It’s been a decade since the first Iron Man movie, and some 30 superhero characters later, we arrive at a two-parter Avengers finale. But maybe you lost track of everything that happened leading up to this point. Sonia Rao and Shelly Tan for the Washington Post got you covered with a filterable timeline. Focus on specific stories, characters, and franchises. Select “block spoilers” in case you still plan to watch something.

I used to watch all of the Marvel movies, but then I had kids. I’ve seen one in five years. So this is right up my alley.

When it comes to robots and love, the concept typically deteriorates to subservient tools to satisfy male fantasies. Creative technologist Fei Lu aims for a more complex relationship with Gabriel2052:

Creating Gabriel2052 is obviously technically challenging, but it’s ultimately a process within my control. He will become something—someone—I can form a lifelong bond with. Through bringing Gabriel2052 to life, I am investigating and confronting the ways in which technology and society create both harmful and uplifting narratives; the ones we’ve become complicit in during our search for love and understanding from others, and the world at large.

So instead of a robot that is purely there to serve, Lu explores a robot that’s a bit closer to human and driven by her emotional needs (and an ex-boyfriend’s text messages) — because inevitably, our relationship with robots will impact our relationships with real people.

Sisi Wei for ProPublica and Nick Fortugno of Playmatics made a game to provide a feeling of what it’s like for someone who needs escape from their home.

Based on the real case files of five asylum seekers from five countries and interviews with the medical and legal professionals who evaluate and represent them, The Waiting Game is an experimental news game that lets you walk in the shoes of an asylum seeker, from the moment they choose to come to the United States to the final decision in the cases before an immigration judge.

Take your time with this one, and use your headphones.

In the game format, I felt more engrossed in the individual stories than I think if it were a linear profile story.

Janelle Shane, who likes to play with output from neural networks, teamed up with knitters in a discussion forum to produce abstract designs. Shane generates the knitting patterns, and the knitters bring the computer output to life. She calls the project SkyKnit.

The neural network produces slightly flawed instructions, but the knitters can figure things out:

Knitters are very good at debugging patterns, as it turns out. Not only are there a lot of knitters who are coders, but debugging is such a regular part of knitting that the complicated math becomes second nature. Notation is not always consistent, some patterns need to be adjusted for size, and some simply have mistakes. The knitters were used to taking these problems in stride. When working with one of SkyKnit’s patterns, GloriaHanlon wrote, “I’m trying not to fudge too much, basically working on the principle that the pattern was written by an elderly relative who doesn’t speak much English.”

Love the meeting between people and computer. [via The Atlantic]

Neural networks have shown usefulness with a number of things, but here is an especially practical use case. Chris Rodley used neural networks to create a hybrid of a dinosaur book and a flower book. The world may never be the same again.

When watching baseball on television, we get the benefit of seeing whether a pitch entered the strike zone or not. Umpires go by eye, and intentional or not, they tend towards finishing a game over extra innings. Michael Lopez, Brian Mills, and Gus Wezerek for FiveThirtyEight:

The left panel shows the comparative rate of strike calls when, in the bottom of an inning in extras, the batting team is positioned to win — defined as having a runner on base in a tie game — relative to those rates in situations when there’s no runner on base in a tie game. When the home team has a baserunner, umps call more balls, thus setting up more favorable counts for home-team hitters, creating more trouble for the pitcher, and giving the home team more chances to end the game.

I doubt the shift is on purpose, but it’s interesting to see the calls go that way regardless. Also, from a non baseball-viewer, why isn’t there any replay in baseball yet?

[arve url=”https://www.youtube.com/watch?v=DYaq2sWTWAA” /]

Abstract: The Art of Design kept popping up on my Netflix recommendations list for the past several months. I ignored it though, because I’m tired of the heavy-handed design shows talking about how design is life and life is design, etc. Also, I probably spend more time flipping through what I can watch than actually watching anything.

In any case, for some reason I hit play and was happy to see the first episode with Christoph Niemann. Niemann is known for his whimsical, visual storytelling, and his process was fun to watch. Recommended.

And if Netflix isn’t your jam, this talk by Niemann is also good:

[arve url=”https://www.youtube.com/watch?v=dG-ZXiYtLy8″ /]

Look from the above at the shapes and geometry we use for cities, blocks, roads, fields, and the like, and you start to get the repeating patterns. Páraic McGloughlin and Pearse McGloughlin highlight these patterns and their connectedness in Arena by stringing together Google Earth images.

[arve url=”https://vimeo.com/259989412″ /]

Learning algorithm steps can be a challenge when viewed only through code or words. So Sándor P. Fekete, Sebastian Morr, and Sebastian Stiller put together IDEA. The collection of illustrations describes common programming algorithms, such as Quicksort, in the style of IKEA furniture assembly instructions. Allen wrench not required. [via kottke]

Hans Rosling was able to build excitement around data like no other. Truth and progress was his rally cry. Before he died, he was working on a book with his Gapminder co-founders Anna Rosling Rönnlund and Ola Rosling. The book, Factfulness: Ten Reasons We’re Wrong About the World—and Why Things Are Better Than You Think, is out now. Ordered and looking forward to it.

The colors you choose to visualize data can completely shift what you convey to a reader. With an ominous color palette, a graphic meant to be light and fun comes off the wrong way. Or the other way around. You wouldn’t use Comic Sans for your résumé (right…?), so choose colors that fit the topic. Viz Palette, made by Elijah Meeks and Susie Lu, aims to make the choosing part easier.

It’s still up to you to figure out the right overall scheme, but Viz Palette takes care of the stuff in between, such as designing for color blindness and perceptually evenly-spaced shades. It also includes a “color report” that points out shades that might look the same in various situations.

While there are many color-picking tools (I typically stick to four.), they are often too simple, overly-complicated, or research-centric. This one seems to strike a good balance for practicality.

This comic from Saturday Morning Breakfast Cereal speaks to me.

You can quantify all the things to look at said things more objectively. However, there are always other things that cannot be quantified and in real life, they are often intertwined with the numeric portion of the data. Split apart the quantitative and qualitative, and you end up making all decisions based on stab percentage.

From Joshua Stevens at the NASA Earth Observatory:

But over the longer term, climate change is causing spring to begin earlier and earlier across the United States. These maps reveal just how much earlier spring is arriving in National Parks across the country. The data were published in 2016 by ecologists from the National Park Service, working in collaboration with colleagues at other agencies and institutions.

Griddy.

Limited to only national parks, the view still provides a good idea. For a model-based view at higher granularity, check out the USA National Phenology Network.

Based on data gathered by the Lunar Reconnaissance Orbiter, NASA pieced together this high-resolution tour of the moon. At the two-minute mark they zoom in on a boulder in the middle of a 100-million-year-old crater, and it blew my mind.

[arve url=”https://youtu.be/nr5Pj6GQL2o” /]

Rafael Irizarry introduces the dslabs package for real-life datasets to teach data science:

[I] try to avoid using widely used toy examples, such as the mtcars dataset, when I teach data science. However, my experience has been that finding examples that are both realistic, interesting, and appropriate for beginners is not easy. After a few years of teaching I have collected a few datasets that I think fit this criteria. To facilitate their use in introductory classes, I include them in the dslabs package.

This is a fun drawing experiment in R by Antonio Sánchez Chinchón. A simple process: convert an image to black and white, sample the black points, and then solve the Traveling Salesman Problem for those points. Draw the resulting path for something like the above.

Grab the R code to try it with your own images and settings.

One way to think about gender pay gap is to imagine women receive the same pay as men each working day until they reach their salary. At some point during the year, women effectively work for free. With a new law that requires companies in Great Britain with 250 or more employees to report pay gap, The Guardian provides a calendar view into the newly reported data that shows the day of the year the free work starts.

The scroller shows companies as you move down the calendar. The information feels less overwhelming than seeing it all at once, and a running counter keeps track of what you already saw.

See also The Guardian’s breakdown by sector.

There are many ways to estimate how similar two cities are — weather, demographics, taxes, etc. Jed Kolko from job site Indeed and Josh Katz for The Upshot used the distribution of job offerings. Just enter your city or a nearby metro, and you get something like this:

I punched in cities I’ve lived in or visited, and the results looked pretty good.

The analysis is based on job postings on Indeed, but I wonder if this would work with Census data. And can we apply this similarity index or some form of it to, say, individuals? Just think of the possibilities, Match.com.