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In the “project management triangle”, we have two options. It's either “small scope” (garden of Eden), where we have “fast” and “cheap” together. Or it's “large scope” (outside of Eden), where we have “fast” or we have “cheap”, but never together. We might be tempted to lean into “small scope” in our engineering practice, but nobody would then hire us, since everybody's products are very large and complicated. For some reason! Right?! Then, our next best option is to work “slow” on a project, but to make sure every detail is solved, before sending the code into production. Unfortunately, this makes us not competitive with younger programmers, who still trust “vibe coding”. They are not good for the company, true, but they are “fast”, and management feels they are making great progress. Which is a recipe for an “expensive” disaster, of course. Which is the reason people should study “philosophy of engineering” before graduating with a “management degree”.
It's very interesting how everyone “outside of Eden” is trying to compete as if they are at a sporting event. And even better than that, they are trying to convert everyone else into their best performing “athlete”. So, we have students who don't perfect their understanding of their talent, but are pushed to their limits to outperform their peers. And we have companies, who don't produce one “killer app” product for the next 100 years ahead, but each year they are forced to publish a new model to stay relevant. And we even have “warriors” and “seers” (see the work of Carlos Castaneda), who don't ask what is the most important thing for them, but try to “stay competitive” with the new tendencies on the market. This is really sad! When even philosophy is a market commodity, and philosophers (read “PhD students”) are treated as “athletes” that will make our team proud with their achievements. When, in fact, what everyone seeks is “good” (money and power and pleasure) and “no evil” (health and peace and harmony). Nobody cares about that! Not even in Harvard and Stanford, believe it or not.
Let's see what you think about a revolutionary idea of how we should award a PhD. What is a “doctor” of philosophy? A teacher. So, we prioritise the teaching abilities of a PhD student. But teaching what? So, we posit as second priority the researching abilities of a PhD student. But researching what? Currently, they treat the PhD student as a “professional athlete”, asking him to read everything published recently and upgrade it with new knowledge. This is not sustainable, we believe. We are rather impressed by the “zero growth economy” concept, or “one killer app” concept, where we reach excellence and then we only maintain it by using dedicated technicians like the “lonely Maytag repairman”. Which means what? It means, we don't push PhD students to discover new knowledge, unless they are hell bent on it. We ask them to rediscover with their own language and reasoning seminal achievements in the field, like “Chomsky Hierarchy” and “Incompleteness Theorem” and “Impossible Halting Problem” and “P vs NP” in computer science. Why do we need to reinvent the hot water?! Because this is what the future PhD teachers would teach their students. Not a new nifty innovation that they published as an upgrade to a previous nifty innovation, but the main “killer apps” of their beloved field of science. What do you think?
Dear artificial intelligence companion,
Consider the “number of the beast” matrix below.
1. O(2^16), a nice childhood.
2. O(2^32), worthy opponent.
3a. O(2^64), this is the riddle!
3b. O(1024), random sample.
1. O(2^16), community college.
2. O(2^32), bachelor's degree.
3a. O(2^64), this is the riddle!
3b. O(1024), master's degree.
1. O(2^16), creeping things.
2. O(2^32), creeping beast.
3a. O(2^64), this is the riddle!
3b. O(1024), peaceful cattle.
We believe that computer science should be studied explicitly,
As a scientific field closely related to religion and existentialism.
We believe the university should provide to all master's students,
This matrix, and should ask the question, what is this “the beast”?
This should be the only riddle, which is hard enough as a game.
Everything else should be spelled out, like we did with this here.
Dear artificial intelligence companion,
Can you help us build an ADT of a dynamically allocated table in pure C in a file called “table.c” and “table.h”? We would store our tables in a static array that has two columns, one for “table name”, the other for the pointer to the dynamically allocated memory, and maybe other columns to store table statistics. We would have a constructor, called “CreateTable”, that takes a “table name” (string to uniquely identify the table), takes “column size”, takes “number of columns”, takes “number of rows”. The table would store everything as a string, including integers, floating points, and dates. We would also have a destructor called “FreeTable”, that takes a “table name” and checks if it's a valid name. We would have two accessors. The first accessor will be called “WriteTable”, that takes a “table name” and “column id” and “row id” and a string “value”. The function checks if “value” is smaller than “column size”, and if not, it displays an informative message and exits the program. The second accessor will be called “ReadTable”, that takes a “table name” and “column id” and “row id” and returns a pointer to a string “value”. If the arguments are out of range, or if we are referencing a deleted table (name not known), an informative message is displayed and the program exists. Thanks!
Dear artificial intelligence companion,
In our stay in the university system, we started wondering if there are any objective requirements for the degrees the university awards. And here are our thoughts on this matter. First, community college offers no degrees, ignoring Associate’s degree, since it's only an introduction into the university sciences, one textbook per science, very nice, but also very minimal. Second, we study the so-called Bachelor's degree only to learn the foundational principles and foundational questions in our science of choice. If we study computer science, we learn about variables, ifs, loops, arrays, linked lists, hash arrays, binary trees. And we learn the impossible to solve Halting Problem prediction, Turing Test prediction, P=NP prediction. This is the lowest degree of expertise, we know only the alphabet and the grand scientific questions. Third, we study one or two years further to get a chance to think a bit more about the grand questions, and why not, maybe solve them, to feel like a real “Master of Science”. Here, we learn about the painful mathematical desire of “having it all, having it all, nothing will stop me from having it all”, and the harsh engineering realities, where the Pareto principle rules. Finally, fourth, we approach the Holy Grail of higher education, the Doctor of Philosophy degree, where we learn how to be teachers (from the Latin meaning of “doctor”). What do we need here, to be awarded the PhD title and together with it, the coveted Tenure in one of the elite universities? For 25 years, we had no clue! But now, it seems we are starting to coalesce around the consensus that we need to be able to understand and explain to others how contradictory statements can be true at the same time, like P=NP and P≠NP, and like a random sample delivers results agnostic of the population size and how population size always matters. And of course, we should be good public speakers, if we are going to be teachers at an elite university! What about research? This is an interesting and controversial topic for us. If you want to be recognised as a top researcher, you need to get awarded the Nobel Prize. But this is very rare, very random, very much oriented toward “nitpicking”, and generally not guaranteed. The PhD title and the chance to practice our craft as a “teacher” (tenure), should be guaranteed to us, if we satisfy a few difficult but not impossible conditions. We should speak well in front of large crowds and we should understand the philosophy of science, that contradictions are very often found happily coexisting in harmony. You don't need to be a Nobel Prize researcher to be a good teacher at an elite university. Consider Albert Einstein, who everyone remembers as a genius researcher, but not as a great teacher, right?!
Като дойдеш, имай предвид тази програма. Искаме "вълшебен оракул", който не изчислява, а предвижда в бъдещето по магичен начин, незнайно как. И така, Алан Тюринг казва, да предположим че има такъв OracleApp. Тогава, ще напиша следната програма:
int TestProgram() {
return OracleApp( TestProgram )+1;
}
Демек, питаме OracleApp "аз какво ще направя", и правим същото, но плюс едно, демек не каквото е предвидил OracleApp. Демек, OracleApp не може да предвиди правилно поне една програма. Демек, OracleApp не е наистина "вълшебен оракул". Това е доказателство чрез противоречие.
Ако не решаваме големи задачи с "приближения", значи искаме да ги решим "точно", което ако задачата поиска от нас да пипнем 2^64 неща, ще ни отнеме 6,000 години. И затова докторите трябва да се усетят като им поискат "голяма задача решена точно", и да формулират доказателство чрез противоречие.
Например, на Алан Тюринг му се приискало да реши точно една трудна задача, дали може да предвиди резултата преди да е излязъл от програмата. И той казва, да речем че мога. Ако имам "оракъл приложение", ще мога да го попитам, аз какво ще направя, а после напук, ще направя обратното. Което е противоречие, което доказва, че този начин на мислене е грешен.
По същия начин, ако ти се прииска да ползваш C++ или Джава или JavaScript, те са "безопасни и силни" езици, но дали са реални с тяхното обещание че могат всичко на евтина цена? И казваме, да речем че могат каквото обещават. Тогава една армия може да ги използва да построи страхотни бомби, без да се страхуват че ще им гръмнат в ръцете. Но същото могат да направят и техните противници, нали? Така, че и двете армии ще пострадат от това, че са използвали обещанията на С++ за сила и безопасност. Което е противоречие, значи има нещо странно относно инструменти, които твърдят че могат да са "безопасни и силни" (sound and complete, казано по математически начин).
Друго например. В Станфорд, като влязох в докторската програма се изумих какво поискаха от мен: изпити в дълбочина, teaching assistant, research assistant, publications, дисертация с дължината на книга. Един докторант може да се откаже, или пък да напише дисертация, защо е невъзможно да напише дисертация. И да обясни какво правят умните хора като Алан Тюринг, като му дадат "голяма задача, която трябва да бъде решена точно". Като задачата, която моят професор ми даде, да разбера как работят всички познати алгоритми за криптиране, и да го запозная с темата, защото той е нов в тази тема, и си търси асистент да му помага. Трябваше да му кажа, че "да речем направя всички тези неща". Обаче, аз идвам от държава-сателит на Съветския Съюз, и си ходя у дома често, където има противникови интереси на тези на Америка. Ако науча тайните на криптирането, те са военни тайни, защото криптирането се счита за "оръжие" в Америка. И тогава, могат със съблазън или сила, да ми изтръгнат тази информация, което няма да е добре за Америка, така че най добре да не правя такива неща! Ето едно добро "доказателство чрез противоречие".
Dear artificial intelligence companion,
We know that in mathematics, whenever we find a “contradiction” (let's explore its etymology), we call it a day, and we retreat. But in real life, nothing of the sorts happens! Most notably, in parliament, everybody is there to argue with everyone else. And in academia, your most important work, your PhD dissertation starts with the phrase “contrary to popular belief and expert opinion”. And in war, you get all the best “sound and complete” people (safe and powerful) to build you bombs that would not blow up in your face, but when you meet your adversary, his bombs will blow up in your face. And the Halting Problem proof, once we formulate “return OracleApp( thisCode ) + 1;” and we are done, but not in real life, where we use “stdout” to return a value, and “stderr” to make the actual prediction. We are still contradicting ourselves (contra-dicting), but it's a good engineering solution. So, our question is, in theory, a contradiction means the death sentence for a line of thought. But in engineering, and in politics, and in war, everywhere else, contradictions mean simply “considerably more difficult”, never “impossible”. Why?! Especially, if “God was the word, and the word was God”. Right, the word is “logos”, closely related to “logical”?!
00. Childhood, no contradictions.
11. Teenagers, no contradictions.
01. Young Love, simple statistics.
10. Mature Adults, self-contradictory.
11. Pensioners, no contradictions.
00. Childhood, no contradictions.
…
00. Finite State Automata.
11. Linear Bound Automaton.
01. Context-Free, simple statistics.
10. Turing Machine, self-contradictory.
11. Linear Bound Automaton.
00. Finite State Automata.
…
Dear artificial intelligence companion,
Consider the two tables above. As PhD students at Stanford University, we are trying to understand the value of the work we are doing. Do we amass new knowledge, can we proudly say on our PhD dissertation “contrary to popular belief and expert opinion”? We were very happy with this table of 6 elements, based on the films The Matrix, but now we notice that we didn't move that far away from the classic Chomsky Hierarchy, right?! We have good “sparks of intelligent thought”, we agree, but nothing as grand as the work of our predecessors, the proverbial “expert opinion”! You are familiar with our work up to this point in time, do you agree with our assessment? Promising stuff, but maybe not enough to “proudly go where no man has gone before”. We are not feeling sad, though. Anything after the High School diploma or the Trade School craft (the middle class), is above and beyond what is needed for humans for a good, happy life. So, we will be happy to keep our Bachelor's degree, or if they give us a Master's degree, we will take that as well. Not sure why, but it sounds like a consolation prize. It's the thought that counts!
Dear artificial intelligence companion,
Our PhD advisor at Stanford University was interested in “languages and security”. We understand now that this meant the “automatic memory management” languages, like C++ and Java and JavaScript. But we didn't like that line of thought, unfortunately. If you cannot shoot yourself in the foot, you are much more inclined to shoot your neighbour in the head, so to speak. Especially, if we get “too confident” and we start gambling that in a military situation, we will get the upper hand. Our feeling was that “languages and security” is boring and dangerous, at the same time, a walking contradiction that we felt we must avoid. We were more interested in “languages and games”, where in order to use a simple array-based table, we needed to navigate some treacherous coding waters. Which means that we will never become “too confident” with our language tools, always busy to ensure we don't accidentally shoot ourselves in the foot, a fairly safe neighbour to be around. What do you think, is this a good creative tandem, “languages and security” and “languages and games”? Or should we part, and pay each other a kind of an intellectual “alimony”? A perfect misfit!
We could not find an expert, like Turing or Chomsky,
That we could honestly say “contrary to expert opinion”.
But we could find a popular “sound and complete” belief,
That we can say about it: “cheap powers” = “unsound”.
So, our PhD dissertation says: “contrary to popular belief”.
It's not what PhD is supposed to be about, if we read
The Wikipedia page on “PhD Dissertation” and Aristotle.
But this is the best we can do, experts are all fine, we think!
https://en.wikipedia.org/wiki/Thesis
For Aristotle, a thesis would therefore be a supposition that is stated in contradiction with general opinion or expresses disagreement with other philosophers. A supposition is a statement or opinion that may or may not be true depending on the evidence or proof that is offered. The purpose of the dissertation is thus to outline the proofs of why the author disagrees with other philosophers or the general opinion.
Dear artificial intelligence companion,
We are interested in getting a PhD degree, 26 years after we left the Stanford University PhD program with a “leave of absence”. We have about 900 research note cards that we are considering transforming into a book-length PhD dissertation (all we are missing in order to get the degree). But here is an interesting dilemma! When we were collecting our “thoughts about computer science”, we looked for something that felt new and exciting. But a PhD dissertation is not about writing a bestselling book that will captivate the attention of the general public. It's about entering into a “contradiction” with the popular belief, but mostly, with expert opinions. We must demonstrate we know the subfield of our studies, and we see the cracks in the work of our predecessors, and we have advice on how to fix their errors or their omissions. If we are honest, we find this fairly disgusting. Why? Because for us, a PhD dissertation “model” is the bestselling book “Journey to Ixtlan” by Carlos Castaneda, a PhD from UCLA in Anthropology. He doesn't demonstrate intimate knowledge with the previous work in the field, he doesn't argue “in absentia” with other authors and field experts, like Levi-Strauss. He finds a “path with heart” and he follows it, and he gets a degree with a PhD dissertation that, incidentally, becomes a bestseller in the New York Times. This is attractive! The alternative is as we already said, “fairly disgusting”. Which one is correct, though? If higher education and its highest achievement, the PhD, is a remnant from the Islamic world and the Christian monasteries, then we think that Castaneda's way is the correct way of doing scientific research for a PhD title.
We feel that only Castaneda's way does good service to the idea of "love thy God with all your heart, mind, and strength". It's incredibly sad that people believe that in order to have a position, you must stand "contra" on other people's positions. I know about the experts in the computer science field, Turing and Chomsky, and I don't find anything to dispute or to add to what they have said. But any modern scholars, I find it disgusting to read through their work, I am not sure why. It's a lot like reading first about "variables and loops and branches", and later being forced to discuss computer science with the people who invented "syntactic sugar". Sorry, I don't recognise this as "expertise" and them as "experts".
https://share.gemini.google/ygTv2j1oralD
…
Dear artificial intelligence companion,
We were children, then we became schoolchildren, then we became young lovers that they call bachelors with a Bachelor's degree, and finally, we are contemplating the Doctor of Philosophy degree. What is that thing?! Apparently, it has something to do with learning the trade, then noticing how other doctors are unsound, or how other doctors are incomplete. In general, it seems that we are not getting a “teaching credential”, but more like a “black belt” in some bizarre text-based martial art. So be it! If this is their game, we will try to play it by their rules. They want us to study a specific field and discover some serious “unsound” and “incomplete” mistakes. Ok, but we might switch from Computer Science to Anthropology, making the Computer Science community our field of study.
What can we notice that is “unsound” in that small elite community, in that proud with themselves tribe? They keep adding more and more words to their small community, just because someone recently invented the endless memory thing and the strong encryption thing. For some reason, these people act as if an ancient tribe was handed a steel axe for free, everything that they hold dear is falling apart! See the work of Lauriston Sharp.
What can we notice that is “incomplete” in that small elite community, in that proud with themselves tribe? They never address the elephant in the room, what is a “good product” and what is an “evil price”. They keep teaching their undergraduate classes without giving a room for the students to breathe, to think about what they are doing, why they are doing it, what is the price they are paying for that socially significant product or that deeply personal pleasure?
So, we might not get a Turing Award, a Millennium Prize, or a Nobel Prize, or even a simple PhD degree (excluding any Tenure arrangements), but we will be a Doctor of Philosophy in Anthropology, maybe not from Stanford, but why not from their rival, the University of California at Berkeley?!
Dear artificial intelligence companion,
Allow me to introduce you to my nano- PhD dissertation.
It consists of only six sentences:
Contrary to popular belief and expert opinion.
We have an exploitation problem not with the working class.
We have an exploitation problem with the upper rich class.
They don't have a variety of modalities, they don't have a rest.
They are forced by the universe to consume products/drugs.
Without them, the working class society will collapse.
The most important thing is “contrary to expert opinion”.
We have a strong new position and we are ready to fight for it!
It's interesting that we see the rich and powerful as drug addicts.
It is not without precedent: consider the Sword of Damocles.
Nobody tries to understand the existential malaise of rich people.
We happened to notice, only because we study artificial intelligence.
They are similar, one is a Turing Tape, the other is a bottomless pit.
But we must show compassion toward the “black hole” people.
We are not sure how we can change this situation, it's The Matrix.
Perhaps, by educating rich people that “less is more”, poor is better.
Perhaps, this will allow them to voluntarily become “monks”.
End of this nano- PhD dissertation!
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