More big numbers coming in. For NYT’s the Upshot, Aatish Bhatia demonstrates the millions of trillions (octillions!) of calculations required for training AI.

During its training, the A.I. is fed a portion of a sentence, and the knobs are adjusted using a series of math calculations to improve its guesses for the next word. This process is then repeated over trillions of words of training data.

It takes only a few additions and multiplications to adjust a single knob. But when trillions of knobs are adjusted trillions of times, the math quickly adds up.

And this is just the first step in training a language model, known as pre-training. It’s followed by additional steps of calculations that hone the A.I.’s behavior and improve its ability to code and solve math problems.

All those calculations require energy, data, and time. The big companies are taking what they can get with the first two things in efforts to reduce the third.

This is fun, or I guess very serious depending on your point of view. Georgios Karamanis adapted the Show Your Stripes project, which visualizes average temperature each year as a stripe, into a screensaver that runs in macOS.

It is fascinating how a simple view of data can fit into different spaces.

There is a proposition on the California ballot this year to impose a one-time tax for billionaires. The result would be a 5% tax on their net worth, which includes investments. For New York Times Magazine, Nicholas Konrad illustrates the scale of this tax on 250 individuals in the context of their wealth, the taxes they typically pay, and their spending to oppose.

For us regular people, one billion dollars is difficult to imagine, so the transitions between tiny and giant circles provide a sense of how big these numbers are. By absolute counts, the tax dollars are a lot, but by percentage, the tax rate doesn’t seem like that much all things considered.

This week, I walk through my own thinking as I poke at fresh data.

This is really going to surprise you, but the wealthy people in the United States became wealthier over the past three years. For the Wall Street Journal, Rachel Louise Ensign and Justin Lahart report on these changes with some charts.

The roughly 137,000 households who make up the nation’s top 0.1% had already become richer in comparison to everyone else in recent decades, thanks to higher incomes and lower tax rates. Now the stock-market boom, driven by strong corporate earnings and highflying tech stocks, is powering their wealth even further.

By comparison, the rest of the top 10% of richest Americans gained $38.2 trillion in wealth over the same period, but it was split among nearly a hundred times as many households.

This is based on fresh Federal Reserve data from the Survey of Consumer Finances.

In reference to the xkcd comic that illustrates the internet as a stack of blocks propped up by a small project, sheets.works shows development history of real open source projects that our devices depend on. It is not uncommon for a single developer to maintain widely used open source software on a volunteer basis.

The Centre for the Study of the Legacies of British Slavery published a searchable database of four million records logged between 1813 and 1840. For the Guardian, Aamna Mohdin reports:

The registers were created by the British government after the abolition of the transatlantic slave trade in 1807, and were designed to prevent illegal trading of enslaved people and to monitor the enslaved population.

Researchers say people were recorded in the registers with chilling precision. Entries include names, ages, physical descriptions and family ties, as well as births, deaths and transfers of ownership. Some distinguish whether a person was born in Africa or in the Caribbean.

Nicka Sewell-Smith, a professional genealogist and senior story producer at Ancestry, said it was not lost on her that records originally created to count enslaved people as property were now being used by their descendants to reconstruct their family histories.

Daniel Willingham, a professor at the University of Virginia, argues that students should only use AI for things they know how to do well.

This lead/associate orientation makes sense for life after graduation. Once a student is in the workforce, they will use AI to produce products: new ideas, reports, and so on. A guiding principle of “Use AI, but let me see your work” makes sense.

This is not, however, an optimal guiding principle for students. Students produce products, but the product is seldom the point. No one wants to read the papers they write, and the math problems they solve have no practical value. Teachers have them do these tasks because they provide practice in cognitive abilities that we think matter.

I remember pulling all-nighters in the computer lab in college, with only Pop-Tarts and a fear of a failing grade to keep me going. What it must be like for students and educators these days knowing that the answer is only a prompt away.

I just keep telling my young kids (while they still listen to me) that we need to know how to do things and how things work. Things will break or need to get better, and that requires people who can think for themselves.

Data for Progress compared social activities among American adults in 1995 against 2026. Hanging out with others is down a lot for many activities.

We find that Americans’ participation in various social activities has declined by double-digit margins, including attending a class or seminar (-22), entertaining people in your home (-21), attending church or a place of worship (-20), going on a picnic (-19), going to a club meeting (-19), playing cards (-19), going out to breakfast at a restaurant (-13), and doing volunteer work (-12).

A few activities have seen increased engagement: using the internet (+90), playing a home video game (+22), attending an exercise class (+14), going to a pop or rock concert (+13), and taking an airline trip for personal reasons (+5).

At least staying late at work is also down quite a bit.

They used a dumbbell chart to show the difference, but arrows that start with the 1995 value and point to the 2026 value would make for an easier comparison. If they wanted to stick with the dumbbell, I would’ve switched the shades so that present day stands out more. The current color scheme reads backwards in time.

Torahmap by Danyel Fisher is an interactive visualization of the Hebrew Bible. Each column represents a book, each line represents a chapter, and each square represents a verse. Zoom and pan to see details.

I know little about the context, but I like the concept and interaction with text. People should do more with words as data.

A population pyramid shows the age distribution of a population, usually with counts on the x-axis, age on the y-axis, and two sides for male and female. You’ve probably seen them before. Karim Douïeb added a third dimension in a visualization that shows total population around the world and over time.

Select a country on the globe, and you get age distributions from 1950 through a projected 2100. One year appears at a time to create a surface.

Each layer is one year, from 1950 to 2100: the share of the population at each single year of age. Young, fast-growing places bulge on the left; ageing ones flatten out and lean right. Switch to 2D to see the years overlaid.

It’s a fun riff on an old chart. Fair warning: my old computer nearly buckled under the pressure in the 3-D view.

This week is about helping data stand out in the land of many points.

Harvard researchers published an analysis arguing that risk of cancer increases when people live closer to nuclear power plants. Deric Tilson and Adam Stein, of the Breakthrough Institute, disagree with the study, so they replicated it. They show that using the Harvard methodology, results suggest any physical landmark will lead to increased cancer risk.

Costco, with its “reasonably priced clothes”, topped the list, which seems like an incorrect conclusion to draw from the data. How dare they.

Is there something that comes after we die? For the Pudding, Alvin Chang imagined what it would be like if you walked into a room and could see everyone’s answer. A dot plot provides a map of where people stand, by age and their responses. You walk and scroll through the crowd along the axes and towards the light while music and narration provide depth to the data.

I like how Chang keeps the abstract connected to reality. It is a recurring theme, like in previous work on loneliness, teenage adversity, and relationships. Zoom out to see the patterns and zoom in to see individual survey responses.

Researchers at the School of Cities, a center at the University of Toronto, are studying the impact of U.S. tariffs in Canada.

Since May 2025, Tara Vinodrai and Karen Chapple have led a team of researchers at the School of Cities to develop interactive mapping and visualization tools that measure and explore the potential impacts of tariffs on jobs and businesses across Canadian cities and communities at the neighbourhood and city level. Our tools illustrate the highly localized and uneven potential impacts of U.S. tariffs reflecting Canada’s underlying urban, regional and sectoral specializations.

An interactive map shows potential exposure. The above shows estimates as of the end of September 2026, which have high uncertainty, because they are dependent on the variable choices of the U.S. president.

Duncan Bradley made a zine of one-liners from data visualization folks. It is called “How to Sound Smart When Talking About Charts.”

It has been a whole ten minutes since posting a color selection tool, so here is another. Phosphor by Joshua Stevens lets you enter hex colors and get a gradient with the gaps filled in between. Made with chroma.js, there are some familiar options, such as color space and color deficiency previews.

OKpalette is a tool to grab a color palette from an image. Load the image in the browser, set the number of colors, and define a region in the image and you get a collection of shades. Images stay on your computer and the color picking is in-browser. Nifty.

There will never be enough color-picking tools.

Over summer break, MIT installed over 500 AI-enabled surveillance cameras around campus. In an act of defiance celebration, professors started a group to bedazzle the cameras.

That said, if any students, staff, or faculty still have pesky concerns about this unprecedented ratcheting-up of campus surveillance, now you know that there’s a committee to contact with complaints. Or, you could join us, the cheerful bedazzlers, by celebrating the beauty of our beholdenness to Big Brother Tech. Do you feel proud to be involved in the capitalist project of improving Ambient.ai’s services and enriching surveillance for all people, everywhere? Do you patriotically embrace the fact that Ambient.ai shares the data they collect with government and law enforcement when they receive subpoenas, without needing to ask or tell the institutions from whom they collected it? If so, there are hundreds of white plastic eyelids out there awaiting artistic adornment to enhance their visibility even as they enhance ours!

In sum, we thank our glorious administration for their brilliant technical solution to a social problem. This is how it should be, because all social and political problems definitely have a technical solution, and usually we just need to pay for something involving AI rather than have an open discussion as a community (what a waste of time that would be!).

This week is about finding your way by getting lost in the data.