Tony Hoare (1934-2026)

Turing Award winner and former Oxford professor Tony Hoare passed away last Thursday at the age of 92. Hoare is famous for quicksort, ALGOL, Hoare logic and so much more. Jim Miles gives his personal reflections.

Jill Hoare, Tony Hoare, Jim Miles. Cambridge, 7 September 2021

Last Thursday (5th March 2026), Tony Hoare passed away, at the age of 92. He made many important contributions to Computer Science, which go well beyond just the one for which most Maths/CompSci undergraduates might know his name: the quicksort algorithm. His achievements in the field are covered comprehensively across easy-to-find books and articles, and I am sure will be addressed in detail as obituaries are published over the coming weeks. I was invited in this entry to remember the Tony that I knew, so here I will be writing about his personality from the occasions that I met him.

I visited Tony Hoare several times in the past 5 years, as we both live in Cambridge (UK) and it turned out that my family knew his. As a Mathematics graduate, I was very keen to meet and learn about his life from the great man himself. I was further prompted by a post on this blog which mentioned Tony a few times and summarised a relevant portion of his work. I took a print out of that entry the first time I visited him to help break the ice - it is the green sheet of paper in the picture above.

Tony read the entry and smiled, clearly recalling very well the material of his that it referenced, and then elaborating a bit, explaining how vastly programs had scaled up in a rather short space of time and how they typically require different methods than many of those he had been developing in the early days.

I was aware that Tony had studied Classics and Philosophy at university so I was keen to learn how one thing had led to another in the development of his career. He explained that after completing his degree he had been intensively trained in Russian on the Joint Services School for Linguists programme and was also personally very interested in statistics as well as the emerging and exciting world of computers. This meant that after his National Service (which was essentially the JSSL) he took on a job 'demonstrating' a type of early computer, in particular globally, and especially in the Soviet Union. He described the place of these demonstrations as 'fairs' but I suppose we might now call them 'expos'. In a sense, this seemed like a very modest description of his job, when in fact - reading up on Tony's career - he was also involved in the development of code for these devices, but perhaps that's a historical quirk of the period: being a demonstrator of these machines meant really knowing them inside and out to the point of acting on the dev team (AND, one might deduce, being fluent in Russian!).

Tony would tell these stories with a clarity and warmth that made it clear that certainly he was still entirely 'all there' mentally, and that his memory was pinpoint sharp, even if there were some physical health issues, typical for anyone who makes it so far into their 80s (and, as we now know, beyond!).

A story that I was determined to hear from the source was the legendary quicksort 'wager'. The story goes that Tony told his boss at Elliott Brothers Ltd that he knew a faster sorting algorithm than the one that he had just implemented for the company. He was told 'I bet you sixpence you don't!'. Lo and behold, quicksort WAS faster. I asked Tony to tell this story pretty much every time we met, because I enjoyed it so much and it always put a smile on both of our faces. To his credit, Tony never tired of telling me this story 'right from the top'. I had hoped to visit again in the past year and record him telling it so that there was a record, but unfortunately this did not happen. However, I discover that it is indeed recorded elsewhere. One detail I might be able to add is that I asked Tony if indeed the wager was paid out or if it had merely been a figure of speech. He confirmed that indeed he WAS paid the wager (!). A detail of this story that I find particularly reflective of Tony's humble personality is that he went ahead and implemented the slower algorithm he was asked to, while he believed quicksort to be faster, and before chiming in with this belief. It speaks to a professionalism that Tony always carried.

About 50% of our meetings were spent talking about these matters relating to his career, while the rest varied across a vast range of topics. In particular, I wanted to ask him about a story that I had heard from a relative, that Tony - whilst working at Microsoft in Cambridge - would like to slip out some afternoons and watch films at the local Arts Picturehouse. This had come about because on one occasion a current film in question was brought up in conversation and it transpired Tony had seen it, much to the bemusement of some present. The jig was up - Tony admitted that, yes, sometimes he would nip out on an afternoon and visit the cinema. When I met Tony and gently questioned him on this anecdote he confirmed that indeed this was one of his pleasures and his position at Microsoft more than accommodated it.

On the topic of films, I wanted to follow up with Tony a quote that I have seen online attributed to him about Hollywood portrayal of geniuses, often especially in relation to Good Will Hunting. A typical example is: "Hollywood's idea of genius is Good Will Hunting: someone who can solve any problem instantly. In reality, geniuses struggle with a single problem for years". Tony agreed with the idea that cinema often misrepresents how ability in abstract fields such as mathematics is learned over countless hours of thought, rather than - as the movies like to make out - imparted, unexplained, to people of 'genius'. However, he was unsure where exactly he had said this or how/why it had gotten onto the internet, and he agreed that online quotes on the subject, attributed to him, may well be erroneous.

One final note I would like to share from these meetings with Tony is perhaps the most intriguing of what he said, but also the one he delivered with the greatest outright confidence. In a discussion about the developments of computers in the future - whether we are reaching limits of Moore's Law, whether Quantum Computers will be required to reinvigorate progress, and other rather shallow and obvious hardware talking points raised by me in an effort to spark Tony's interest - he said 'Well, of course, nothing we have even comes close to what the government has access to. They will always be years ahead of what you can imagine'. When pressed on this, in particular whether he believed such technology to be on the scale of solving the large prime factorisation that the world's cryptographic protocols are based on, he was cagey and shrugged enigmatically. One wonders what he had seen, or perhaps he was engaging in a bit of knowing trolling; Tony had a fantastic sense of humour and was certainly capable of leading me down the garden path with irony and satire before I realised a joke was being made.

I will greatly miss this humour, patience, and sharpness of mind, as I miss everything else about Tony.

RIP Tony Hoare (11 January 1934 - 5 March 2026)

How does AI do on Baseball-Brothers-Pitchers

In my graduate Ramsey Theory class I taught Kruskal's tree theorem (KTT) which was proven by Joe Kruskal in his PhD thesis in 1960.  (Should that be in a graduate Ramsey Theory class? There are not enough people teaching such a course to get a debate going.) A simpler proof was discovered (invented?) by Nash-Williams in 1963.

The theorem is that the set of trees under the homeomorphism ordering is a well quasi order.

But this blog post is not about well quasi orderings. It's about baseball brothers and AI.

The Kruskals are one of the best math families of all time. See my post on math families.The Bernoullis are another great math family.  What makes both families so great is that they had at least THREE great math people. Most have two.

Having taught the KTT and talked briefly about math families, I was curious how ChatGPT would do on the better-defined question of largest number of wins by a pair of brothers in baseball.  So I asked my students to look that up and include which tools they used,  as a HW problem  (worth 0 points but they had to do it). 

I wrote up 9 pages on what the answer is (there are some issues) and what the students' answers were. See my write up.

In case those 9 pages are tl;dr, here are the main takeaways

1) 7 of the answers given were just WRONG no matter how you look at it.

2) 7 had either the Clarksons who played in the 1880's, so don't count as modern baseball, or there were three of them, or both.  Even so, one could argue these are correct

3) 1 got it right. (That's   one got it right  not  I, bill g, got it right. It can be hard to distinguish the numeral for one, the letter capital i, and the letter small L. I blogged about L vs I here in the context of Weird AI vs Weird AL).

4) There were 13 different answers, which I find amazing.

As usual, when I study some odd issue, I learn a few other things of interest, at least to me, which may also have life lessons, though YMMV. 

a) Around 85% of pitchers in the Hall of Fame have won over 200 games. Dizzy Dean (his brother was also a pitcher which is why I was looking at this) got into the Hall of Fame with only 150 wins. Why? For 6 years he was the most dominant pitcher in the game. In addition (1) there was some sympathy for him since his career was cut short by an injury he got in an All-star game, and (2) he had a colorful personality and people liked him. The four least-wins for a HOF are: Candy Cummings (W-L 145-94).  [he is said to have invented the curveball], Dizzy Dean (W-L 150-83) , Addie Joss (W-L 160-97 )[he only played 9 years, which is less than the 10 needed for eligibility in the HOF, but he died so he was given an exception], Sandy Koufax (W-L 165-87) [he was dominant, like Dean, for a short time]. Sandy is the only one who is still alive. (W-L means Win-Loss. For example, Candy won 145 games and lost 94.)

b) Modern baseball starts in 1900. But this is arbitrary. In 1904 Jack Chesbro won 40 games which would never happen in modern baseball. But you have to draw the line someplace.  History is hard because there are fuzzy boundaries.

c) I had not known about the Clarkson brothers. 

d) My interest in this subject goes back to 1973. In that year I heard the following during a baseball game and, ever since I began blogging, I wondered how I could fit it into a blog post:


An old baseball trick question is now gone!  Just last week [in 1973] if you asked

What pair of brothers in baseball won the most games?

the answer was Christy and Henry Mathewson. Christy played for 16 seasons and had a W-L record of 373-188, while his brother Henry played in 3 games and had a W-L record of 0-1. So their total number of wins is 373 which was the record.

But this last week [in 1973] the Perry brothers, Gaylord and Jim, got 374 wins between them.  Hence the question

What pair of brothers in baseball won the most games?

is no longer a trick question since both Perrys are fine pitchers [Gaylord made it into the Hall of Fame; Jim didn't, but Jim was still quite good.]

As you will see in my writeup, the Perrys' record was broken by the Niekros.

e) Christy and Henry are a trick answer because Henry wasn't much of a player with his 0-1 W-L record. Are there other pairs like that? Greg (355 wins) and Mike (39 wins) Maddux might seem that way but they are not. While Mike only had 39 wins he had a 14-year career as a relief pitcher. Such pitchers can be valuable to the team, but because of their role they do not get many wins.

SO the usual question: Why did AI get this one wrong? Lance will say that the paid version wouldn't have and he may be right. David in Tokyo might say that ChatGPT is a random word generator. Others tell me that ChatGPT does not understand the questions it is asked (my proofreader thinks this is obvious).  I'll add that the pitcher-brother  question has more ambiguity than I thought- do you count pitchers before 1900? Do you count a brother who won 0 games? What about 3 brothers (not the restaurant)?

The Purpose of Proofs

In discussions of AI and Mathematics, the discussion often goes to mathematical proofs, such as the the First Proof challenge. So let's look at the role of proofs in mathematics.

Without a proof, you don't even know whether a theorem is true or false. It's not even a theorem until you have a proof, just a conjecture or hypothesis. You might have some intuition but you don't know the hardness of a proof until you find it. Even then that only gives you an upper bound on hardness as someone might find a simpler alternative proof in the future,

Proofs give an understanding of why a theorem is true. A proof can look like a piece of art, especially if the proof works in a new or clever way, maybe even a proof from the book like Kelly's Proof of the Sylvester–Gallai theorem. A mathematician often has their most satisfying experiences finding a proof of a theorem they or others have had challenge proving earlier.

Conjectures drive the need for proofs, but often it goes the other direction. A failed proof leads to a new conjecture and maybe a different theorem altogether. My time-space tradeoffs came out of a failed attempt to show NP different from L. Too often I see papers with theorems that are really just what the best proof they can find achieves.

Proofs are supposedly objective, either it is a valid proof or it isn't, the biggest reason AI tackles proofs as we can grade them as right or wrong. Most academic papers have high-level proofs and a referee however needs to make a subjective decision whether the proof has enough details to count as a legitimate proofs. If a proof has minor fixable errors, then it isn't a proof but we usually count it as one.

Now we have proof systems like Lean where we can make proofs truly objective. There's a large mathlib project to formalize much of mathematics in Lean, and talk of a cslib as well. 

Lean excites me less, I'm not sure what we gain by formalizing proofs besides certainty. Will Fermat's last theorem be any more true once we formalize it? One could argue Lean also helps AI reason about mathematics in a grounded way, though could AI just get lost in the logical weeds?

I worry about the heavy task of converting proofs to Lean, even with AI help, takes away from time spent finding and proving new theorems and we lose the beauty of the proofs. If there was a fully lean verified proof of P ≠ NP that you couldn't understand, would you find that satisfying?

Goodhart's law: Ken Jennings and Types of Knowledge

Goodhart's law: When a measure becomes a target, it stops being a measure.  

I was watching the show Masterminds where Ken Jennings is one of the Masterminds. Here is what happened: 

Brook Burns (the host): The only vice president in the 20th century with initials H.H. was Hubert Humphrey. Who was the only vice president in the 19th century with initials H.H?

Bill Gasarch shouting at the screen: Hannibal Hamlin, Lincoln's VP for his first term! This is really obscure so this may be a rare case where I get a question right that Ken Jennings does not know!

Ken Jennings: Hannibal Hamlin. 

Bill Gasarch: Darn! However, I suspect he studied lists of presidents and vice presidents for the purpose of doing well on Jeopardy and now other shows. My knowledge is more natural in that I read books on presidents and vice presidents (best book, maybe the only book,  on Vice Presidents: Bland Ambition).  My knowledge of it is different from his. I tend to think my knowledge is more legitimate, though it would be hard to make that statement rigorous. If on quiz shows they asked follow-up questions, that might help alleviate this problem, if it is indeed a problem.

Misc: 

Ken Jennings is a Mormon so he does not drink or know about drinks. However, before going on Jeopardy he studied drinks for the show.

Ken Jennings does know novelty songs and that is legit since novelty songs comes up rarely on Jeopardy so I doubt he studied them in his preparation for going on Jeopardy. 

a) He knew that Shel Silverstein wrote A boy named Sue which was sung by Johnny Cash

b) He knew that Johnny Cash did a cover of Shel Silverstein's I'm being swallowed by a boa constrictor.

c) During his streak there was a category on novelty songs. He got 4 out of the 5 correct, and the one he got wrong I could tell he really knew. I got them all right, and faster, but Ken Jennings gets my respect for his legit knowledge of novelty songs. See here for the questions and answers. 

Goodharts law (maybe): Jeopardy is supposed to be about what people know. Is it supposed to be about what people study? Does studying for it help you  for things other than quiz shows?

When I watch a quiz show and get a question right there are levels of legitimacy:

1) I know the area naturally. 

2) I saw the question and answer on a different show and and I then looked it up and know more about it. So this is now legit.

3) I saw the question and answer on a different show and just know it as stimulus-response with very little understanding. Example: Kelly Clarkson was the first winner on American Idol, but I have never seen the show and only vaguely know how it works.

4) I saw the question and answer on the show I am watching, the exact episode, and I am watching a repeat. Sometimes I know stuff I don't normally know and then say OH, I've seen this episode before. 

Back to my point:

Is Ken Jennings memorizing lists a less-legit form of knowledge? If so, how to make that statement rigorous?

Is this what AI does? See also Chinese Room since that's also about a device that gets the right answers but perhaps for the ''wrong'' reason. 

A Probability Challenge

Last week I had the pleasure of meeting Alex Bellos in Oxford. Among other things Bellos writes the Guardian Monday puzzle column. He gave me a copy of his latest book, Puzzle Me Twice, where the obvious answer is not correct. I got more right than wrong, but I hated being wrong. Here is one of those puzzles, Sistery Mystery (page 28), which is a variation of a puzzle from Rob Eastaway. 

Puzzle 1: Suppose the probability of a girl is 51% independently and uniformly over all children. In expectation, who has more sisters, a boy or a girl?

Go ahead and try to solve this before reading further.

In any family with both boys and girls, each boy will have one more sister than each girl. For example in a family with four girls, each boy has four sisters and each girl only has three. Thus boys have more sisters on average.

Wrong, it's exactly the same. To see this consider Alex, the sixth child of a ten-child family. The number of Alex's sisters is independent of Alex's gender. This is a pretty robust result, it doesn't depend on the probability of a child being a girl, or if we allow non-binary children, or if the distributions aren't identical, say the probability of a girl is higher for later kids in a family. All you need is independence.

So what's wrong with my initial intuition that in every family boys have more sisters than girls. Eastaway suggests this gets balanced by the families of a single gender, but this happens rarely for large families. Instead it's a variation of Simpson's paradox. The naive argument doesn't account for the fact that girls are overrepresented in girl-heavy families. Consider a family of two boys and eight girls. Each of the two boys has eight sisters but four times as many girls have seven sisters, which adds to the expected value more to the girls than the boys.

If you lose independence the solution may not hold, for example if we have identical twins. 

I'll leave you with one more puzzle.

Puzzle 2: Suppose you are in a country where each family has children until they get their first boy. In this country, do boys or girls have more sisters on average?

Answer below.

In Puzzle 2 we lose independence and if Alex is a girl, she's more likely to be in a family with many girls. Indeed if boys and girls have equal probability, when you work out the infinite sums in expectation a boy will have one sister and a girl will have two.

ChatGPT gets an easy math problem wrong (I got it right). How is that possible?

A commenter on this post asked for me (or anyone) to solve the problem without AI:

A,B,C,D,E are digits (the poster said A could be 0 but I took A to be nonzero)  such that

ABCDE + BCDE + CDE + DE + E = 20320.

(CLARIFICATION ADDED LATER: We allow two letters to map to the same digit.) 

I solved it completely by hand. You can try it yourself or look at my solution which is here. I found seven solutions. 

I THEN asked ChatGPT to give me all solutions to see if I missed any. 

 

I had it backwards. ChatGPT missed some solutions.  The entire exchange between chatty and me is here.

I asked it how it could get it wrong and how I can trust it. It responded to that and follow-up questions intelligently. 

Note that the problem is NOT a Putnam problem or anything of the sort. But I've read that AI can solve Putnam problems. SO, without an ax to grind I am curious- how come ChatGPT got the abcde problem wrong.

Speculative answers

1) The statement AI has solved IMO problems refers to an AI that was trained for Putnam problems, not the free ChatGPT. For more issues with the AI-IMO results see Terry Tao's comments here

2) ChatGPT is really good when the answer to the question is on the web someplace or can even be reconstructed from what's on the web. But if a problem, even an easy one, is new to the web, it can hallucinate (it didn't do that on my problem but it did on muffin problems) or miss some cases (it did that on my problem). 

3) It's only human. (It pretty much says this.)

4) The next version or even the paid version is better!  Lance ran it on his paid-for chatGPT and it wrote a program to brute force it and got all 7 solutions. 

5) I said ChatGPT got the problem wrong. If a student had submitted the solution it would get lots of partial credit since the solution took the right approach and only missed a few cases. So should I judge ChatGPT more harshly than a student? Yes. 

The question still stands: How come ChatGPT could not do this well defined simple math problem. 

Joe Halpern (1953-2026)

Computer Science Professor Joseph Halpern passed away on Friday after a long battle with cancer. He was a leader in the mathematical reasoning about knowledge. His paper with Yoram Moses, Knowledge and Common Knowledge in a Distributed Environment, received both the 1997 Gödel Prize and the 2009 Dijkstra Prize. Halpern also co-authored a comprehensive book on the topic.

Halpern helped create a model of knowledge representation which consisted of a set of states of the world, and each person has a partition into a collection of sets of states, where states are in the same partition if that person can't distinguish between those states. You can use this system to define knowledge and common knowledge, and model problems like muddy children. It also serves as a great framework for temporal logic. 

Halpern led the creation of the Computing Research Repository (CoRR), a forerunner of arXiv, and would later moderate CS papers for arXiv. 

Joe Halpern was the driving force behind the Theoretical Aspects of Rationality and Knowledge (TARK) conference, which attracts philosophers, economists, computer scientists and others to discuss what it means to know stuff. I had two papers in TARK 2009 in Stanford. But my favorite TARK memory came from a debate at the 1998 TARK conference at Northwestern. 

Consider the centipede game, where two players alternate turns where each can either play to the right (R/r), or defect (D/d) to end the game immediately, with payouts in the diagram below.

The game is solved by backward induction, working out that in each subgame the player does better defecting.

The debate asked the following. Player 1 needs to think about the backward induction of the future moves, considering the case where player 2 played right in its first move. But this is an irrational move, so why should you assume player 2 is being rational when playing its second move later on?

Someone said such reasoning is fine, like when we assume that square root of two is rational, in order to get a contradiction. The counter argument: Square root of two does not "choose" to be irrational.

Thank you Joe for helping us think about knowledge and giving us the forums to do so.

Assigning Open Problems in Class

I sometimes assign open problems as extra credit problems. Some thoughts:

1) Do you tell the students the problems are open?

YES- it would be unfair for a student to work on something they almost surely won't get.

NO- Some Open Problems are open because people are scared to work on them. Having said that, I think P vs NP is beyond the one smart person phase or even the if they don't know it's hard maybe they can solve it phase.

NO- See Page 301 of this interview with George Dantzig where he talks about his mistaking an open problem for a homework and ... solving it.

CAVEAT---There are OPEN PROBLEMS!!! and there are open problems???  If I make up a problem, think about it for 30 minutes, and can't solve it, it's open but might not be hard. See next point. 

 I tell the students:

This is a problem I made up but could not solve. It may be that I am missing just one idea or combination of ideas so it is quite possible you will solve it even though I could not. Of course, it could be that it really is hard. 

A friend of mine who is not in academia thought that telling the students that I came up with a problem I could not solve, but maybe they can,  is a terrible idea. He said that if a student solves it, they will think worse of me. I think he's clearly wrong.  If I am enthused about their solution and give NO indication that I was close to solving it (even if I was) then there is no way they would think less of me.

Is there any reason why telling the students I could not solve it but they might be able to is a bad idea? 

2) Should Extra Credit count towards the grade? (We ignore that there are far more serious problems with grades with whatever  seems to make them obsolete: Calculators, Cliff  notes,  Cheating, Encyclopedias, Wikipedia, the Internet, ChatGPT, other AI, your plastic pal who's fun to be with.) 

No- if they count towards the grade then they are not extra credit. 

I tell the students they DO NOT count for the grade but they DO count for a letter I may write them.

What do you do? 

The Future of Mathematics and Mathematicians

A reader worried about the future.

I am writing this email as a young aspiring researcher/scientist. We live in a period of uncertainty and I have a lot of doubts about the decisions I should make. I've always been interested in mathematics and physics and I believe that a career in this area would be a fulfilling one for me. However, with the development of AI I'm starting to have some worries about my future. It is difficult to understand what is really happening. It feels like everyday these models are improving and sooner rather than later they will render us useless.

I know that the most important objective of the study of mathematics/science is understanding and that AI will not take that pleasure away from us. But still I feel that it will be removing something fundamental to the discipline. If we have a machine that is significantly better than us at solving problems doesn't science lose a part of its magic? The struggle that we face when trying to tackle the genuinely difficult questions is perhaps the most fascinating part of doing research.

I would very much like to read about your opinion on this subject. Will mathematicians/scientists/researchers still have a role to play in all of this? Will science still be an interesting subject to pursue?

There are two questions here, the future of mathematics and the future of mathematicians. Let's take them separately.

Looks like the same letter went to Martin Hairer and highlighted in a recent NYT article about the state of AI doing math and the too-early First Proof project. According to Hairer, "I believe that mathematics is actually quite ‘safe', I haven’t seen any plausible example of an L.L.M. coming up with a genuinely new idea and/or concept."

I don't disagree with Hairer but the state of the art can quickly change. A few months ago I would have said that AI had yet to prove a new theorem, no longer true.

It's too early to tell just how good AI will get in mathematics. Right now it is like an early career graduate student, good at literature search, formalizing proofs, writing paper drafts, exploring known concepts and simple mathematical discussions. But no matter how good it gets, mathematics is a field of infinite concepts, there will always be more to explore as Gödel showed. I do hope AI gets strong at finding new concepts and novel proofs of theorems, and see new math that might not have happened while I'm still here to see it.

For mathematicians the future looks more complicated. AI may never come up with new ideas and Hairer might be right. Or AI could become so good at theorem proving that if you give a conjecture to AI and it can't resolve it, you might as well not try. The true answer is likely in-between and we'll get there slower rather than faster. 

People go into mathematics for different reasons. Some find joy in seeing new and exciting ideas in math. Some like to create good questions and models to help us make sense of mathematical ideas. Some enjoy the struggle of proving new theorems, working against an unseen force that seems to spoil your proofs until you finally break through, and impressing their peers with their prowess. Some take pleasure in education, exciting others in the importance and excitement of mathematics. These will all evolve with advances in AI and the mathematician's role will evolve with it.

My advice: Embrace mathematics research but be ready to pivot as AI evolves. We could be at the precipice of an incredible time for mathematical advances. How can you not be there to see it? And if not, then math needs you even more.

I used to think historians in the future will have too much to work with. I could be wrong

 (I thought I had already posted this but the blogger system we use says I didn't. Apologies if I did. Most likely is that I posted something similar. When you blog for X years you forget what you've already blogged on.) 

Historians who study ancient Greece often have to work with fragments of text or just a few pottery shards. Nowadays we preserve so much that historians 1000 years from now will have an easier time. Indeed, they may have too much to look at; and have to sort through news, fake news, opinions, and satires, to figure out what was true.

The above is what I used to think. But I could be wrong. 

1) When technology changes stuff is lost. E.g., floppy disks.

2) (This is the inspiration for this blog post) Harry Lewis gave a talk in Zurich on 

The Birth of Binary: Leibniz and the origins of computer arithmetic

On Dec. 8, 2022 at 1:15PM-3:30PM Zurich time. I didn't watch it live (too early in the morning, east coast time) but it was taped and I watched a recording later. Yeah!

His blog about it (see here) had a pointer to the video, and my blog about it (see here) had a pointer to both the video and to his blog.

A while back  I was writing a blog post where I wanted to point to the video. My link didn't work. His link didn't work. I emailed him asking where it was. IT IS LOST FOREVER! Future Historians will not know about Leibniz and binary! Or they might--- he has a book on the topic that I reviewed here. But what if the book goes out of print and the only information on this topic is my review of his book? 

3) Entire journals can vanish. I blogged about that here.

4) I am happy that the link to the Wikipedia entry on Link Rot (see here) has not rotted.

5) I did a post on what tends to NOT be recorded and hence may be lost forever here.

6) (This is  bigger topic than my one point here.) People tend to OWN less than they used to. 

DVDs-don't bother buying! Whatever you want is on streaming (I recently watched, for the first time, Buffy the Vampire Slayer, one episode a day, on Treadmill, and it was great!)

CD's- don't bother buying!  Use Spotify. I do that and it's awesome-I have found novelty songs I didn't know about! Including a song by The Doubleclicks  which I thought was about Buffy: here. I emailed them about that it and they responded with: Hello! Buffy, hunger games, divergent, Harry Potter, you name it.

JOURNALS- don't bother buying them, its all on arXiv (Very true in TCS, might be less true in other fields). 

CONFERENCES: Not sure. I think very few have paper proceedings. At one time they gave out memory sticks with all the papers on them, so that IS ownership though depends on technology that might go away. Not sure what they do now. 

This may make it easier to lose things since nobody has a physical copy. 

7) Counterargument: Even given the points above, far more today IS being preserved than used to be. See my blog post on that here. But will that be true in the long run? 

8) I began saying that I used to think future historians will have too much to look at and have to sort through lots of stuff (using quicksort?) to figure out what's true. Then I said they may lose a lot. Oddly enough, both might be true- of the stuff they DO have they will have a hard time figuring out what's true (e.g., Was Pope Leo's ugrad thesis on Rado's Theorem for Non-Linear Equations? No. See my blog about that falsehood getting out to the world here. Spoiler alert- it was my fault.)

QUESTIONS:

1) Am I right--- will the future lose lots of stuff?

2) If so, what can we do about this? Not clear who we is in that last sentence. 

Sampling the Oxford CS Library

Wandering around maze known as the Computer Science building at Oxford I found the computer science library. Rarely these days do you see a library (and a librarian) devoted to computer science. The librarian found their copy of The Golden Ticket and asked me to inscribe and sign it, just like at Dagstuhl, perhaps the only other active CS library I know of.

It brought back memories of the early 90s when I would often head to the 
Math/CS library at the University of Chicago to track down some conference or journal paper. Now we just click and download but you miss finding something else interesting in the proceedings or the stacks in general.

I had time to kill so I wandered around the library finding memories in the stacks including the 1987 STOC Proceedings, home to my first conference paper, The complexity of perfect zero-knowledge. The paper might be best known for my upper bound protocol which is republished here in its entirety. 

That's how I wrote it nearly four decades ago, without proof just an intuition why it works. Those were the days. I did work out the full covariance argument in the journal version though I missed other bugs in the proof. 

The upper bound requires the verifier to have a random sample of the distribution unknown to the prover. Rahul Santhanam, who is hosting my visit to Oxford, asked if the converse was known. Goldreich, Vadhan and Wigderson, in the appendix of their Laconic Prover paper, show a sampling protocol based on the upper bound on the size of a set, though the sample is not completely unknown to the prover. Neat to revisit questions from my first conference paper. 

Oxford CS Librarian Aza Ballard-Whyte

Before the ChatGPT-HW debate there were other ``If students use X to do their HW'' debates

Lance and I had a blog-debate about What to do about students using ChatGPT to do their Homework.

Some commenters pointed out that we've been here before. I will now list past technologies that looked like they were a problem for student assignments and ponder what happened. 

If students can consult diagrams in their text then they will lose the ability to I DON"T KNOW AND I DON"T CARE . I did a post about this  titled In the 1960's students protested the Vietnam war!/In 1830 students protested...Math? I suspect that students eventually got to consult their texts. Actually, the entire topic of geometry of conic sections,  seems to have gone away.

If students learn how to read then they will lose the ability to listen to their elders tell stories and also lose the ability to memorize. I've heard this was a concern though I don't really know if it was. In any case people are probably worse at memorizing than they used to be, but the plus of having books and reading far outweighs the negative of less good memories. 

If students use spellcheck they will forget how to spell I think people are sloppier with first drafts than they used to be since they know that spellcheck will catch their spelling errors. And even before ChatGPT there were programs to check grammar as well. My spell check wants me to replace ChatGPT with catgut. This points to the need to use spellcheck carefully which foreshadows having to use ChatGPT carefully. My spellcheck does think that spellcheck is spelled correctly.

If students have calculators they will forget that 7*8 equals... hmm, I forgot: I think we ask much fewer questions depending on calculation than we used to.  Do kids in grade school still memorize times-tables? If so, then up to what number?  In my blog post on Numbers That Look Prime But Aren't, I casually mentioned that students learn up to 12x12 but I do not know if that's true. 

SO- for those of you who have kids in grade school, leave a comment on if they 

a) Memorize Times Tables.

b) Learn an algorithm for multiplication ( O(n^2) or O(n^{1.58}) or  O(n log n)) . I used Wikipedia for the pointer to the O(n^{1.58}). The entry describes the algorithm very well. I used Wikipedia for the O(nlog n) algorithm. That entry just says that there is a galactic algorithm (one that needs very large n to be worth using). They did not give the algorithm or a pointer to a paper that has it.) 

c) Are allowed calculators on exams.

d) Some combination of the above or something else. 

If students use Encyclopedias they will not be using primary sources. Over time Encyclopedias became seen as primary sources. My proofreader relayed the following to me: When I was in fourth grade I weren't supposed to use Encyclopedias for their reports if the library had suitable books. So the trick was to find a topic that the library did not have suitable books on. My proofreader is only a few years older than me, and lived in the same state, but I was allowed to use Encyclopedias. 

If students use Wikipedia they will not be using primary sources. I don't hear this debated anymore but I am not sure how the issue was resolved, or if it was resolved. If someone who has kids in school knows, please leave a comment. 

Annie and Lance Fortnow had a blog entry about the Wikipedia issue. 

I reviewed a book titled Should you Believe Wikipedia? by Amy Bruckman. The review is here. Spoiler alert: She thinks yes but I am more skeptical.

I once needed a list of ER-complete problems and asked an expert if there was a survey. He said that the best source was the Wikipedia page. For other examples of Wikipedia being the only source  see this blog post.

A similar issue is referring to papers on arXiv that have not been refereed. That might be the topic for a future blog post. 

If the first programming language is in a high-level language the students will not learn assembly code and stuff about how computers really work. This has happened. I think students do not know as much about low-level code as they used to. Is that a problem? This type of concern happens whenever a  higher level language is available. Students using  ChatGPT  to write code for you is another example of this issue, though it also has other problems. 

If students learn typing too early they will not learn cursive. I am an early example of this---my handwriting was bad (still is) so I eagerly took a typing class in my school in 5th grade (the class was 13 girls whose parents wanted them to be secretaries, and me) and worked really hard at it and began handing in typed book reports.  

The only letters I know how to do in cursive are

 W I L L I A M   G A S A R C H  

and only  in that order.  

ANYWAY, people have lost the ability to write in cursive, or even write in print neatly.  Drew Faust, a former history professor at Harvard (she retired in 2018) has pointed out that students have a hard time reading cursive in her article Gen Z Never Learned to Read Cursive.

I ask non-rhetorically, is losing the ability to read or write cursive  a problem? 

Takeaways: 

1) (From the prior blog on ChatGPT) Grading has been broken for a long time. ChatGPT just makes that more obvious.

2) When a  new technology comes along we may have to rethink education. 

The Fighting Temeraire (Re)visited

The Fighting Temeraire by JWM Turner

A year ago I wrote about an experiment I ran to learn about the modern period of art from ChatGPT. Chatty picked four paintings to discuss and I wrote about Joseph Mallord William Turner's The Fighting Temeraire. To review, the Temeraire fought in the Battle of Trafalgar but in this painting it's being towed by a steamboat up the Thames to be broken down for parts. I liked the painting because it captured the change in technology from the great sailing ships to boats moving without sails. How technology can move us from the beautiful to the practical with parallels to what we see today.

I wrote the post based on high-resolution images but there is nothing like seeing a painting in person. So during a trip into London, we made a pilgrimage to Room 40 of the National Gallery to see the Temeraire up close. The National Gallery is across the street from Trafalgar Square and a short walk from the Thames the Temeraire traveled in its last trip.

I had a new experience with the painting. I could see the brush strokes, and details I missed before, like the people on the steamboat and how its wheels pushed it along the water. More generally, the emotional experience of seeing this last trip of a great ship. A reminder that no matter how digital our world gets, seeing art in its original form brings the artist's true intentions to mind.

In the same room hang another JMW Turner masterpiece, Rain, Steam, and Speed - The Great Western Railway.

Rain, Steam, and Speed - The Great Western Railway by Turner

Turner painted The Great Western Railway in 1844 less than a decade after the train line started running. Like the Temeraire captures the change in technology, this big fast train moving quickly towards the viewer in a quiet countryside. On the right side of the painting, a bit hard to see even in person, is a man with a horse-drawn plough, and a small boat on the river on the left.

Coincidentally I took the Great Western Railway into London that day, but it's not the same railroad company.

Turner captured a new time in history, where man could travel faster than by horse on land and by wind on the sea, in the early days of the industrial revolution, a reminder of the technological changes we see today. But also the importance of the museum, of seeing something in person that no digital experience can replicate and a location where you can focus on art, undistracted by anything else, except other art.

Two hundred years from now will someone go to a museum to see art that captures the early days of the AI revolution? And will it be generated by a human?

Online Talks on Accessible Theorems!

Bogdan Grechuk has written a book Landscapes of 21st Century Mathematics that came out in 2021. There is a revised version coming out soon. The theme is that he takes theorems whose statements can be understood and describes them in 5–10 pages. No proofs, but lots of context and, most importantly, if you read all 800 pages of it you know about many areas of mathematics and where to look things up. He is organizing a series of online seminars with accessible talks about great recent theorems featuring world-renowned mathematicians:

• January 28, 2026. Prof. Boaz Klartag. “Sphere packing.” join the talk here.
• February 11, 2026. Prof. Avi Wigderson. “Expander graphs.” join the talk here.
• March 4, 2026. Prof. Assaf Naor. “Sparsest cut.”
• April 2026. Prof. Harald Helfgott. Date and topic to be decided.
• May 20, 2026. Prof. Ben Green. “The polynomial Freiman–Ruzsa conjecture.”

More details can be found on the seminar webpage and the blog devoted to great accessible theorems across mathematics.

Community

I once had a provost who felt that academic departments hindered the university as they tended to silo the faculty. He would argue we should eliminate departments and that would increase cross-disciplinary work. That went nowhere of course.

He missed a critical point. While departments play an official role in hosting academic programs, they importantly serve as the main community for the faculty within the department. You see your colleagues in the department in meetings, at seminars and just walking by them in the hallways. They are your peers and the ones who hired you and judge your promotion and tenure cases. You will argue with them but you all share a common mission to make your department as strong as possible, so you can attract even better colleagues.

At Oxford, most faculty have a position at one of the 36 colleges, and faculty community comes in the college instead of the department. Many faculty walk from their department offices to have lunch in their college. The colleges are interdisciplinary, there are only at most a couple of computer scientists in each college. My visiting fellowship is with Magdalen College, not the CS department though I plan to spend some time there.

Does the Oxford system lead to more interdisciplinary research, or less research in the field? Not sure, and it's hard to run a controlled experiment.

We all have two communities, one where we work, but also our academic community, the people who do research in the same areas, the people in our field that we collaborate with and see at conferences and workshops. The ones we try to impress with our own research so they may accept our papers, give us grants, nominate us for awards and write good letters for us. That's the real challenge of interdisciplinary research--you hope to impress researchers in both fields, but really you just impress those in the intersection.

In computer science our academic communities are pretty small, usually the conferences where we publish regularly. I was having a conversation with another computer scientist at Oxford. I asked him which conference and he said ICALP B. I said I've published in ICALP A, completely different!

What to do about students using ChatGPT to do their homework?

Students are using ChatGPT to do their HW. Here are things I've heard and some of my thoughts on the issue (Lance also added some comments). I have no strong opinions on the issue. Some of what I say here applies to any AI or, for that matter, old-fashioned cheating by having your friend do the homework for you or by going to the web for the answer (Is ChatGPT  going to the web for the answer but with a much better search tool?)

1) Ban the use of ChatGPT.  That might be impossible. 

2) Allow them to use ChatGPT but they must take what it outputs and put it in their own words. This should work in an advanced course where the students want to be there and are mature, but might be problematic in, say, Freshman calculus. This is also a problem if you want to see if they can come up with the answer on their own. Lance: How do you know if the words come from AI or the student?

3) Assign problems that ChatGPT does not do well on. I can do this in Ramsey Theorem and (to some extent) in Honors Discrete Math but (a) over time it will be harder, and (b) this may be hard to do in a standard course like calculus. Lance: Pretty much impossible for most undergrad courses. Bill: Slight disagreement- computer science is a fast-moving field where recent papers can be used to get problem sets before ChatGPT has digested the paper. Sometimes. 

4) For some assignments have them use ChatGPT and hand in both what chatGPT output and the student's rewriting of it. If ChatGPT made a mistake they should also report that. Lance: Many would just have AI rewrite the AI. Bill: Alas true.

5) Revise the curriculum so that the course is about HOW to use ChatGPT intelligently including cases where it is wrong and types of problems where it is wrong (do we know that?). Lance: And that changes as models change. Bill: True but not necessarily  bad. 

6) Make sure that a large percent of the grade is based on in-class exams.  I don't like this but it may become needed. Lance: I just grade homeworks for completion instead of correctness so that students didn't feel they needed to use AI to keep up with their classmates. Bill: I like the idea but they may still use AI just because they are lazy. 

7) The in-class exams will have variants of the homework so if a student used ChatGPT this will test if they actually learned the material (I do this anyway and warn the students on day one that I will do this to discourage cheating of any kind.) Lance: This works until the students wear AI glasses. Bill: I can't tell if you are kidding. More seriously, how far off is that? Lance: Not far.

8) Abolish Grades. Lance thinks yes and blogged about it here. Abstractly I wish students were mature enough to not need grades as a motivation. But even putting that aside, we do need some way to tell if students are good enough for a job or for grad school. Lance: We do need that, but grades aren't playing that role well anymore. Bill: What do do about this is a topic for another blog post. 

9) In a small class have the students do the homework by meeting with them and having them tell you the answer, and you can ask follow up questions. Perhaps have them do this in groups. Lance: Panos Ipeirotis used AI to automate oral questioning. Bill: Looks great, though I'll wait till they work out the bugs.

So---readers- what have been your experiences? 

Rational Functions Solved!

It's not every day that one of my open problems is solved, especially one that I asked about over three decades ago. Matt Kovacs-Deak, Daochen Wang and Rain Zimin Yang just posted a paper showing that if you have a Boolean function \(f\) and two polynomials \(p\) and \(q\) of degree at most \(d\) such that \(f(x)=p(x)/q(x)\) for every \(x\) of length \(n\) then \(f\) has decision tree complexity at most \(2d^4\).

Noam Nisan and Mario Szegedy had this beautiful paper in the early 90s showing that if a low-degree polynomial approximated a Boolean function, the decision tree complexity, the number of bits you needed to query from the input to decide the output, was bounded by the degree. For rational functions, that wasn't true, the majority function can be approximated by the ratio of two low-degree polynomials. This was the main tool used by Beigel, Reingold and Spielman when they showed that the complexity class PP is closed under intersection. 

Motivated by the complexity class C\(_=\)P\(\cap\)co-C\(_=\)P, I asked Mario if a low-degree rational function exactly computed a Boolean function, then would that function have low decision tree complexity. Mario thought about it, said it was an interesting problem, and added it as an open question to the journal version of his paper with Nisan.

I gave the problem to every student who asked me for an open combinatorial problem, as well as every reasoning AI model I could get my hands on. Iyer et al. posted a paper that examined this problem and showed it true for symmetric functions but the general case remained open until now.

The main insight of Kovacs-Deak, Wang and Yang is to consider the minimum block sensitivity, which is usually an uninteresting property of a function. The majority function for example has large maximum and average block sensitivity but low min block sensitivity. Nevertheless, their proof bounded the min block sensitivity by twice the degree of the rational function and used it to get a degree reduction of the rational function by querying a small number of bits of the input. Nice.

I always joked that if I told people the complexity application of the problem they would lose interest. The question came out of work with Steve Fenner, Stuart Kurtz and Lide Li on An Oracle Builder's Toolkit. C\(_=\)P is a generalization of co-NP where we ask if a gap-P function (the difference of two #P functions) is zero. So C\(_=\)P\(\cap\)co-C\(_=\)P is a generalization of NP\(\cap\)co-NP. This new theorem implies the technical result that if P = PSPACE unrelativized then P = C\(_=\)P\(\cap\)co-C\(_=\)P relative to a generic oracle. This implies an oracle where P = C\(_=\)P\(\cap\)co-C\(_=\)P and the PH is infinite and another where C\(_=\)P\(\cap\)co-C\(_=\)P has no complete sets.

If you are more of a quantum person, C\(_=\)P\(\cap\)co-C\(_=\)P = PostEQP while PostBQP = PP, and this work shows quite a difference between these classes. PP has complete sets and if P = PP then the polynomial-time hierarchy and even the counting hierarchy collapses to P.

Is `smells like' commutative?

 
1) Smells Like... Something

In many TV shows having to do with murder (and there are plenty of them), I’ve heard the following exchange:

        His breath smells like bitter almonds. So he was poisoned with cyanide

They’re either saying

        bitter almonds smell like cyanide

or

        cyanide smells like bitter almonds.

If you say X smells like Y, you mean that X is the new smell and Y is the familiar one.  However, on these shows, people seem to smell cyanide a lot,
yet I’ve never seen them smell or taste bitter almonds.  That's good since bitter almonds can be lethal (see here). So there should be mystery stories where bitter almonds are used and the cops say

             His breath smells like cyanide. So he was poisoned with bitter almonds.

I don’t know what either one smells like.

2) Rotten Eggs

In real life: My Darling grew up in Pittsburgh when it was still a steel-mill city.
She said she often smelled something that

        smelled like rotten eggs.

It was sulfur. But in telling me this, she assumes I’ve smelled rotten eggs.
I haven’t. But I have smelled other things that I was told smell like rotten eggs.

I think the phrase

        smells like rotten eggs

is often used by people who’ve never actually smelled rotten eggs.

3) Cardboard and Matzoh

A  blog post by Scott (see here), and my post about his post (see here), brought up the question:

        Does matzoh taste like cardboard?

I doubt any of us have actually tasted cardboard.

My proofreader once accidentally did, while eating takeout from a paper container. He says
(1) it doesn’t taste like matzoh, and
(2) it doesn’t taste like food — which matzoh does.

4) Dog Food

I’ve heard the cliché insult:

        Your cooking is so bad that it tastes like dog food.

I’ve never eaten dog food.  Maybe it tastes good.

5) When X Smells Like Y

If someone says X smells like Y, then:

a) If people know what Y smells like but not X, that’s informative.
b) If people know what X smells like but not Y, that’s not informative.
c) If I hear that X smells like rotten eggs and Y smells like rotten eggs, then I know X and Y smell the same —
even though I don’t know what rotten eggs smell like.
Oh wait — I do. They smell like X or Y!

6) How do the following fit into this discussion?:

a) The Nirvana song Smells Like Teen Spirit, video here.
b) The Weird AI song Smells Like Nirvana, video here.

Computational Depth

I'm posting from Oxford University where I will be spending the "Hilary Term" (through late March) as a visiting fellow at Magdalen College. If you are relatively local, reach out if you'd like to connect.

I plan to get back into research after thirteen years of administration, working primarily with Rahul Santhanam and his group. I haven't had a significant sabbatical or leave since Amsterdam 30 years ago, which is what comes from changing jobs too often.

Today I'd like to talk about a 2006 paper about a topic I first thought about in Amsterdam and will likely play a role in this visit, Computational Depth: Concept and Applications by Luis Antunes, Dieter van Melkebeek, Vinod Variyam and myself. 

In Amsterdam, I was hosted by Paul Vitányi and Harry Buhrman at CWI, and naturally worked on Kolmogorov complexity, the algorithmic study of randomness, as well as various problems in computational complexity.

Very simple strings don't have much information. Completely random strings have maximal Kolmogorov complexity but not particularly useful either as we can create our own random strings by flipping coins. Is there some way to measure useful information? 

In particular I had this question: If NP reduces to a sparse set then it has polynomial-size circuits. If NP reduces to a random set then it has polynomial-size circuits. Is this just coincidence or is there a broader principle here?

Charlie Bennett developed a notion of logical depth that has a similar philosophy but it is a difficult definition to work with. Luis Antunes, a student from Porto, came to work with me at NEC Research in New Jersey when I was there around the turn of the century. We developed this simpler notion of computational depth as the difference of two Kolmogorov measures, like time-bounded Kolmogorov complexity minus traditional Kolmogorov complexity. This would be small for both very easy strings and full random strings. With then NEC postdoc (and now Nebraska professor) Vinod Variyam, we found a connection to finding SAT witnesses and with DIMACS and IAS postdoc Dieter van Melkebeek (now Wisconsin professor), we came up with the notion of shallow sets, that generalized both random and sparse sets and, as hoped, if NP reduces to a shallow set than it has polynomial-sized circuits. Luis would title his PhD thesis "Useless Information".

Luis and I would later find a nice connection of computational depth to average-case complexity. 

Computational Depth had a resurgence of popularity with the rise of meta-complexity, with 50 of the original paper's 90 citations coming since 2020.

So I hope to find new applications of computational depth working with Rahul who's at the center of meta-complexity. Maybe computational complexity can help us understand machine learning better based on my frozen concentrate analogy, where the learning process removes the randomness, leaving structured information behind. 

AI and Research Papers

2026 will be a year of AI disruption across all of academia. Let's start by talking about AI is changing how we write research papers. Not the research itself (another post), just about the dissemination thereof.

Technology has changed research distribution since the invention of paper, and the printing press, typewriters, journals and conferences have all had dramatic effects. But we've already seen such dramatic changes just within my research career from authors doing their own formatting (TeX/LaTeX), instant distribution of papers (email/web) and collaborative writing (email/shared repositories/Overleaf).

How AI affects research writing follows a trend not unlike other AI applications. 

The Slop Phase

This is where AI does more harm than good. Useful for finding grammar mistakes and making fancy LaTeX tables, but if you use it for writing it may make stuff up, create citations out of thin air and put in words like "as a large-language model" in the text. Things have gotten much better with the latest models, I'm currently a fan of Gemini 3, but we haven't fully left the slop phase and anything AI written needs close verification.

The Verification Phase

2026 will be the year we go from humans having to look over AI generated content to AI having to look over human-generated content. Already you should give your paper, ideally in LaTeX, to the best model you have access to with a prompt asking for a critical review, or use a specialized service. You don't have to follow what the AI says but you should be open to improving your paper. Google provided an optional tool for those preparing STOC 2026 submission that many found valuable. In 2026 some conferences and journals will start requiring submitted papers to go through an AI review as well as a human review (for now), especially with those overwhelmed by submissions.

At some point, we may require authors, with AI help, have their theorems verified by theorem provers like Lean or Rocq. It will be a while, especially in computational complexity, where it is hard to properly state a theorem in formal logic, let alone its proof. 

The Collaborative Phase

Not just AI critiquing papers, but researchers and AI working together in paper writing. We already see pieces of this, having AI fill in details of arguments, give a first draft of an introduction, help create diagrams, find relevant citations, etc. Right now anything the AI writes requires a careful lookover by the researcher. As these models improve, we'll see more and more generated by the AI and researchers starting to trust the output, maybe more than they should. This will lead to...

The Generation Phase

Here the AI writes the LaTeX from scratch based on proof sketch given by the researchers. As an experiment, I "vibe-coded" a minor research paper and it went better than expected. 

The researcher will go and ask for updates and touch up the LaTeX at the end, but at some point the human won't ever touch the LaTeX file, just a prompt or a markdown file that generates it. 

By the end of the 1980s, researchers just trusted the LaTeX for formatting, fixing only syntax errors and overfull boxes. Most researchers (but not all) stopped fiddling with the look of the paper. At some point, and at higher stakes, we'll do the same with the paper content as well.

The Betty White Award for 2025

The Betty White Award goes to people who die at the end of the year--- too late to be on those articles with titles like 

                                                            people we lost this year.

The  point of the award is that news outlets and blogs should WAIT until Jan before having any articles that look back on the prior year. (I tell Lance we should have our end-of-the-year post in January just in case someone solves P vs NP the last week of December. [Lance: I'm not worried])

I made up the award in 2023. More recently, I looked at the web to see who would have won it in past years. My chart is here. As I was writing this post I wondered if there already was a Betty White Award for something else. The response is here. Short version: ChatGPT says that there is an informal award called that for reading cue cards really well on Saturday Night Live. As opposed to my award which is formal(?).

This year's list of winners so far has only one person:

Brigitte Bardot who died on Dec. 28, 2025, at the age of 91. She was an actress, singer, model, and activist (she was for animal rights and against interracial marriage---see her Wikipedia page for other views she had).  Like most celebrities who die when they are over 90, I suspect many people either don't know who she was or thought she was already dead. For more on her see her Wikipedia page here. Or type her name into chatty, though be warned that you may get some false statements. 

1) This year I am taking NOMINATIONS for additions to the list.  The number of winners can be more than 1. The max in a year has been 4 so far.  SO 

If you know of someone who

a) died between Dec. 20 and Dec. 31

b) is famous

then leave a comment.  DEADLINE: Jan. 8 at noon East Coast time. On Jan. 8 I will update this blog post with more winners if need be. 

I've already looked at Wikipedia's list of who died in 2025, here. If there is either (1)  someone on there who is more famous than I thought (that happened last year with Manmohan Singh) or (2)  there is someone not on the Wikipedia list who is famous to our community, let me know.

2) As noted above, the point of the award is to point out that publications should WAIT until Jan to have those lists. I am happy to say that Entertainment Tonight has found a good way to handle this---they had an article title Celebrity deaths 2025: Remembering those who've died this year but they are updating it (at least their online version). The updated version includes Bardot! See here. Paper publications  won't be able to do that. This may not matter---will there still be paper publications at the end of 2026? I blogged on paper free stuff here.