Show all the data at once so that you can see a full trend efficiently, but show a bit at a time and show how the data builds.

Joanie Lemercier used a grid of spinning paddles that turn with the wind. Collectively, they show the flows through the air in real-time.

It reminds me of a digital map that used a similar geometry to show wind patterns across the United States.

Alexander Miller wrote a “fable of emergence” that combines Conway’s Game of Life with Pandora’s Box.

Conway’s game grew on Pandora the more she played. Although the rules of the game were relatively straightforward, it was surprisingly difficult to predict the next generation from the previous. Something was hidden within this deceptively simple format. The rules formed a subterranean structure of which she could only see the surface.

It’s animated with a sprinkle of interaction to make sure you’re paying attention.

It continues to get easier to take someone’s face and put that person in compromising situations. For The Markup, Mariel Padilla reports on states trying to catch up with the fast-developing technology.

Carrie Goldberg, a lawyer who has been representing victims of nonconsensual porn—commonly referred to as revenge porn—for more than a decade, said she only started hearing from victims of computer-generated images more recently.

“My firm has been seeing victims of deepfakes for probably about five years now, and it’s mostly been celebrities,” Goldberg said. “Now, it’s becoming children doing it to children to be mean. It’s probably really underreported because victims might not know that there’s legal recourse, and it’s not entirely clear in all cases whether there is.”

The internet is going to get very weird and very confusing, especially for those who can’t fathom how a photo, a video, or audio could be fake when it seems so real. Scammers’ imaginations must be running wild these days.

OpenAI previewed Voice Engine, a model to generate voices that mimic, using just a 15-second audio sample:

We first developed Voice Engine in late 2022, and have used it to power the preset voices available in the text-to-speech API as well as ChatGPT Voice and Read Aloud. At the same time, we are taking a cautious and informed approach to a broader release due to the potential for synthetic voice misuse. We hope to start a dialogue on the responsible deployment of synthetic voices, and how society can adapt to these new capabilities. Based on these conversations and the results of these small scale tests, we will make a more informed decision about whether and how to deploy this technology at scale.

They provide worthwhile use cases, such as language translation and providing a voice to those who are non-verbal, but oh boy, the authenticity of online things is going to get tricky very soon.

For Knowing Machines, an ongoing research project that examines the innards of machine learning systems, Christo Buschek and Jer Thorp turn attention to LAION-5B. The large image dataset is used to train various systems, so it’s worth figuring out where the dataset comes from and what it represents.

As artists, academics, practitioners, or as journalists, dataset investigation is one of the few tools we have available to gain insight and understanding into the most complex systems ever conceived by humans.

This is why advocating for dataset transparency is so important if AI systems are ever going to be accountable for their impacts in the world.

If articles covering similar themes have confused you or were too handwavy, this one might clear that up. It describes the system and steps more concretely, so you finish with a better idea of how systems can end up with weird output.

Alasdair Rae outlines the basics of visualizing basketball shot data with QGIS, an open-source software package typically used for geographic maps. Even if you’re not into basketball, sports data can be fun to poke at because it’s comprehensive and usually covers a good range of time and categories.

I collect visualization tools and learning resources and then round them up at the end of each month. Here’s the good stuff for March.

Satellite imagery on its own can be limited in what it can say without context. It’s photos from the sky, which is neat and technical, but then what? For Nightingale, Robert Simmon describes the many ways that journalists use satellite imagery to tell stories and layer meaning.

This is a fun project by Jan Willem Tulp. Based on data from a cross-verified database of notable people, Tulp scrolls through history to show when these people enter and leave the world based on their age. Start in 3500 BC and scroll from there.

To gain a better understanding of how ChatGPT works under the hood, Santiago Ortiz repeatedly passed the prompt “Intelligence is” to the chatbot. Then he visualized the statistical paths to get to a response using a 3-D network. If you squint, the network kind of looks like a computer’s brain.

Alec Singh added another dimension to Conway’s Game of Life for a pretty, mesmerizing animation. The z-axis is used to show positions over time.

If a chart is seen by enough people, someone will call it misleading. There are no exceptions.

The World Happiness Report, published each year since 2012, just dropped for 2024. They focused on age and happiness this year. Overall, the United States ranked in the range from 17 to 29 among all countries, but was worse for young people. Finland was definitively at the top.

The visualizations are clinical, which is kind of sad given the topic of the report. Someone should collate the data and have some fun with it.

Jer Thorp has combined birding and data visualization into a unique course called Binoculars to Binomials:

I dreamt up Binoculars to Binomials as a hybrid site of learning. It’s for coders who are interested in cultivating an observational practice, and for birders who want to dive into the rich pool of data that comes out of their hobby.

More broadly, it’s for anyone who’s interested in the overlap between nature, data and creativity.

Sounds good to me.

One of the best ways to learn how to visualize data is to apply it to a specific field. You figure out the mechanics and the context behind the data, which makes visualization meaningful and useful. In this case, you get your hands in all parts of the process.

For Rest of World, Victoria Turk breaks down bias in generative AI in the context of national identity.

Bias in AI image generators is a tough problem to fix. After all, the uniformity in their output is largely down to the fundamental way in which these tools work. The AI systems look for patterns in the data on which they’re trained, often discarding outliers in favor of producing a result that stays closer to dominant trends. They’re designed to mimic what has come before, not create diversity.

“These models are purely associative machines,” Pruthi said. He gave the example of a football: An AI system may learn to associate footballs with a green field, and so produce images of footballs on grass.

Between this convergence to stereotypes and the forced diversity from Google’s Gemini, has anyone tried coupling models with demographic data to find a place in between?

Bartosz Ciechanowski is at it again with an in-depth explainer that makes heavy use of slider-driven interactive graphics. This time he simulated the patterns of air flowing over and around the wings of an airplane, also known as airfoil.

The length of each article starts to feel kind of long at times, but there’s something to these simple sliders that are useful in keeping you engaged and helping to understand the physics.

On April 8, 2024, the moon is going to completely block the sun along a designated path. For the Washington Post, Dylan Moriarty and Kevin Schaul use a strip of satellite imagery to show the totality across the United States, with events and time along the way.