AI companies like to say that they are close to or reached a level of “intelligence” in their tools that it’s like having a PhD assistant in your pocket. Claus Wilke argues that classification is misguided.

Now, presumably AI models have the required tenacity for a PhD (as long as somebody pays for the token budget), and I just said exceptional intelligence is not required. So what’s keeping current AI models from PhD-level performance? In my opinion, it’s the ability to actually reason, to introspect and self-reflect, and to develop and update over time an accurate mental model of their research topic. And most importantly, since PhD-level research occurs at the edge of human knowledge, it’s the ability to deal with a situation and set of facts that few people have encountered or written about.

In practical terms, I’m pretty sure most people do not want a PhD-level assistant. They want immediate answers. They want a diligent intern.

A PhD assistant is going to answer your question with more questions, and then five to seven years later, you will finally get an “answer” that might be correct but you won’t know for sure because there will be a lot of uncertainty attached. However, the good news is that you might be able to explore more with further research and future directions. You will have to do that on your own though or find another PhD assistant, because the original assistant has since moved on to a different interest.

Because no one is perfect, even those striving for PhD-level intelligence.

Wplace uses a world map as a canvas. Zoom in to where you live and paint with pixels, or just gander at what others’ masterpieces (given technical difficulties, it seems you can only look for now).

If this looks familiar, Wplace is based on r/place from 2017, when Reddit released a canvas that anyone could draw a pixel on every few minutes. Wplace is like that but in a geographic space and no battles for pixel space.

In what now seems like a tale as old as time, a man grew convinced that he had untapped mathematical genius, with the help of ChatGPT. But 90,000 words later, it seems that might not be the case. For the New York Times, Kashmir Hill and Dylan Freedman evaluated Allan Brooks’ very long chat.

This is going to keep happening, and it’s probably going to get worse until people realize that the chatbot is not thinking. It’s a product of statistical convergence. The “delusions” are computer errors. Please stop pretending the chatbots are people.

Molly Smith reports for Bloomberg on the appointing of EJ Antoni to head the BLS. Antoni has been vocal about recent estimate revisions.

Antoni came on Bannon’s podcast shortly after the latest jobs report was released, where he was asked if there was a “MAGA Republican” in charge of BLS. Antoni responded, “No, unfortunately.”

Antoni added that the absence of a Trump pick running the agency is “part of the reason why we continue to have all of these different data problems.” He contributed to the Project 2025 policy rubric, which, in part, called for maximizing hiring of political appointees at the Labor Department, which oversees BLS.

Antoni is calling for an audit of the full statistical process and more transparency. Interesting.

Many are discovering that the Bureau of Labor Statistics updates past estimates, but some have been misled to believe that the recent correction to jobs numbers was politically motivated. For the New York Times, Ben Casselman, with graphics by Keith Collins and Christine Zhang, explains why updates to the data are a regular thing.

There is a fundamental tension inherent in all economic data: accuracy vs. timeliness.

Policymakers, investors and businesses want information as quickly as possible so it can inform their decisions. But the most complete data is often based on tax returns, Social Security filings or other records that aren’t available until months or years later.

Revisions are the imperfect solution to this problem. Statistical agencies release preliminary estimates of job growth, inflation, gross domestic product and other measures, then revise them as more complete data becomes available.

Reduced funding, agency layoffs, and uncertainty in the economy don’t help with the accuracy.

The stores use Flock cameras to collect license plate data from cars entering parking lots. Law enforcement is tapping the data as a source for their growing surveillance systems. Jason Koebler for 404 Media reports.

“What we’re learning is that two of the country’s most popular home improvement stores are contributing to the massive surveillance dragnet coordinated by Flock Safety,” Dave Maass, director of investigations at the Electronic Frontier Foundation, told 404 Media. “Do customers know that these stores are collecting their data and sharing indiscriminately? Probably not. Have these companies given thought about how this data might put their customers in danger, whether it’s cops stalking their exes or aggressive ICE agents targeting yard workers? Probably not. If these companies want customers to feel safe in their homes, then they should make sure they’re also safe where they buy their supplies.”

Maybe this doesn’t affect you directly now, but on our current path, it will eventually.

During the DOGE-fueled federal firings, which seems like a lifetime ago already, the National Weather Service lost about 550 employees. They received permission recently to hire 450, because it is necessary. For CNN, Andrew Freedman reports.

The announcement was also met with frustration over the people the agency lost in the failed attempt at government savings.

“How much time/money is it going to cost to train a bunch of new people when we had already-trained people in place?” asked another NOAA official, who requested anonymity because they were not authorized to talk to the media. It is possible that some of the new hires will have been previously trained employees who were let go in the DOGE cuts.

OpenAI introduced GPT-5 in a livestream, and they used a set of seemingly straightforward charts for benchmarks. The point was to show the improved performance of GPT-5 over previous models. However, the labels do not remotely match the bar heights.

The bar for 69.1% is the same height as the one for 30.8% when the former should be more than twice the height of the latter. The bar for 52.8% is taller than the one for 69.1%. It’s off.
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Hansi Lo Wang reporting for NPR:

The 14th Amendment requires the “whole number of persons in each state” to be included in a key set of census numbers used to determine how presidents and members of Congress are elected.

It’s unclear if Trump — who, according to the Constitution, does not have final authority over the census — is referring to the regularly scheduled national head count in 2030 or an earlier tally.

Trump said he’s instructed the Commerce Department, which oversees the Census Bureau, to “immediately begin work” on a census using “the results and information gained from the Presidential Election of 2024.” It’s unclear why the election results would matter to the census.

The Trump announcement comes just a couple weeks after the Census Bureau released their operational plan for the 2030 count, naturally.

Matt Seaton, for the Atlantic, reports on how the statistical approach impacted cycling and led to Tour de France winning.

The riders now train with the data, they race with the data, they publish their data on Strava and similar training apps, they probably dream about their data. So we know that during a crucial mountain stage in last year’s Tour de France—won convincingly by Pogačar on his way to overall victory—he produced approximately 7 watts per kilo for nearly 40 minutes. His main rival, Vingegaard again, actually tried an attack that failed, despite an estimated output of more than 7 watts per kilo for nearly 15 minutes. These were efforts in the Pyrenees; at sea level, the numbers would be even higher. (This all gets geeky quickly.)

Winning cyclists are riding faster and more efficiently now than they were during the peak doping era.

These days, data plays a part in most sports to optimize athletic performance. There’s a clear response variable — winning — and everything else (explanatory variables) feeds into that.

Cycling and other endurance sports work especially well with data, because the interactions between opponents and the movements to get from point A to point B are more straightforward. Fewer variables and less uncertainty.

I’m looking forward to seeing advanced analytics for bowling. Competitive darts? Beer pong?

Staying within chart defaults might provide an advantage.

Reuters highlights unsafe temperatures in prison cells, using building models, public records, and temperature data.

There’s often little relief within the prison walls. Nearly half of state prisons across 29 states have partial or no air conditioning in housing units, according to an exclusive database created by Reuters using documents obtained through public records requests to all 50 states. The Bureau of Prisons, which oversees all 122 federal prisons, did not respond to Reuters’ request seeking information on how many facilities have air conditioning as of the time of publication.

Find more on their methodology here.

It’s exciting when an animal escapes from the zoo. A monkey breaks free from its cage, ridding itself of restriction with nothing but freedom ahead. In a way, we are animals in a zoo biding our time until we can finally break free. Indeed.

For WaPo’s Department of Data, Bonnie Berkowitz, Artur Galocha, and Andrew Van Dam reminisce over the animal escapes since 1990, based on data catalogued by Born Free USA.

Drew Harwell, reporting for the Washington Post:

Artificial intelligence tools are not just reshaping how we work, learn and live. They’re also changing what happens after we die, as families and funeral homes seek help in creating tributes and eulogies to define a loved one’s life.

Funeral directors are increasingly asking the relatives of the deceased whether they would prefer for AI to write the obituary, rather than take on the task themselves. Josh McQueen, the vice president of marketing and product for the funeral-home management software Passare, said its AI tool has written tens of thousands of obituaries nationwide in the past few years.

Call me old-fashioned, but I’d rather have a poorly-written, incomplete obituary from a loved one than a pristine-looking convergence of words from a machine.

N. Gregory Mankiw and Cecilia Rouse, both former chairs of the Council of Economic Advisers, who advised President George W. Bush and President Joe Biden, respectively, warn of the consequences of the BLS firing.

While no single piece of economic data is dispositive in a leader’s decision-making, each must be as accurate as possible to help guide their decisions. Recognizing this need, the federal government has over time created 13 statistical agencies. The BLS is one of the oldest, created in 1884 to collect data on many aspects of the economy, including the monthly employment report and retail and wholesale prices. Today, government and business leaders throughout the economy rely on this information to make sound decisions. These statistics shed light on the state of the labor market, trends and opportunities in business investment, and the size and health of our population.

For the New York Times, Steven Strogatz explains taxicab geometry through the lens of an Etch A Sketch. It is a world where the Pythagorean theorem does not apply and total distance is simply the sum of a and b rather than the length of a hypotenuse.

The problem with two-dimensional map projections is distortion creeps in no matter what you do. The problem with globes on a screen or in print is that they only show the geography facing the viewer. xkcd solved both problems.

For the New York Times, Ben Casselman highlights the potential repercussions for shunning agency data with examples from other countries who chose darkness.

Perhaps most famously, there is the case of Argentina, which in the 2000s and 2010s systematically understated inflation figures to such a degree that the international community eventually stopped relying on the government’s data. That loss of faith drove up the country’s borrowing costs, worsening a debt crisis that ultimately led to it defaulting on its international obligations.

Ignore and complain all you want, but reality eventually settles in. The person experiences unemployment, we pay the expensive grocery bills, and we see what’s happening in our neighborhoods. Data helps us keep track before the problems grow overly difficult.