up. They’re the same book.

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Even with vaccine, probably shouldn’t rush into easing up on restrictions

With vaccines, we might be tempted to jump back into “normal” life before it’s really safe. The New York Times reports on why waiting until March instead of February might be the way to. This is based on estimates from Columbia University researchers, and you can read the preprint here (pdf) by Jeffrey Shaman et al.

We’ve come this far already…

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Random number generation with lava lamps

Tom Scott explains how Cloudflare uses a wall of lava lamps to generate random numbers. A video camera is pointed at the wall, and the movement in the lamps plus noise from the video provides randomness, which is used to secure websites.

Even though computers can do many things on their own, they still need help from the physical world for true unpredictability. The robot overlords aren’t here yet. [via kottke]

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Not so likely life of The Simpsons

For The Atlantic, Dani Alexis Ryskamp compares the financials of The Simpsons against present day medians, arguing that the fictional family’s lifestyle is no longer attainable:

The purchasing power of Homer’s paycheck, moreover, has shrunk dramatically. The median house costs 2.4 times what it did in the mid-’90s. Health-care expenses for one person are three times what they were 25 years ago. The median tuition for a four-year college is 1.8 times what it was then. In today’s world, Marge would have to get a job too. But even then, they would struggle. Inflation and stagnant wages have led to a rise in two-income households, but to an erosion of economic stability for the people who occupy them.

Someone should take this a step further and look at distributions and time series to show the shift, with The Simpsons as baseline.

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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 the science behind what differentiates a cake from a bread or a cookie. I also like machine learning so I thought: what if I could combine it with baking??!

Robinson provides the final recipe at the end, so first, I need to try this recipe. Second, what other foods and beverages can this apply to?

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Neural network creates images from text

OpenAI trained a neural network that they call DALL·E with a dataset of text and image pairs. So now the neural network can take text input and output random combinations of descriptors and objects, like a purse in the style of Rubik’s cube or a teapot imitating Pikachu.

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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 the machine learning model’s understanding of what opera singing sounds like, based on what it learnt from them.

So smooth. So blobby.

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