It is not bad to say "I told you so".
Sometimes I'm surprised by how experts react to developments that were bound to happen. Frankly, I feel the reactions are signs of self-censorship that harm everyone.
I'm referring to the latest AI 'misuse' trend, the abuse of women by deep-fakes requested to Grok. To see why this was foreseeable, let's run a quick mental exercise.
Imagine you have 10 objects: Object1, Object2, and so on to Object10. Each object has the 'pleasantness' property. ObjectK.pleasantness is a number that reflects how pleasant ObjectK is. We can rewrite ObjectK.pleasantness by its proportion with respect to all the objects, i.e., ObjectK.pleasantness = ObjectK.pleasantness / Tp. Where TP = Object1.pleasantness + ... + Object10.pleasantness
Now the pleasantnesses (what a word) make a probability function, call it Pp.
Throw all the objects into an opaque urn and draw objects from the urn following Pp, that is, if ObjectI is more pleasant than ObjectL, then it is more likely to be drawn from the urn.
Once you draw an object, you log which object you drew and then return it to the urn. You keep going until you are tired.
Perhaps your pleasantness perception does not agree with Pp. For example, Pp might say Object7 is the most pleasant, but to you, Object2 is the most pleasant.
It is quite easy to go over your log and discard the Objects that you don't like or that you find less pleasant than others. This could be tiresome, especially if you disagree with Pp. If Object7 is the most likely and Object2 the least likely, you should expect to discard many more objects than the ones you keep.
On the other hand, if you somewhat agree with Pp, you might be happy with any draw.
Moreover, on your log, you could keep a record of the (partial) sum of the objects you have seen, instead of the full list.
For example, if your original log showed the 4th draw as Object7, Object5, Object2, it will now show Object7+Object5+Object2, assuming objects can be added.
Let's complicate this experiment a bit more. Imagine the objects are colors, and you throw "magical" 1-unit colors into the Main urn. You also have a second opaque urn that we will call "Mixing urn". Each time you draw a magical 1-unit color from the Main urn, you empty its contents into the Mixing urn and then return the empty unit into the Main urn. Since the unit is magical, it will refill with its color once it is inside the Main urn.
You cannot peek inside the Mixing urn, and you cannot see which color you draw from the Main urn. Colors are drawn following Pp.
The Mixing urn will give you a 1-unit color, the result of mixing all the colors you drew. Once you mix two colors, you can not separate them, so by only seeing the resulting color, you can not determine the proportions of each color.
Let's say you know the 10 base colors, you are allowed to create as many mixed colors as you wish, but you do not know Pp. Would you be able to make "good" guesses about Pp? Probably, let’s say Red is one of the most liked colors, if all the mixed colors seem to have a 'red touch' (whatever that means), you could conclude that Red must have a high probability. On the other hand, if all the colors seem to be bright, you could conclude that black or dark blues (assuming they are base colors) must have low probabilities.
Probably now you are devising how well you can approximate Pp by playing with the base colors.
For example, you can take all the subsets of 2 and create mixed colors, see what happens, then all subsets of 3, and so on.
Final complication. Imagine you add a new dimension: "intensity" to each base color. How many? To keep it 'real', let's use the RGBA model and consider 256 different intensities. Now we have 2560 'different' colors and Pp is adjusted to consider the intensity. For example, light blue could be more pleasant than dark blue, however, both are 'blue'. If we repeat the previous experiment, deducing the proportions from the mixed color seems to become a bit more difficult problem; in essence, it is the same problem, but with bigger numbers.
Now, what does this mental exercise have to do with Grok and corporate AI in general? Well, our mental exercise is an oversimplification of how dominant AI models work: Each time you give the model a prompt (a series of draws in our example), the model gives you a response (a magical mixed color). We do not know the precise characteristics of the training data (base colors' pleasantness), but we may know some of the base colors (academic, formal, humorous, sexist, racist). Moreover, our own pleasantness concept may not agree at all with the underlying pleasantness of the training set. But just as in our mental exercise, we could draw some conclusions about the pleasantness of the categories in the training set by examining the responses to various prompts. However, it is impossible to filter out categories (colors) from the response (mixed color).
The signs were/are all over, or, as I said at the beginning, "I told you so".
Remember a famous Time article about OpenAI hiring Kenyans to make ChatGPT 3 'less toxic' from 2 or 3 years ago? If you create and create and create mixed colors over and over and over again, and all seem to have a Red component, what can you conclude?
If you have enough 'problems' with what your AI generates, big enough to devise a way to make it less toxic, is your training set valuable? I'll give you a sec to think about this one from a social and corporate perspective.
Perhaps you are thinking what I'm thinking, we can fix it, we just did it in our colors mental exercise, just remove the base colors we don't want. Yes, we can do that, but there are 2 things to consider. 1) Read this post backwards. If we consider colors and intensities, it is difficult to correctly classify them: is this Red, or Rose, or Light Red?
2) If you remove that which has a higher probability of being liked, what does it mean to your average user, to your business?
As biases in AI models came to light for the general public, experts' debates often focused on the implications of using these tools for society. How it reinforced stereotypes, how it was detrimental for minorities, e.g., AI-approved bank loans. However, let us not forget that, with our "Internet data trained" AI models, we are being forced to look at ourselves in a mirror that, many times, enhances a reflection we have conveniently decided to bury; a reflection that scares us.
The Internet didn't build itself; we built it.
If we want better AI models, we need better training data.
To get better massive training data, we need a better Internet content.
To get a better Internet content, we need a healthier society.
Sometimes I'm surprised by how experts react to developments that were bound to happen. Frankly, I feel the reactions are signs of self-censorship that harm everyone.
I'm referring to the latest AI 'misuse' trend, the abuse of women by deep-fakes requested to Grok. To see why this was foreseeable, let's run a quick mental exercise.
Imagine you have 10 objects: Object1, Object2, and so on to Object10. Each object has the 'pleasantness' property. ObjectK.pleasantness is a number that reflects how pleasant ObjectK is. We can rewrite ObjectK.pleasantness by its proportion with respect to all the objects, i.e., ObjectK.pleasantness = ObjectK.pleasantness / Tp. Where TP = Object1.pleasantness + ... + Object10.pleasantness
Now the pleasantnesses (what a word) make a probability function, call it Pp.
Throw all the objects into an opaque urn and draw objects from the urn following Pp, that is, if ObjectI is more pleasant than ObjectL, then it is more likely to be drawn from the urn.
Once you draw an object, you log which object you drew and then return it to the urn. You keep going until you are tired.
Perhaps your pleasantness perception does not agree with Pp. For example, Pp might say Object7 is the most pleasant, but to you, Object2 is the most pleasant.
It is quite easy to go over your log and discard the Objects that you don't like or that you find less pleasant than others. This could be tiresome, especially if you disagree with Pp. If Object7 is the most likely and Object2 the least likely, you should expect to discard many more objects than the ones you keep.
On the other hand, if you somewhat agree with Pp, you might be happy with any draw.
Moreover, on your log, you could keep a record of the (partial) sum of the objects you have seen, instead of the full list.
For example, if your original log showed the 4th draw as Object7, Object5, Object2, it will now show Object7+Object5+Object2, assuming objects can be added.
Let's complicate this experiment a bit more. Imagine the objects are colors, and you throw "magical" 1-unit colors into the Main urn. You also have a second opaque urn that we will call "Mixing urn". Each time you draw a magical 1-unit color from the Main urn, you empty its contents into the Mixing urn and then return the empty unit into the Main urn. Since the unit is magical, it will refill with its color once it is inside the Main urn.
You cannot peek inside the Mixing urn, and you cannot see which color you draw from the Main urn. Colors are drawn following Pp.
The Mixing urn will give you a 1-unit color, the result of mixing all the colors you drew. Once you mix two colors, you can not separate them, so by only seeing the resulting color, you can not determine the proportions of each color.
Let's say you know the 10 base colors, you are allowed to create as many mixed colors as you wish, but you do not know Pp. Would you be able to make "good" guesses about Pp? Probably, let’s say Red is one of the most liked colors, if all the mixed colors seem to have a 'red touch' (whatever that means), you could conclude that Red must have a high probability. On the other hand, if all the colors seem to be bright, you could conclude that black or dark blues (assuming they are base colors) must have low probabilities.
Probably now you are devising how well you can approximate Pp by playing with the base colors.
For example, you can take all the subsets of 2 and create mixed colors, see what happens, then all subsets of 3, and so on.
Final complication. Imagine you add a new dimension: "intensity" to each base color. How many? To keep it 'real', let's use the RGBA model and consider 256 different intensities. Now we have 2560 'different' colors and Pp is adjusted to consider the intensity. For example, light blue could be more pleasant than dark blue, however, both are 'blue'. If we repeat the previous experiment, deducing the proportions from the mixed color seems to become a bit more difficult problem; in essence, it is the same problem, but with bigger numbers.
Now, what does this mental exercise have to do with Grok and corporate AI in general? Well, our mental exercise is an oversimplification of how dominant AI models work: Each time you give the model a prompt (a series of draws in our example), the model gives you a response (a magical mixed color). We do not know the precise characteristics of the training data (base colors' pleasantness), but we may know some of the base colors (academic, formal, humorous, sexist, racist). Moreover, our own pleasantness concept may not agree at all with the underlying pleasantness of the training set. But just as in our mental exercise, we could draw some conclusions about the pleasantness of the categories in the training set by examining the responses to various prompts. However, it is impossible to filter out categories (colors) from the response (mixed color).
The signs were/are all over, or, as I said at the beginning, "I told you so".
Remember a famous Time article about OpenAI hiring Kenyans to make ChatGPT 3 'less toxic' from 2 or 3 years ago? If you create and create and create mixed colors over and over and over again, and all seem to have a Red component, what can you conclude?
If you have enough 'problems' with what your AI generates, big enough to devise a way to make it less toxic, is your training set valuable? I'll give you a sec to think about this one from a social and corporate perspective.
Perhaps you are thinking what I'm thinking, we can fix it, we just did it in our colors mental exercise, just remove the base colors we don't want. Yes, we can do that, but there are 2 things to consider. 1) Read this post backwards. If we consider colors and intensities, it is difficult to correctly classify them: is this Red, or Rose, or Light Red?
2) If you remove that which has a higher probability of being liked, what does it mean to your average user, to your business?
As biases in AI models came to light for the general public, experts' debates often focused on the implications of using these tools for society. How it reinforced stereotypes, how it was detrimental for minorities, e.g., AI-approved bank loans. However, let us not forget that, with our "Internet data trained" AI models, we are being forced to look at ourselves in a mirror that, many times, enhances a reflection we have conveniently decided to bury; a reflection that scares us.
The Internet didn't build itself; we built it.
If we want better AI models, we need better training data.
To get better massive training data, we need a better Internet content.
To get a better Internet content, we need a healthier society.
It is January 3rd 2026, so far I had 2 topics in the queue that I
wanted to write about, but today it made 3 topics. What an eventful
start of the year!
ACM Digital Library Goes "Open Access"
When I was a bachelor's student, one of my professors said, "If you publish in the ACM, you are somebody in computing." That statement stuck with me. I became a member when I was a bachelor's student. The main reason to be a member was not the conference benefits but the ability to read any paper published in the Digital Library (DL). If you are a member, you can download any paper. They also had agreements with book publishers, giving you more access to computing literature. That was a lot of resources for about 50 USD.
A few years back the "publishers crisis" hit and hit hard. In a nutshell, a publisher's job is to publish papers while ensuring access to every published paper. In the old days (pre-computing dominance), this was mostly manual; in particular, a physical library must have existed somewhere to ensure access. As a publisher, you must also ensure your publications meet a certain level of "quality". To meet this goal, publishers hired editors who decide what gets published and how a prospective paper should be modified to meet the quality desired. Editors rely on reviewers to perform this task, so the basic workflow is writers -> editor -> reviewers -> editor -> writers and, so on.
The same overall structure is adopted for conferences. If a conference is backed by a publisher, e.g., Springer, Elsevier, IEEE, ACM, etc., authors have a certain "quality" assurance, but mostly a certain liveness assurance; their paper would not be lost in 10 or 15 years. In the era of computing dominance, authors and readers get an extra assurance: instant availability. Before computers and the Internet, if you were an academic and wanted to read a paper published at a conference held abroad, you had to file a petition to your university's library, which in turn filed a petition (by physical mail) to the library of the hosting university. The hosting university staff would search their records, make a copy of the requested paper, and send it back (again by physical mail) to the requesting library, which would finally deliver it to the academic; this process, more or less, was followed for each and every paper you required a copy of. Nowadays, you go to the publisher's Webpage, search for the paper, and click on 'PDF' and seconds (at most) later, you hold a copy of the published paper.
Publishers then act as middlemen: readers rely on them to access papers, and writers rely on them to publish papers. And so, it is not difficult to see the power-position publishers have, and they make the most of it: access fees. This model contradicts the popular belief that science should be "free" (as in freedom); mostly performed by scientists working at (public) universities, the results should be for the benefit of all (mankind). Access fees, also known as 'paywalls', intuitively contradict this essence. However, the mere existence of scientific publishers is evidence of how the main actors of scientific creation neglected the proper keeping of their legacy. By "main actors", I encompass "all": from university management down to writers. In theory, it is evident that we do not require publishers, if all (and by all I mean ALL, not just the ones we like) universities of the world shared a common knowledge library. If by any chance this seems like a strange idea, think about blockchain and cryptocurrencies :) A library owned by all, and cared for by all.
Going back to the main topic. I became aware of a petition at iptetitions.com. If you can not access it, you can read the petition here. Written by Terence Patrick Kelly, a well-known computer scientist. The ACM steering committee decided to make metrics, 'advanced search', and comments available only to members; previously they were available to all. You can read about this on the current ACM Premium description here. In brief, that means if you are a "Basic Edition" user and you visit the page of a paper, you can no longer write a comment, and (perhaps) more importantly, you can not see how many times the paper has been downloaded and cited.
The petition is a call to take that back.
It has been some time since I downloaded papers by searching the DL directly. Like most people (or so I thought), I use Google Scholar and download the papers from there. I mostly use a publisher's website to download the paper's BibTeX entry. I learned the hard way that Google Scholar is not the best source for this kind of information. However, I very little 'action' in the comments section when I visit the DL page, so, I wouldn't mind having it entirely removed.
I regard the citation download metrics more like 'gossip' than something truly meaningful. But I have noticed them, and I admit they have sometimes biased my logic: "Oh, this must be good if it was downloaded 1K times last week". It is kind of when you see a post on your feed (now called timeline) with a lot of likes. Likes would be times downloaded, citations, perhaps comments? I could also live without them.
AI-generated article summaries are another Premium feature. I could definitely live without them. I mean, is the ACM's ruling body aware that papers have a section called Abstract/Résumé?
Bulk citation exports and PDF downloads. That could be a downside for me. I have folders with relevant papers in PDF that I can consult at any time, so if I cannot download PDFs, that could be a problem for me.
Advanced search is also a miss for me. I mostly search for papers published within a certain date range; without it, I'm shown all papers published, and well, those are a lot. I think this is detrimental to the spirit of fast availability; you are unable to search for papers published in a given venue or within a given time frame. The ACM is truly hurting itself because it cannot provide accurate access to its Open Access papers; any computer scientist would laugh at how difficult it is to access the information within its main society.
On the last two points alone, I should be signing the petition, but I did not. The reason is: I agree with the goals, but I do not agree with the reasons given in the petition. It reads like an influencer-written complaint: if people can not see my likes and posts, they can't know I'm an influencer.
"Author recognition is the social contract that binds writers into a sustainable community: Authors fill the ACM Digital Library by donating articles, and the DL rewards authors by enabling readers and prospective employers to find and appraise authors' contributions. The new paywall voids this social contract, shortchanging authors and removing much of the incentive that formerly motivated them."
I'm probably very naive, but I don't write to get some metrics that anyone can see. I do understand, on the other hand, that you may want to evaluate a prospective employee by checking those metrics. However, in that case, I expect the employer to have greater access to the world's leading computing association than the Basic one. Hence, you would not have any problem at all.
This statement also raises an interesting question: How many authors are affiliated with institutions that DO NOT have an institutional ACM DL access? I would expect the number to be small.
"Volunteer labor is the foundation of ACM's existence. The new paywall attempts to monetize volunteers' contributions, which were intended for the benefit of the public."
Just recently, the ACM moved to Open Access only after intensive pressure to do so. Unlike other Editors, the ACM is a "society"; hence, at its core, it should not be a business. Therefore, going Open Access was the right move; should statistics be "open" as well?
The short answer is yes, just because if you collect data from "free" raw data, the results should also be "free". Not because you lose visibility to the "outside world".
The move is clearly detrimental if you judge by numbers.
After reading this petition, I must say that hiding stats would be a good idea; it may force readers to actually read the paper and decide for themselves, instead of "going with the flow". If you are an employer and read the paper, you get to decide whether it is a good paper, rather than relying on "it has X citations, so it must be good". Especially after we have seen how these metrics have been abused.
Kongens nytårstale
While you live in a new place, you get to pick some of the traditions of the place you are living in.
In my case, it was the Queen (now King) of Denmark's New Year's Eve speech. I learned that it is tradition to guess the topics the King would address in his speech. This year, an obvious choice would be Greenland (Grønland in Danish).
The King visited Greenland during the summer, but there is also the constant threat from the USA to get it "One way or the other". So it was obvious that a clear message should be sent. But it seems the message was not exactly what was expected: a brief mention of Greenland and the danger lurking... unaddressed.
At first, I thought that my limited Danish prevented me from capturing more, but then I saw a couple of news reports at DR highlighting this.
The speech of the Prime Minister Mette Frederiksen did not seem to add anything new, but it was not as tepid as the one from the King (to my view of course). The policy of silence is worrisome. Specially when your "closest ally" (nærmeste allierede in danish) is the one that just put a stunt (see next).
Make America Great Again
I have always wondered what Donald Trump actually means by "Great Again". To what greatness is he referring? I'm not a Trump expert, but to the best of my knowledge, he hasn't actually defined a time frame for the greatness he would like to transition the USA back to. Perhaps he grew up with his grandfather's stories about Andrew Carnegie and John D. Rockefeller, and that is the time frame he would like his country to go back to. Or perhaps he would like to go back to the early 50's or 60's, I don't know.
It actually doesn't really matter what he means; it is the end, and "the end justifies the means", and the actual focus should be on "the means" as they are today's end.
It is not a secret that the República Bolivariana de Venezuela, commonly known as Venezuela, is an oil-rich country. And oil is craved by Trump's USA.
Trump's logic is plain: we need oil, Venezuela has it, we try to seize power by proxy (i.e. we back-up a Venezuelan citizen to seize power), we fail, we declare a Venezuelan cartel a terrorist organization, we blow-up some boats near their shores, we attack a sovereign country and kidnap its president, and declare ourselves "managers of the country". Does this sound remotely familiar to you?
Not so long ago, almost four years ago, a country attacked another, and a bunch of countries cried to the heavens. It was unacceptable, unimaginable that a country wanted to annex another, the drums of war could be heard, money donations, bullets donations were made, and are still being made.
In this case? Keir Starmer would like to "clarify the facts"; Ursula von der Leyen: "We stand by the people of Venezuela and support a peaceful and democratic transition. Any solution must respect international law and the UN Charter". As we say in Mexico, "lo caido caido" or tough luck, better luck next time. It is laughable, but following the playbook to the letter.
What happened and is planned has a very common name: theft. At his press conference, Trump didn't specify how long the occupation will last, but a good guess can already be made: as long as it takes to bleed out the country of its oil reserves, not a minute longer, not a minute shorter. It is not like Trump is in the business of trying to annex countries or part of them to the 50 stars flag, oh, with the obvious exception, of course.
I kind of expected more from Trump. I would have respected him more if he had come out a year earlier and said something like "I want Venezuela's oil, and we are going to get it one way or the other. It is vital for our interests." And then come a year later and say: "See? I told you we were going to get it." That straight, honest, and confrontational attitude I could respect, and I would entertain the idea that he might be able to "Make America Great Again". Instead, he beats around the bush, hiding behind the narco-whatever rhetoric. I think I created that expectation after seeing him behave with Ukraine's president, stating plainly that they would squeeze the best deal possible out of him. Or seeing him threaten an ally, who buys his combat planes, with getting a piece of their territory. I think it is easy to threaten those who are already under your thumb.
Don't get me wrong, I'm not downplaying the determination and courage shown by the USA president. It takes both for a 79-year-old man, apparently not at the peak of his health, to sit down for 3+ hours and follow through a TV show, which is how he described how he felt watching the feed.
My grandmother had trouble following an action movie plot when she indulged me and watched it with me, and action movies are bite-sized. I imagine real-time military feeds are much more complicated, and to be able to follow them, regulating your emotions and your bodily responses must require paramount determination and courage.
Voices within the USA spoke against the attack, however, I wonder how long it will take to silence the internal opposition. Perhaps when the projected financial gains are shared, the internal disagreement will reconsider its position. Who knows, perhaps it is a big enough gain to put the Epstein's files to rest (for a while at least).
I'm not an expert on Venezuela, nor on Trump. I do not know how much the recent 20 years of occupation taught the USA about bleeding out natural resources.
I do think that, among the top administration that approved the strike, the logic was as simple as in a Vin Diesel movie: wander around cartel territory to arrest the kingpin, and everyone will be cool about it, embracing the deliverance. Reality, however, is a little bit more complicated than a movie.
It remains to be seen how much more complicated it will be in this case.
The day the Ukraine war began, my wife and I were talking about it. I conjectured that Ukraine would end like Germany after WWII. I'm not a consummated politician nor a political science professor, but I wasn't born yesterday either. If I recall correctly, the colorful character Henry Kissinger stated something like the USA (or EU) should negotiate with Russia. Of course, he got crucified. It turns out that is exactly what is happening. Now, I will use my "I wasn't born yesterday" powers to conjecture that Trump really, really wants the Venezuelan succession government to work, because it is in his best interest to minimize the troops' presence in Venezuela. Not because he is afraid, but because he has 3 years left as president, he can not afford a mishap that would cost time. He has his sights set on other countries like Colombia, Nicaragua, Brazil, Cuba, and, of course, Mexico; as well as his strong interest in adding a new star to the USA flag. Therefore, he requires his army to be intact. He is betting/hoping that no country would put up a fight. He wants his legacy to be the one that brought stability to the USA's influence sphere and, surprise, surprise, "Make America Great Again". However, we might be witnessing the last attempt of a dying empire.
ACM Digital Library Goes "Open Access"
When I was a bachelor's student, one of my professors said, "If you publish in the ACM, you are somebody in computing." That statement stuck with me. I became a member when I was a bachelor's student. The main reason to be a member was not the conference benefits but the ability to read any paper published in the Digital Library (DL). If you are a member, you can download any paper. They also had agreements with book publishers, giving you more access to computing literature. That was a lot of resources for about 50 USD.
A few years back the "publishers crisis" hit and hit hard. In a nutshell, a publisher's job is to publish papers while ensuring access to every published paper. In the old days (pre-computing dominance), this was mostly manual; in particular, a physical library must have existed somewhere to ensure access. As a publisher, you must also ensure your publications meet a certain level of "quality". To meet this goal, publishers hired editors who decide what gets published and how a prospective paper should be modified to meet the quality desired. Editors rely on reviewers to perform this task, so the basic workflow is writers -> editor -> reviewers -> editor -> writers and, so on.
The same overall structure is adopted for conferences. If a conference is backed by a publisher, e.g., Springer, Elsevier, IEEE, ACM, etc., authors have a certain "quality" assurance, but mostly a certain liveness assurance; their paper would not be lost in 10 or 15 years. In the era of computing dominance, authors and readers get an extra assurance: instant availability. Before computers and the Internet, if you were an academic and wanted to read a paper published at a conference held abroad, you had to file a petition to your university's library, which in turn filed a petition (by physical mail) to the library of the hosting university. The hosting university staff would search their records, make a copy of the requested paper, and send it back (again by physical mail) to the requesting library, which would finally deliver it to the academic; this process, more or less, was followed for each and every paper you required a copy of. Nowadays, you go to the publisher's Webpage, search for the paper, and click on 'PDF' and seconds (at most) later, you hold a copy of the published paper.
Publishers then act as middlemen: readers rely on them to access papers, and writers rely on them to publish papers. And so, it is not difficult to see the power-position publishers have, and they make the most of it: access fees. This model contradicts the popular belief that science should be "free" (as in freedom); mostly performed by scientists working at (public) universities, the results should be for the benefit of all (mankind). Access fees, also known as 'paywalls', intuitively contradict this essence. However, the mere existence of scientific publishers is evidence of how the main actors of scientific creation neglected the proper keeping of their legacy. By "main actors", I encompass "all": from university management down to writers. In theory, it is evident that we do not require publishers, if all (and by all I mean ALL, not just the ones we like) universities of the world shared a common knowledge library. If by any chance this seems like a strange idea, think about blockchain and cryptocurrencies :) A library owned by all, and cared for by all.
Going back to the main topic. I became aware of a petition at iptetitions.com. If you can not access it, you can read the petition here. Written by Terence Patrick Kelly, a well-known computer scientist. The ACM steering committee decided to make metrics, 'advanced search', and comments available only to members; previously they were available to all. You can read about this on the current ACM Premium description here. In brief, that means if you are a "Basic Edition" user and you visit the page of a paper, you can no longer write a comment, and (perhaps) more importantly, you can not see how many times the paper has been downloaded and cited.
The petition is a call to take that back.
It has been some time since I downloaded papers by searching the DL directly. Like most people (or so I thought), I use Google Scholar and download the papers from there. I mostly use a publisher's website to download the paper's BibTeX entry. I learned the hard way that Google Scholar is not the best source for this kind of information. However, I very little 'action' in the comments section when I visit the DL page, so, I wouldn't mind having it entirely removed.
I regard the citation download metrics more like 'gossip' than something truly meaningful. But I have noticed them, and I admit they have sometimes biased my logic: "Oh, this must be good if it was downloaded 1K times last week". It is kind of when you see a post on your feed (now called timeline) with a lot of likes. Likes would be times downloaded, citations, perhaps comments? I could also live without them.
AI-generated article summaries are another Premium feature. I could definitely live without them. I mean, is the ACM's ruling body aware that papers have a section called Abstract/Résumé?
Bulk citation exports and PDF downloads. That could be a downside for me. I have folders with relevant papers in PDF that I can consult at any time, so if I cannot download PDFs, that could be a problem for me.
Advanced search is also a miss for me. I mostly search for papers published within a certain date range; without it, I'm shown all papers published, and well, those are a lot. I think this is detrimental to the spirit of fast availability; you are unable to search for papers published in a given venue or within a given time frame. The ACM is truly hurting itself because it cannot provide accurate access to its Open Access papers; any computer scientist would laugh at how difficult it is to access the information within its main society.
On the last two points alone, I should be signing the petition, but I did not. The reason is: I agree with the goals, but I do not agree with the reasons given in the petition. It reads like an influencer-written complaint: if people can not see my likes and posts, they can't know I'm an influencer.
"Author recognition is the social contract that binds writers into a sustainable community: Authors fill the ACM Digital Library by donating articles, and the DL rewards authors by enabling readers and prospective employers to find and appraise authors' contributions. The new paywall voids this social contract, shortchanging authors and removing much of the incentive that formerly motivated them."
I'm probably very naive, but I don't write to get some metrics that anyone can see. I do understand, on the other hand, that you may want to evaluate a prospective employee by checking those metrics. However, in that case, I expect the employer to have greater access to the world's leading computing association than the Basic one. Hence, you would not have any problem at all.
This statement also raises an interesting question: How many authors are affiliated with institutions that DO NOT have an institutional ACM DL access? I would expect the number to be small.
"Volunteer labor is the foundation of ACM's existence. The new paywall attempts to monetize volunteers' contributions, which were intended for the benefit of the public."
Just recently, the ACM moved to Open Access only after intensive pressure to do so. Unlike other Editors, the ACM is a "society"; hence, at its core, it should not be a business. Therefore, going Open Access was the right move; should statistics be "open" as well?
The short answer is yes, just because if you collect data from "free" raw data, the results should also be "free". Not because you lose visibility to the "outside world".
The move is clearly detrimental if you judge by numbers.
After reading this petition, I must say that hiding stats would be a good idea; it may force readers to actually read the paper and decide for themselves, instead of "going with the flow". If you are an employer and read the paper, you get to decide whether it is a good paper, rather than relying on "it has X citations, so it must be good". Especially after we have seen how these metrics have been abused.
Kongens nytårstale
While you live in a new place, you get to pick some of the traditions of the place you are living in.
In my case, it was the Queen (now King) of Denmark's New Year's Eve speech. I learned that it is tradition to guess the topics the King would address in his speech. This year, an obvious choice would be Greenland (Grønland in Danish).
The King visited Greenland during the summer, but there is also the constant threat from the USA to get it "One way or the other". So it was obvious that a clear message should be sent. But it seems the message was not exactly what was expected: a brief mention of Greenland and the danger lurking... unaddressed.
At first, I thought that my limited Danish prevented me from capturing more, but then I saw a couple of news reports at DR highlighting this.
The speech of the Prime Minister Mette Frederiksen did not seem to add anything new, but it was not as tepid as the one from the King (to my view of course). The policy of silence is worrisome. Specially when your "closest ally" (nærmeste allierede in danish) is the one that just put a stunt (see next).
Make America Great Again
I have always wondered what Donald Trump actually means by "Great Again". To what greatness is he referring? I'm not a Trump expert, but to the best of my knowledge, he hasn't actually defined a time frame for the greatness he would like to transition the USA back to. Perhaps he grew up with his grandfather's stories about Andrew Carnegie and John D. Rockefeller, and that is the time frame he would like his country to go back to. Or perhaps he would like to go back to the early 50's or 60's, I don't know.
It actually doesn't really matter what he means; it is the end, and "the end justifies the means", and the actual focus should be on "the means" as they are today's end.
It is not a secret that the República Bolivariana de Venezuela, commonly known as Venezuela, is an oil-rich country. And oil is craved by Trump's USA.
Trump's logic is plain: we need oil, Venezuela has it, we try to seize power by proxy (i.e. we back-up a Venezuelan citizen to seize power), we fail, we declare a Venezuelan cartel a terrorist organization, we blow-up some boats near their shores, we attack a sovereign country and kidnap its president, and declare ourselves "managers of the country". Does this sound remotely familiar to you?
Not so long ago, almost four years ago, a country attacked another, and a bunch of countries cried to the heavens. It was unacceptable, unimaginable that a country wanted to annex another, the drums of war could be heard, money donations, bullets donations were made, and are still being made.
In this case? Keir Starmer would like to "clarify the facts"; Ursula von der Leyen: "We stand by the people of Venezuela and support a peaceful and democratic transition. Any solution must respect international law and the UN Charter". As we say in Mexico, "lo caido caido" or tough luck, better luck next time. It is laughable, but following the playbook to the letter.
What happened and is planned has a very common name: theft. At his press conference, Trump didn't specify how long the occupation will last, but a good guess can already be made: as long as it takes to bleed out the country of its oil reserves, not a minute longer, not a minute shorter. It is not like Trump is in the business of trying to annex countries or part of them to the 50 stars flag, oh, with the obvious exception, of course.
I kind of expected more from Trump. I would have respected him more if he had come out a year earlier and said something like "I want Venezuela's oil, and we are going to get it one way or the other. It is vital for our interests." And then come a year later and say: "See? I told you we were going to get it." That straight, honest, and confrontational attitude I could respect, and I would entertain the idea that he might be able to "Make America Great Again". Instead, he beats around the bush, hiding behind the narco-whatever rhetoric. I think I created that expectation after seeing him behave with Ukraine's president, stating plainly that they would squeeze the best deal possible out of him. Or seeing him threaten an ally, who buys his combat planes, with getting a piece of their territory. I think it is easy to threaten those who are already under your thumb.
Don't get me wrong, I'm not downplaying the determination and courage shown by the USA president. It takes both for a 79-year-old man, apparently not at the peak of his health, to sit down for 3+ hours and follow through a TV show, which is how he described how he felt watching the feed.
My grandmother had trouble following an action movie plot when she indulged me and watched it with me, and action movies are bite-sized. I imagine real-time military feeds are much more complicated, and to be able to follow them, regulating your emotions and your bodily responses must require paramount determination and courage.
Voices within the USA spoke against the attack, however, I wonder how long it will take to silence the internal opposition. Perhaps when the projected financial gains are shared, the internal disagreement will reconsider its position. Who knows, perhaps it is a big enough gain to put the Epstein's files to rest (for a while at least).
I'm not an expert on Venezuela, nor on Trump. I do not know how much the recent 20 years of occupation taught the USA about bleeding out natural resources.
I do think that, among the top administration that approved the strike, the logic was as simple as in a Vin Diesel movie: wander around cartel territory to arrest the kingpin, and everyone will be cool about it, embracing the deliverance. Reality, however, is a little bit more complicated than a movie.
It remains to be seen how much more complicated it will be in this case.
The day the Ukraine war began, my wife and I were talking about it. I conjectured that Ukraine would end like Germany after WWII. I'm not a consummated politician nor a political science professor, but I wasn't born yesterday either. If I recall correctly, the colorful character Henry Kissinger stated something like the USA (or EU) should negotiate with Russia. Of course, he got crucified. It turns out that is exactly what is happening. Now, I will use my "I wasn't born yesterday" powers to conjecture that Trump really, really wants the Venezuelan succession government to work, because it is in his best interest to minimize the troops' presence in Venezuela. Not because he is afraid, but because he has 3 years left as president, he can not afford a mishap that would cost time. He has his sights set on other countries like Colombia, Nicaragua, Brazil, Cuba, and, of course, Mexico; as well as his strong interest in adding a new star to the USA flag. Therefore, he requires his army to be intact. He is betting/hoping that no country would put up a fight. He wants his legacy to be the one that brought stability to the USA's influence sphere and, surprise, surprise, "Make America Great Again". However, we might be witnessing the last attempt of a dying empire.
A couple of days ago (give or take) my social media feed showed a post
that goes like this: Here is a real task from my job. I have a 100GB binary
file. Produced daily. I can't grep it. But I can decode it.
However, I can't store the decoded version either. It's too big.
How do I efficiently query it?
Decoding piped to grep takes 2 minutes. I want 2 seconds.
When I saw this post, my initial reaction was to question the approach. Unless the binary file is highly encoded, for example, encrypted, it seems inefficient to decode it to plain text and then search with grep; especially within a company that reported a revenue in the billions last year. While this method may be understandable for those with limited resources, one would expect a much more optimized solution in a large enterprise setting.
The engagement generated by the post led the author to disclose more details, for example, that this file was generated by a microservice. And that it had a record-type structure with various record types. (1) We all know what microservices use to talk to each other, right? (2) 100GB files seem to defeat the micro purpose, but that is relative. Also, someone suggested to grep the binary file, but the author seemed to not be aware of such a tool, and the reply was something along the lines of wanting to avoid translating the grep queries into binary grep ones. This seems reasonable if the queries are complex, which is kind of expected.
My suspicions were confirmed when the author disclosed that the decoded file is a JSON file worth at least 1 TB. Then I thought "Who greps such a big file when there are a lot of efficient tools that parse JSON?, again, you must be kidding, right? I mean, you are aware that there are a bunch of smart folks who create software specialized in storing and querying data, right?" But again, probably you don't want to add an extra layer of complexity, so you prefer to stick to big file transactions, or perhaps (and more likely) a high number of small transactions.
The cherry of the cake was when the author posted a follow-up post: You just gotta love how many people jump with solutions without clarifying requirement. This is a common mistake I see while interviewing people at *****. People propose solutions without trying to clarify constraints, or input data patterns, or edge cases.
So, this might as well have been an interview question and an interview behavior. I think it is troublesome. If you are given a problem to solve as part of an interview for a job about solving problems, the first natural reaction is to attempt to solve the problem with the information you are given. Moreover, if the person doing the interview is your prospective immediate supervisor, then it is reasonable to assume you are given all the information necessary or at least available at the time to solve the task. I think most people try to be polite and won't tell the interviewer, "Who the f..k does that? Are you incompetent or just plain lazy?" even if they think about it.
If you are given a half-baked problem by the person who is going to supervise your work, then you, as a prospective employee, can easily make the conclusion "this guy is plain incompetent, he wants me to boost his production by more than 10x without having a complete idea of the problem he wants to solve." If you want to assess the candidate's ability to correctly frame a problem, there are many more meaningful ways to do so.
Interviews go both ways, just as the interviewer concludes the candidate makes a mistake by not asking more questions about the problem, the candidate (even if they ask questions) can easily conclude that the interviewer is lazy, incompetent, or withholds information on purpose to other colleagues.
When I saw this post, my initial reaction was to question the approach. Unless the binary file is highly encoded, for example, encrypted, it seems inefficient to decode it to plain text and then search with grep; especially within a company that reported a revenue in the billions last year. While this method may be understandable for those with limited resources, one would expect a much more optimized solution in a large enterprise setting.
The engagement generated by the post led the author to disclose more details, for example, that this file was generated by a microservice. And that it had a record-type structure with various record types. (1) We all know what microservices use to talk to each other, right? (2) 100GB files seem to defeat the micro purpose, but that is relative. Also, someone suggested to grep the binary file, but the author seemed to not be aware of such a tool, and the reply was something along the lines of wanting to avoid translating the grep queries into binary grep ones. This seems reasonable if the queries are complex, which is kind of expected.
My suspicions were confirmed when the author disclosed that the decoded file is a JSON file worth at least 1 TB. Then I thought "Who greps such a big file when there are a lot of efficient tools that parse JSON?, again, you must be kidding, right? I mean, you are aware that there are a bunch of smart folks who create software specialized in storing and querying data, right?" But again, probably you don't want to add an extra layer of complexity, so you prefer to stick to big file transactions, or perhaps (and more likely) a high number of small transactions.
The cherry of the cake was when the author posted a follow-up post: You just gotta love how many people jump with solutions without clarifying requirement. This is a common mistake I see while interviewing people at *****. People propose solutions without trying to clarify constraints, or input data patterns, or edge cases.
So, this might as well have been an interview question and an interview behavior. I think it is troublesome. If you are given a problem to solve as part of an interview for a job about solving problems, the first natural reaction is to attempt to solve the problem with the information you are given. Moreover, if the person doing the interview is your prospective immediate supervisor, then it is reasonable to assume you are given all the information necessary or at least available at the time to solve the task. I think most people try to be polite and won't tell the interviewer, "Who the f..k does that? Are you incompetent or just plain lazy?" even if they think about it.
If you are given a half-baked problem by the person who is going to supervise your work, then you, as a prospective employee, can easily make the conclusion "this guy is plain incompetent, he wants me to boost his production by more than 10x without having a complete idea of the problem he wants to solve." If you want to assess the candidate's ability to correctly frame a problem, there are many more meaningful ways to do so.
Interviews go both ways, just as the interviewer concludes the candidate makes a mistake by not asking more questions about the problem, the candidate (even if they ask questions) can easily conclude that the interviewer is lazy, incompetent, or withholds information on purpose to other colleagues.
UPDATE:
After writing this I reached out to Marcus Pilgaard to thank him for posting the scores at musescore and showed him the result. He didn't just spot the mistakes and pointed them out, he was so kind enough to offer the solution! That's super cool.
Marcus handed me a revised score for the melody adjusted to the kind of sounds I was generating and he also provided me with a couple of MIDI files for a keyboard. I was able to revise the whole melody. It was nice to hear what I generated along with a trusted baseline.
So here are the results:
Søren Banjomus Revised
I mixed Marcus MIDI melody and what I generated and here is the result
I also tweaked the sound shaper to produce quieter sounds. The revised version is here
Marcus: Tusind tak, du er den bedste!
Original:
A while ago I wrote a white noise generator to avoid going to an app. I wanted to be able to hear it and tune some elements on the fly. Since the generator is written in Python and use Simpleaudio to construct the wav file, I consulted the documentation to figure out how to play raw data.
There I found an example using numpy. The numpy example had some appealing syntax so I decided to create a small program to generate some notes. My "wife" had the idea to play the Imperial March from Star Wars on it and from there I decided to make it play one of my favorites Christmas songs: Søren Banjomus.
Luckily I found a score written by Marcus Pilgaard HERE which was wonderful for my attempts :)
These are the .wav results: Imperial March, Søren Banjomus
And finally this is the code: Player the configuration settings (i.e. filename and playback) are the last two lines
I think it is not bad for a couple of days work considering I had to recall how to read scores, interpret them, and so on :D
After writing this I reached out to Marcus Pilgaard to thank him for posting the scores at musescore and showed him the result. He didn't just spot the mistakes and pointed them out, he was so kind enough to offer the solution! That's super cool.
Marcus handed me a revised score for the melody adjusted to the kind of sounds I was generating and he also provided me with a couple of MIDI files for a keyboard. I was able to revise the whole melody. It was nice to hear what I generated along with a trusted baseline.
So here are the results:
Søren Banjomus Revised
I mixed Marcus MIDI melody and what I generated and here is the result
I also tweaked the sound shaper to produce quieter sounds. The revised version is here
Marcus: Tusind tak, du er den bedste!
Original:
A while ago I wrote a white noise generator to avoid going to an app. I wanted to be able to hear it and tune some elements on the fly. Since the generator is written in Python and use Simpleaudio to construct the wav file, I consulted the documentation to figure out how to play raw data.
There I found an example using numpy. The numpy example had some appealing syntax so I decided to create a small program to generate some notes. My "wife" had the idea to play the Imperial March from Star Wars on it and from there I decided to make it play one of my favorites Christmas songs: Søren Banjomus.
Luckily I found a score written by Marcus Pilgaard HERE which was wonderful for my attempts :)
These are the .wav results: Imperial March, Søren Banjomus
And finally this is the code: Player the configuration settings (i.e. filename and playback) are the last two lines
I think it is not bad for a couple of days work considering I had to recall how to read scores, interpret them, and so on :D
In her CACM 2006 landmark paper "Computational Thinking" Jeannette
M. Wing coined the term Computational Thinking.
Wing concludes: Professors of computer science should
teach a course called "Ways to Think Like a Computer Scientist" to
college freshmen, making it available to non-majors, not just to
computer science majors.
If we recognize that all professions are equally important, then the
first years (not semester nor semesters) of college would be a
plethora of courses "Ways to Think Like a ...".
Which, actually, seem to be more suitable to be taught at
High-School level and should give a more informed landscape of
professional development possibilities.
But why is necessary to have the concept Computational Thinking? According to Wing, "Computational thinking is a grand vision to guide computer science educators, researchers and practitioners as we act to change society's image of the field". As this paper resonated within the Computing Education community, research projects and papers found a conductive thread to spread the "Computational Thinking" concept and how to promote it in pre-college learners, although it remains challenging to define exactly what you should observe to conclude that students have reached "Computational Thinking".
Long before Wing's paper, Leone Minna Gold Burton in 1984 published "Mathematical Thinking: The Struggle for Meaning" in JRME (Journal for Research in Mathematics Education) in which she argues that "mathematical thinking is not thinking about the subject matter of mathematics but a style of thinking that is a function of particular operations, processes, and dynamics recognizably mathematical". In essence this is similar to Wing's definition: "Computational thinking involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science". While Wing acknowledges Computational Thinking is related to Mathematical Thinking, she states this relation is due to "like all sciences, its formal foundations rest on mathematics". Indeed Computer Science is a branch of Mathematics, we study problems solvable by algorithms, that is, well-defined (hopefully unambiguous) processes. There should be little doubt that computational problems are more restricted than Mathematical problems, and hence, Computational Thinking should be a restricted, specialized version of Mathematical Thinking.
Wing claims Computational Thinking is a "Fundamental skill", "something every human being must know to function in modern society". As far as I know, Wing does not justify this requirement, other authors state this is because computers are "everywhere". Wing does mention: "Ubiquitous computing was yesterday's dream that became today's reality" but she doesn't relate this reality with the "Fundamental skill" characteristic of Computational Thinking.
Arguably most people own a watch: wristwatch, wall-clock, alarm clock, etc. It is almost impossible to imagine going on your daily modern routine without a tool that marks the time down to the second. Clocks are a marvel of human ingenuity and nowadays 'smart-watches' even check your oxygen saturation. And yet, how to think like a clock designer/builder/tinker is not a course nor a "Fundamental skill". Shall we teach "Car engineering Thinking" just because in your country cars are everywhere? Shall we teach "Railroad design" to European students because you can reach many small towns by train? Or shall we teach "How to think like a librarian" just because a vast majority of persons in the "modern society" own a book? The difference is clear: a computer does much more than mark the time, it doesn't take you from A to B but it can show you how to get from A to B the quickest, it is not a book, but it can contain millions of books in just a tiny fraction of the space.
Is this enough to justify Computational Thinking?
It is futile to cement Computational Thinking on the "Computer" object because problems solvable by well-defined processes transcend computers and had been, in fact, a constant presence throughout the history of mankind: you can't cook an animal you haven't hunted yet. Computers, at the present moment, are instrumental just because they are our current predictable model in which we can test our theories.
But why is necessary to have the concept Computational Thinking? According to Wing, "Computational thinking is a grand vision to guide computer science educators, researchers and practitioners as we act to change society's image of the field". As this paper resonated within the Computing Education community, research projects and papers found a conductive thread to spread the "Computational Thinking" concept and how to promote it in pre-college learners, although it remains challenging to define exactly what you should observe to conclude that students have reached "Computational Thinking".
Long before Wing's paper, Leone Minna Gold Burton in 1984 published "Mathematical Thinking: The Struggle for Meaning" in JRME (Journal for Research in Mathematics Education) in which she argues that "mathematical thinking is not thinking about the subject matter of mathematics but a style of thinking that is a function of particular operations, processes, and dynamics recognizably mathematical". In essence this is similar to Wing's definition: "Computational thinking involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science". While Wing acknowledges Computational Thinking is related to Mathematical Thinking, she states this relation is due to "like all sciences, its formal foundations rest on mathematics". Indeed Computer Science is a branch of Mathematics, we study problems solvable by algorithms, that is, well-defined (hopefully unambiguous) processes. There should be little doubt that computational problems are more restricted than Mathematical problems, and hence, Computational Thinking should be a restricted, specialized version of Mathematical Thinking.
Wing claims Computational Thinking is a "Fundamental skill", "something every human being must know to function in modern society". As far as I know, Wing does not justify this requirement, other authors state this is because computers are "everywhere". Wing does mention: "Ubiquitous computing was yesterday's dream that became today's reality" but she doesn't relate this reality with the "Fundamental skill" characteristic of Computational Thinking.
Arguably most people own a watch: wristwatch, wall-clock, alarm clock, etc. It is almost impossible to imagine going on your daily modern routine without a tool that marks the time down to the second. Clocks are a marvel of human ingenuity and nowadays 'smart-watches' even check your oxygen saturation. And yet, how to think like a clock designer/builder/tinker is not a course nor a "Fundamental skill". Shall we teach "Car engineering Thinking" just because in your country cars are everywhere? Shall we teach "Railroad design" to European students because you can reach many small towns by train? Or shall we teach "How to think like a librarian" just because a vast majority of persons in the "modern society" own a book? The difference is clear: a computer does much more than mark the time, it doesn't take you from A to B but it can show you how to get from A to B the quickest, it is not a book, but it can contain millions of books in just a tiny fraction of the space.
Is this enough to justify Computational Thinking?
It is futile to cement Computational Thinking on the "Computer" object because problems solvable by well-defined processes transcend computers and had been, in fact, a constant presence throughout the history of mankind: you can't cook an animal you haven't hunted yet. Computers, at the present moment, are instrumental just because they are our current predictable model in which we can test our theories.
I have heard and read the statement "Programming isn't Math" too
many times, but this one is new, it doesn't just tell you what
programming "isn't" but what "is".
Today a tweet with a video claiming this popped up and I
watched it.
The video dwells on different kinds of "ambiguities" that can happen on C++ programs with examples of "English counterparts". For some reason omitted the fact that Mathematics has ambiguities too, called paradoxes, one of the most famous is "Russell's paradox".
Like all the times I have seen "Programming isn't Math", the video failed to explain this point at any serious level, it follows the "conter-example fallacy"; showing counter examples that curiously fall in the claimed category and conclude the subject at hand must belong to the claimed category. I feel the expression "Programming isn't Math" is meant to point out that having a formal mathematical education is not a pre-requisite to write programs. It is obvious that you can create programs without any formal Mathematical background just as you can build a brick house without any civil engineering knowledge. Imagine you can get a building manual with recipes to build any possible two-story house that explains how you should arrange all the elements. Then, clearly you don't need any civil engineering knowledge because every possible case is solved in the manual, how that manual came to be is beyond this discussion, the point is the manual exists. The existence of the manual can be considered as the culmination of civil, structural and material engineering research into two-story houses. The interesting, open question is: if we accumulate all the programs ever written, can we write any possible program by taking only parts of the existent programs?
I usually do not check "the credentials" of authors because I feel those bias us, but since some of the people I follow, follow that account, then I was curious. I was "surprised" to find the profile indicates the author to be a researcher at the company that created the most successful search engine.
I can not help to be left with this taste of marketing paving: if to create programs you don't need "math" just to be able to have an idea of how to "write proper English" then you are all set now the last step is to use this super intuitive AI tool to help you...
The video dwells on different kinds of "ambiguities" that can happen on C++ programs with examples of "English counterparts". For some reason omitted the fact that Mathematics has ambiguities too, called paradoxes, one of the most famous is "Russell's paradox".
Like all the times I have seen "Programming isn't Math", the video failed to explain this point at any serious level, it follows the "conter-example fallacy"; showing counter examples that curiously fall in the claimed category and conclude the subject at hand must belong to the claimed category. I feel the expression "Programming isn't Math" is meant to point out that having a formal mathematical education is not a pre-requisite to write programs. It is obvious that you can create programs without any formal Mathematical background just as you can build a brick house without any civil engineering knowledge. Imagine you can get a building manual with recipes to build any possible two-story house that explains how you should arrange all the elements. Then, clearly you don't need any civil engineering knowledge because every possible case is solved in the manual, how that manual came to be is beyond this discussion, the point is the manual exists. The existence of the manual can be considered as the culmination of civil, structural and material engineering research into two-story houses. The interesting, open question is: if we accumulate all the programs ever written, can we write any possible program by taking only parts of the existent programs?
I usually do not check "the credentials" of authors because I feel those bias us, but since some of the people I follow, follow that account, then I was curious. I was "surprised" to find the profile indicates the author to be a researcher at the company that created the most successful search engine.
I can not help to be left with this taste of marketing paving: if to create programs you don't need "math" just to be able to have an idea of how to "write proper English" then you are all set now the last step is to use this super intuitive AI tool to help you...
The first time I heard about Roskilde University (Roskilde Universitet) was when I was a bacherlor student: the PhD advisor of one of my bachelor advisors worked there on program analysis of logic programs, the topic I was working on at that time. Little I knew what a special place Denmark and Roskilde would have in my wife's heart and mine. How our breath would be taken away standing in the shadow of Roskilde Domkirke, our weekly trips and special trips to the Vikingeskibsmuseet to see the ships pulled out of the water for the winter while discussing how nice it would be to live in Roskilde and be part of the maintenance crew. Little I knew how we would wait for summer to buy Carolina Reaper chilly peppers at the weekend market and how patient the sweet couple would be with my less-than-acceptable danish or how nice was the 'Honey man' from whom we would buy our Roskilde Ring honey, how much we would cherish our wood crafts we bought at the local market or seeing all the cherished belongings people would sell on weekends or our 'tours' to find and photograph all Kilder. I wouldn't even imagine telling to my office mate at CMU over lunch (during a research stay) about how much I would have loved to have an academic position at RUC while staying at one of the most prestigious computer science universities. I can not help, but feel disappointed and somewhat heartbroken about the proposal to close RUC.
What I write here is not based on exhaustive research, as I said, my Duolingo fueled danish is not what it should be, so there is a lot that I do not understand; it is based on perspective and feeling. It seems some doubtful decisions were made regarding the acceptance of 300 students from Bangladesh in 2024 into a masters program in business that had financial consequences. The consequence for RUC is that it seems it 'profited' millions of danish krone. Perhaps if it were just that, then it would be kind of ok. However, politicians seem to suspect the students and accompanying spouses true purpose is to seek jobs; a discourse often heard worldwide nowadays. Suspicion alone is not a fact; suspicion draws attention and requires investigation, research and debate. If wrong doing with intent is found, then corrective actions must be taken, if necessary fire the university's entire ruling body, but closing a regional university has no sense at all!; specially when a higher-education decentralization strategy is in the works. It seems to me the goal of RUC shutdown is to send a message not an actual step in the right direction to fix the root causes. A message that will have far more negative consequences than 'letting' other universities known whatever there is for them to know.
I feel for the RCU and Roskilde communities but I also feel for the foreigner students that may be prevented from experiencing the warmness and kindness my wife and I had the opportunity to be part of.
I got news regarding a position I applied to, it was rejected. What is particularly frustrating about this situation, is that you had to write a proposal and attach it to the application. It took me several days of work to write the application, reading books, papers, and so on to frame the proposal within the scope of the job advertisement. In the end, I got a template email basically saying "thank you for your interest". From my perspective, that kind of response does not help with your self-grow as a prospective employee. There is no feedback, nothing, just a boolean answer. Of course, I reached out in the hopes of getting a more elaborated feedback, after all we are humans and not AI models using backpropagation. I got an email wrote by a human but still template, however, what was interesting is that only short-listed applicants will get feedback on their application. This seems reasonable if you get a significant number of applicants. That being said, if complete feedback is only given to short-listed applicants, then why not require the proposal only to short-listed applicants? It seems to be a proportional reasonable request. It would be the equivalent of programming tests some companies perform as part of their hiring process.
I still have another issue tho. This is the 3rd position of this type that I have applied and the second that uses a hiring platform. The first position was sent by email, this was a long time ago. In that case I got an interview and then the employer reached out saying they picked the other candidate because he had more experience with Y which I totally get; there was a well-stated reason behind my rejection, of the n boxes we had to tick I checked only n-1. In the second case I got accepted. But in this case there was nothing. I find unsettling that in an era where there are platforms that help to bring talent in, it is still a black hole: information gets sucked in and nothing gets out. There is no theoretical/technical limitation that prevents employers from providing feedback even if you only send your CV; someone, somewhere had to say 'no because ...' and if you don't wish to type there are speech synthesizers or check boxes. I think even LinkedIn provides you with some metrics, if you pay them of course.
I feel there is a culture of opacity which obviously seem reasonable to some employers, perhaps feedback would give a glimpse into the thinking process behind selection which might be exploitable or legal-abiding? Bottom-line if we accept employers have the right to reject candidates for any reason, for example, if you have your head tilted to the right slightly on your profile picture, then nowadays, it is a piece of cake to write a program that desk-reject such applications and write a template email: "We thank you for your interest unfortunately your head was slightly tilted...". Then it is up to you as a job seeker to take it or leave it; unlike AI model training, we do have a choice to incorporate feedback or not.
I have many reusable paper sheets. Sometimes I print on one side and the other is clean, others I buy extra paper sheets that end up not being what I needed and sometimes friends gift me some extra clean or reusable paper sheets. I love handwriting, specially when I use fountain pens, there is something I like about them. My handwriting is terrible; I often tell my students that by the end of the semester they would master two skills, the academic goals of the assignment and decoding hieroglyphs :). I often craft my own notebooks and blocks. Don't get me wrong, I also have my fair share of Moleskine, Fabriano and 365 notebooks. Lately I'm using I block I made a long time ago, perhaps 7 or so years ago with some block sheets that were too thin for me at the time. The sheets are even older, perhaps 12 years? I don't even remember. But I bought a couple of new fountain pens, a Platinum Preppy Extra-Fine and a Pilot kaküno also Extra-Fine and both are great!. If the pen doesn't come with a converter, it is not a problem, I have a syringe, ink bottles and an affinity to mix inks :)
I have been spending a lot of time preparing Programming Without Problems there were many ups and downs and corrections and so on. At some point it was no fun to see so much HTML code together. So I wrote a small Python (go figure :) ) script to help me strip all HTML code and just focus on my writing. It seems to fit the bill so far so, I'm happy. It also helps me to manage my references automatically, so that is nice; if BibTeX is an interstellar ship, my code is the Wright's Brothers sketch :D.
When I was in high-school at UNAM's CCH-Sur I used to spend quite sometime at the library. It is a beautiful place, not only because the building is gorgeous but also because its mathematics collection is amazing. I spent hours there reading and browsing books and of course, napping.
I got the same feelings on the early days of Google. After clicking on the 3rd page or so :)
With AI (ChatGPT, etc.) it is like entering a library in which all books are blacked out and there is this annoying librarian asking what you want and giving you a paragraph of a black book.
When you know exactly what you are looking for, this annoying librarian could be useful. I have spent half a day looking for a reference of some quote I have buried deep in my brain. However, when you look for guidance, the annoying librarian is the least help you should seek.
I understand the need for empirical evidence on a claim. What I can't grasp is the reaction of surprise when the evidence supports common sense, like today on my social media feed regarding a paper showing accumulation of "Cognitive Debt" when using AI for creative writing.
AI will accelerate the success of those that 1) Know exactly what thing they are looking for, 2) it is not easy to find or tedious to achieve and 3) it is common enough in the training set.
AI will accelerate the creative process destruction of those that are novice to a subject and are seeking guidance.
Of course, I would not be surprise that a model happens to be advertised as fine-tuned to provide guidance.
Sometime ago I found that a "white noise" broadcast on YouTube helped me to concentrate while listing to it on my headphones. However, I feel it is too much of a waste to just listen to "random noise". So, I wrote a small Python program to generate some "white noise". We all know it is not trully random, but it is close enough :) And, since I had control over what I created, I decided to mix in some effects.
The first Passing Rain is white noise with the same sinusoidal volume control on both ears.
The second Passing Rain Adjusted is white noise with the same sinusoidal volume control on both ears, but before adjusting the volume, a linear interpolation with 35 points is performed between pairs of "sufficiently off" points. Effectively removing some randomness (actually a lot :D).
The third Alternating Rain is white noise with a sinusoidal volume control starting at for the left channel and another starting at for the right channel.
The fourth and fifth are more if you are into regular beats.
The fourth Slow Beat is a sine wave each second using only the upper part of the intensity spectrum. I'm using 16 bits for each number but only using the [0,32767] range.
The fifth Slow Motor are 15 sine waves per second, again using only the upper part of the intensity specturm.
Watch for your volume if you are using headphones!
I asked Gemini to generate a code to solve the 24 arithmetical puzzle in Ada. I haven't made progress checking the correctness of the generated code. I was planning to cross check it with implementations on other languages in RosettaCode, but it has been down for the best part of the week. What got my eye was the "tests" Gemini generated, here is an example:
It is difficult to believe this kind of code generation is taken seriously. Of course I chose this language and this problem because I don't expect to exist a lot of Ada implementations of 24. At RosettaCode there was none. And if you are using AI to generate code that has been done and redone for 1000 times, then, isn't really a cheaper way?
I watched a GitHub Copilot tutorial video about basics. At some point Copilot is asked what error handling functionality should be added to some code. The first one to be suggested was an ArgumentNullException and I immediately remembered back when I was an undergrad. The Java compiler used to refuse to compile your code if you failed to catch a NullPointerException that some method you called may throw. I had this professor that used to say: "Just compile the damn thing, I KNOW what I'm DOING".
I came across this post on the once
great, then not so great before they sold it and now it is just a
zombie. If I recall correctly it was one of the main aims of
C++: to not impose a certain mindset. But for some reason they lobby
a lot for modern C++. I think one of the reasons is that you need
certain standard guidelines in order to be able to work quickly
on different teams without the (re)learning curve. Some changes are
justified to help the careless developer. I think it is one of
the worst reasons to change a language, it is like saying: We are
going to introduce these changes into (insert your favorite natural
language here) because some people fail to speak it "properly".