While at work today, debugging a weird edge-case where some emails to Outlook have some unexpected padding, I learned something weird: Microsoft Outlook still (kinda) supports WordArt.

If you’re too young to remember: when Microsoft Office ’97 came out, it shipped with a feature called WordArt. Basically, these were a somewhat-customisable set of on-demand clip-art-esque text styles that you could apply to your own text.

The ease with which you could make use of these wacky designs meant that, for a while during the late 1990s, you wouldn’t see a poster advertising a school disco nor the front page of a GCSE essay that didn’t feature at least one¹ of these colourful eccentricities.

They gradually got phased-out and replaced with more-tasteful tools, but if you used them back in the day then images like the one above are sure to evoke a feeling of nostalgia.

Even as Microsoft adopted the Web as a platform, they were half-hearted about it. While HTML rapidly became used for things like styling emails as well as Web pages, Microsoft resisted the change, instead favouring two of their own standards in their own email clients. Outlook Express preferred to do “formatted” email using RTF, the format underpinning Microsoft Write, while fully-fledged Outlook effectively used Microsoft Word as its formatting engine².

You can still use RTF in some modern versions of Outlook (but you shouldn’t). But nowadays if you’re typing formatted text in Outlook, you’re almost-certainly creating HTML (albeit probably still using Microsoft Word³).

But this introduced a problem with backwards-compatibility. If Outlook switched to HTML as its default rendering format, what would happen to all the old WordArt? I’m confident that somebody asked this in a meeting at Microsoft in around 2007.

It turns out that Microsoft implemented a whole stack of custom CSS directives, just to support WordArt in Outlook⁴. For real! You can use CSS like mso-effects-shadow-angledirection: 5400000;⁵ to put a text-shadow directly below your text or mso-style-textoutline-outlinestyle-dash: longdashdotdotgel; to outline it with a mixture of dots and dashes. And it only works in Outlook. These form part of a translation layer for converting WordArt to and from a HTML-like format⁶.

So yeah: you can send an email containing classic WordArt to a modern Outlook installation and it still looks as horrible as it always did:

But you know what I find myself wondering? How do Microsoft’s new, Web-based Office Suite apps handle this?

I assume that if you send them “classic” WordArt from e.g. Outlook ’97, then they render it correctly. But to do that, they’re presumably…

1. Receiving the original WordArt in whatever proprietary format Word ’97+ used to put these graphics into emails.
2. Converting the WordArt to mso-prefixed CSS directives that Outlook 365 can understand.
3. Converting those custom CSS directives into standards-compliant modern CSS directives that your browser can understand.
4. Rendering that to the page you see the WordArt in all its… “glory”.

That seems like an insane amount of work, but it might not be far from reality. The (horrifying) alternative would be that Microsoft maintain a cluster of virtual machines running old versions of Windows, Office, and Internet Explorer, used just to convert WordArt into static images!

Or maybe Outlook 365 for Web finally killed off WordArt for email? I haven’t tested. But if it did… then it was probably about time!

In any case, this was a fun and unanticipated discovery of some long-maintained backwards-compatibility. It brought a smile to my face during the otherwise-tedious task of regression-testing blind-coded changes against a variety of versions of Microsoft Outlook!

🥰 RSS is great, and I'm glad you're using your feed reader... but you're missing out on some WordArt-like font magic that you can only see if you visit the original post. Maybe give it a look? 👀

“School is too slow for my kid.” That idea is ascendant on Twitter, particularly among tech-adjacent families. They post stories of their kids skipping years in weeks and reading before they can crawl, frequently using software or alternative school models.

I build curriculum and technology for schools, so I too experience a shiver at the thought of kids learning more, better, or faster. But I am also working weekly in a public fifth-grade math classroom this year, which helps me understand that the right follow-up question whenever anyone claims they can help kids learn 2x, 5x, or 10x faster is “learn what?”

Last week, we spent twenty minutes on two problems.

Last week, our fifth-grade class spent twenty minutes on two multiplication problems, both of which a calculator could chew through in nanoseconds, both of which many kids could certainly answer in less time if getting an answer were the only goal.

Those afflicted with edtech psychosis will declare this a profound waste of time—inefficient and pre-modern. The immunized will demonstrate more curiosity here. What else were those students doing besides calculating answers more slowly than a calculator? Here is an incomplete list:

• They were developing conceptual connections. They solved the same problems in three different ways, connecting older methods like area models and newer methods like the standard algorithm. Without those connections, many kids would inadvertently learn ideas like math is magic.

• They were developing their intuition. The teacher asked them to estimate the answer first as a check on their later calculation.

• They were developing their understanding of place value. 4 isn’t two times as large as 2 in the problem above as it seems to many students. It’s 200 times larger. The standard algorithm conceals that fact while partial products and the area model bring it into the sunlight.

• They were learning how to negotiate ideas together—how to critique and support one another’s work, how to explore their own path while keeping the group’s needs in focus. During a period of independent classwork, some kids moved farther through the problem set than others, but everyone returned at the end of class to consolidate their learning together.

The public has set out two goals for classrooms here:

• Teach math at a certain level of conceptual depth.

• Teach dozens of different kids to negotiate their ideas together.

The public can debate those goals, of course, but they require resources, chief among them time. Doing more stuff generally requires more time. One weird trick for saving time, more and more parents are learning, is to commit to fewer goals. For example, to teach math at shallower depth or to teach kids to negotiate their ideas with fewer other kids who differ from them in fewer ways.

Schools should always work to improve and always take seriously how much of a kid’s only childhood is spent under its care. I’m never going to be content with the work we do in schools, but I will always be impressed by the teachers, schools, and parents who commit every day to meeting ambitious goals with what few resources they have, to do more with less, unbothered by people who believe it is a great accomplishment to do less with less.

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Paul Jorgens is a long-time high school teacher who has also had the chance to teach fifth grade recently:

There is so much going on in the 5th grade world. I should have done this earlier in my career. There is much to learn from an elementary school teacher. The next week I subbed for a day in high school. A different energy was needed. Last week I modeled a lesson in a 7th grade classroom and did some observation at that middle school as well a different high school. There is a common thread. In all of those rooms the most important resource was the students. Their voices need to be heard and their contributions honored. We also have a great resource among our teaching colleagues. There is so much to learn from each other.

Checking in on Our Friends: Alpha School

Recently, I shared evidence that Alpha School hasn’t lived up to its predictions for its public school Unbound Academy. Here is some Alpha School media that has interested me in the weeks since.

¶ No one at Alpha School responded to my request for comment on the Unbound Academy results, but Craig Barton had Alpha School principal Joe Liemandt on his podcast and asked him about them.

CB: It seems that the results [at Unbound Academy] are not quite to the level that your schools are. What does that tell you about the barriers for scaling? What has Unbound not got that Alpha has?

JL: Great. So I don’t want to speak for Unbound because it’s actually a school that we don’t own and so they need to speak for themselves, but I can talk … I have ones I own that are similar so I can talk about it.

Unbound Academy is non-selective and tuition-free. The schools Liemandt goes on to discuss are selective and fee-based, which are crucial differences.

(p. 187 of Unbound Academy’s charter application)

Also, Liemandt seems confused about something important here. Of course he doesn’t own Alpha School because no one owns a public school. The closest thing a public school like Unbound Academy has to an owner is its board of directors which is responsible for ensuring the school meets its obligations to the people of Arizona and the commitments it made in its charter application.

And Unbound Academy’s founding board president was MacKenzie Price, the co-founder of Alpha School. Her husband, Andrew, was the board treasurer. The other two named directors are long-time employees of Alpha School. Liemandt acts like Unbound Academy is some independent franchise, but all four of his employees are still on its board. Disavowing Unbound Academy is like a district’s school board members disavowing responsibility for its schools. All of those people attached their names to huge promises of academic growth that Unbound Academy didn’t deliver. None of those people have answered to Arizona taxpayers for promising 60% math proficiency and delivering 10%.

¶ Andrew Woo spoke with Unbound Academy’s head of school, Michael Goto, finding that Alpha School’s promise of “2 Hour Learning” is slipping as they integrate their model into public schools.

Michael Goto, Unbound’s head of school, told me that students spend about 2.5 hours on learning apps each morning, plus another 1.5 hours on Mondays and Fridays. Virtual afternoon clubs include chess, invention and broadcasting. Teachers monitor students online and can pull them into breakout rooms when they get stuck. But if a student never logs in or closes the computer, staff text the family. Repeated incidents lead to an intervention meeting.

Four hours of dedicated app time in a day is a pretty big pill to swallow. I’m curious to see what other parts of a model built for the wealthy will have to change in response to the needs of public school students.

¶ ProPublica and the Texas Tribune reported that one of Alpha School’s other public school experiments has not produced the results promised by Alpha School either.

Texas Preparatory School received its third consecutive F rating, largely based on the test scores it received during the partnership with Alpha.

¶ Alpha School claims it doesn’t screen its applicants for academic achievement, but somehow it has selected freshmen with elite academic achievement—SAT scores that are, on average, 38% higher than the average Texas student.

¶ Ex-Obama guy Ravi Gupta had me on his podcast Lost Debate to discuss the model and why it hasn’t worked.

¶ There are a bunch of concerns online about Alpha High School’s start-of-school bootcamp. The New York Times also has an interesting piece on the connections between Alpha School, 2 Hour Learning, Guidepost Montessori, and (?!?) AltSchool. That last one really lights up the corkboard conspiracist in me, but look—I have a lot on my plate right now. I can only get so worked up about rich people getting hand-me-down assets from other rich people. I can only get so worked up about how rich parents decide to parent their rich kids. My interest in Alpha School is pretty tight: Is there anything here for kids outside the top tax bracket? Can 99% of schools learn anything from Alpha School? I’ve gotta stay focused, I’m sorry.

I am proud to be among the list of 25 initial signatories — all Fields Medallists — to the declaration below, which grew out of discussions between ourselves over the last week. We have also posted our declaration on this web page, and (similarly to the Leiden declaration) invite further signatures. (It is unfortunate that we did not have the time to have a more consultative process, as with Leiden; but we decided that the urgency of the situation was such that we needed to release a statement sooner rather than later.)

See also this recent article in the Economist regarding our declaration, and a brief interview with James Maynard on this topic. A French version of this declaration was published in Le Monde.

Over the last few months, the mathematical capabilities of LLMs have improved dramatically, to the point that they can solve major outstanding problems in many fields of mathematics. However, the push by AI companies to solve mathematical problems as a benchmark is detrimental to the science of mathematics, and to the mathematical community. The goals of the AI companies and the goals of the mathematical community are severely misaligned. We see these as part of broader alignment issues impacting other scientific and creative professions, as well as the whole of society.

Research mathematics deals with understanding basic structures of shapes, numbers, and natural phenomena. Over the course of generations, it has built a large corpus of sophisticated ideas, methods, abstractions, and other tools to comprehend the mathematical landscape. In turn, modern technologies and sciences are based on mathematical tools.

Famous problems have often served as landmarks and lighthouses against which one can measure an improved understanding of this landscape. Solving one of these problems has been a certain sign of new insights and interesting methods, which would then be studied by a community of mathematicians, through a long and arduous process of talks, discussions, simplifications. At the end of this process, one will ideally find a textbook presentation of the results suitable for any graduate or even undergraduate student to study. Some of the mathematical ideas pursue their journey even further to become, decades or centuries after, tools that are understood and used by the whole population.

The mathematical community functions, in many ways, as a miniature version of humanity. It consists of individuals using a wide variety of different approaches, joined by core values. The most precious resources of our profession are students and ideas, and these we nurture with great care. We feel responsible to let them grow to their full potential, until they can live a life of their own in the mathematical world. For students we often suggest problems with the core intention of developing skills making them well-positioned for advances in research and elsewhere. Our ideas we disseminate in talks, private discussions and careful writeups, connecting them to the previous ideas of others. These processes invariably take time and are based on human interaction.

In recent months, the success of AI in solving major mathematical problems has made headlines even outside mathematical circles. But solving problems is only a tool and proxy for achieving the primary goal of conceptual understanding and insight. Forgetting this in the world of AI may turn the tool against the primary goal. Indeed, the mass production at faster and faster pace of “true/false” statements could destroy fertile ground instead of breathing life into new ideas.

Often these solutions are announced in a rush, leaving no time for a proper writeup, the isolation of new methods and ideas, and citing relevant previous work of others. As in all creative professions, this raises severe attribution and plagiarism questions. Moreover, without the willing mathematicians who must take care of their development and integration into the mathematical canon, AI-conceived ideas would never become fully alive and the crucial human transmission chain between mathematicians would be lost.

We are witnessing a general threat to intellectual work, with misalignment between the outcome of the use of AI and its initial purpose. In many fields and activities, years of training have traditionally served not only to produce a final answer or product, but also to develop understanding and the ability to formulate new questions and ideas. However, building on a vast body of previous human work, AI systems are becoming increasingly capable of producing the results of such work directly, and these goals cease to align. The issues the mathematical community faces now are similar to issues that other scientific and creative professions are facing, and indicate issues that all of humanity might face: how to make sure that, as AI changes the way work is done, we do not lose sight of what that work was meant to achieve in the first place.

AI offers the potential of enhancing and accelerating genuine mathematical study and understanding. Mathematics as a profession will need to adapt to these changes in several ways. However, whether these changes ultimately benefit the field or have a destructive effect will in large part be determined by the decisions of the humans in control of this new technology.

These issues must be addressed urgently, in the mathematical community, by the companies developing these technologies and, more broadly, by a society that will confront similar problems in many other forms of intellectual work.

Artur Avila (Fields Medal 2014)
Manjul Bhargava (Fields Medal 2014)
Caucher Birkar (Fields Medal 2018)
Pierre Deligne (Fields Medal 1978)
Yu Deng (Fields Medal 2026)
Simon Donaldson (Fields Medal 1986)
Hugo Duminil-Copin (Fields Medal 2022)
Alessio Figalli (Fields Medal 2018)
Martin Hairer (Fields Medal 2014)
June Huh (Fields Medal 2022)
Maxim Kontsevich (Fields Medal 1998)
Elon Lindenstrauss (Fields Medal 2010)
Pierre-Louis Lions (Fields Medal 1994)
James Maynard (Fields Medal 2022)
Curt McMullen (Fields Medal 1998)
Shigefumi Mori (Fields Medal 1990)
Ngô Bảo Châu (Fields Medal 2010)
Andrei Okounkov (Fields Medal 2006)
Peter Scholze (Fields Medal 2018)
Stanislav Smirnov (Fields Medal 2010)
Terence Tao (Fields Medal 2006)
Maryna Viazovska (Fields Medal 2022)
Cédric Villani (Fields Medal 2010)
Wendelin Werner (Fields Medal 2006)
Efim Zelmanov (Fields Medal 1994)

We are most human when we play freely, imaginatively, pointlessly. Gamification risks atrophying that most precious capacity

- by Justin Neuman

Read on Aeon

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For a while, I must admit, it looked as if software developer roles like mine were done for. How could we fight against tireless robots? But our industry is slowly realizing that making truly cutting-edge software still requires humans to think and work together, to maximize their skill sets and to practice their respective crafts. A.I. can write very good software, but it also makes it easy to do someone else’s job badly, which is part of why all those projects fail. Now that everyone can code, it’s become clearer why many shouldn’t.

— Paul Ford, A.I. Was Supposed to Give Us New Killer Apps. What Happened?

Tags: paul-ford, generative-ai, deep-blue, ai, llms