Post in Spanish, about some aspects of the Spanish [work] culture. I had this draft saved since August 3rd of 2016. I will post it now so that it naturally flows to the bottom of the total posts. Continue reading
The character limit in Twitter is a feature for greater reach. Make each character count, for your readers and your visibility. Long texts are not necessarily more valuable or useful, they have a greater extension to do so, and they have more sentences to try to get quoted and get visibility that way. If that works, short text may not be a good idea. This may be a matter of opinion. At the very least, this is a short guide to always be revised in the future, by me, and primarily for me. YMMV.
“I didn’t have time to write a short letter, so I wrote a long one instead.”
My opinion is clear, keeping posts short is important:
- Brevity is the soul of wit.
- LoC spent,
- Long posts are never finished, and never published. Do or don’t, there’s no try.
Short posts can always become long. Single tweets can become long discussions, and tweet storms are not so comfortable to write, or read, or discuss. To keep the process faster, more rewarding, and less boring, I am going to:
- make many assumptions about what readers know and can understand (less explanations), including Spanish, (not-so-pop) culture, and any acronym I know (but not too many),
- limit posts to 500 words, and
- stop striving to provide answers, because I still have a lot of things to figure out. Food for thought is not bad.
Current options (e.g. Twitter) are not the final form discussions on the Internet. Assuming zero or few replies (my case), blogs are fine despite of their “asymmetry”, when written with discipline. Twitter is successful for a number of reasons, but one is the discipline it imposes. Here are some suggestions for discipline to achieve similar results (in addition to the 500 word limit).
- The title is a sentence and an idea. It should fit in a tweet with the link. If reach is important, try to make the preview interesting, i.e. title and fist sentence.
- The whole post is about that idea, for clarification, explanation, presenting supporting information, proof, evidence, educated guesses,… If there is any related idea that could be necessary for the background, either link to some explanation, or write it if needed and then link to it. There are no excuses for digressing, edit and fix. Think of the single responsibility principle. If it is too short for a post (less than 200 words), then add a comment in the comment system.
- No fillers. 500 words is the limit, not the goal. You may think that lists are nicer with some number of elements (3, 5, 10,…). The best number is one. If that is not possible, the number that is closest to one is the best.
- Read and reword. Could you write shorter sentences? Each character counts. Remember Twitter. It should be as short as possible, if it is shorter than possible, that is fine. Misinterpretation may be better (e.g. accurate) than original intention.
Clearly, 500 words is plenty, maybe too much, so no excuses.
There is a theory about how people would just do drugs, have sex, and do no good in general if they did not need to be accountable by their jobs and the pressure of society to keep them under control. Note: not that doing drugs and having sex are bad things, but they are only good (for individuals and society) when in moderation and under control.
I have always thought I was not that kind of person. I have always thought that if I had some basic income, for example, then I would spend my days doing free software, publishing some research, and also having a healthier life: exercise every day, respecting sleep hours, enjoying more time with family and friends, etc. But I found myself wasting most of the time that I am not working.
A man who dares to waste one hour of time has not discovered the value of life.
Back to the point, certainly a job is not a sufficient condition to keep humans accountable, if that was so, work addiction would not be a problem. All sorts of volunteers, NGOs, parents,… prove that it is not a necessary condition either. A fulfilling life with a purpose is a much better way to keep humans accountable. Sometimes it is not about what you want from life, but what “life” wants from you, and caring about that.
Therefore, the theory mentioned in the first paragraph is clearly wrong. It is a quite harmful model, and something to forget. At this point in time, I do not think that life wants much from me. But I know that I do not want to be the kind of person described in the theory. Therefore, for various reasons, I am going to start to write some blog posts. They are going to be short and fairly crappy, to be honest, but they are better than nothing. This may be one of those rare cases of information published on the Internet where only the author reads it, and I am fine with that.
But more importantly, this may be one of those rare cases where a person (or group of them) can prove a theory wrong, intentionally. So if you say: “people will not do anything useful unless you pay them”, I respond: “you cannot stop me”.
To try to give a take-home message: try to do something that you genuinely enjoy and that you think that it is valuable, because that may be what gives you energy (motivation, not stamina), a sense of purpose in your life. Some things provide instant gratification (like Twitter), some things take a bit longer (like WordPress), and some much longer (like GitHub). I am going to try to train myself to get more and deeper gratification by doing gradually harder and more useful things. Maybe you should try to do the same thing.
tl; dr: FMA:B does not criticize science, nor scientism, it criticizes taking something that is not deserved, the greatest sin committed by the homunculus. The core of the series is truth and value. A similar sin would be proselytism on ideas worth nothing or with a negative value, bullshit vending, which seems to be the main occupation of too many philosophers nowadays. Be skeptical on ideas, and loving on people, nothing is more valuable, as people are the ends that provide value to any means.
FullMetal Alchemist: Brotherhood may be my favorite series of all time, e.g. I took my Twitter avatar from it. [Spoilers in the whole post]. The series explains how wrong the concept of “human resources” is as well as concepts like anti-fragility in simple and beautiful ways.
As a consequence of my reflection on that last link, I was considering to go (in some far future, i.e. never) through the series commenting each episode or scene, and the “truth behind the truth” in each of them. I saw much truth and value in the series, maybe because there are apt and consistent metaphors for real phenomena, or maybe because of my interpretation. To find out which one of the two could be the case, I decided to check what other people had done. I found an analysis on the eyes and their meaning, quite right and partial (to the eyes part) IMHO, and an analysis of the philosophy in the series, and my reply to it as a comment became so long that it is now this blog post [probably only] you are reading.
There are two problems with the latter video:
- The lesser problem: it does not accurately describe the series, numerous references to Feyerabend clearly indicate an epistemological anarchism bias, opposed with the series.
- The greater problem: it masks criticism towards science through scientism from epistemological anarchism. While verificationism has great flaws, they have been interpreted as a blank cheque by all kinds of bullshit vendors to promote relativism and get rid of the so uncomfortable truth.
In the remainder of this post, I will go through both of them.
About the series
There are two main concepts in the series: truth and value. The main truth is the law of equivalent exchange (especially highlighted in the previous series), and Truth is even a character of the series.
The law of equivalent exchange resembles conservation of mass and conservation of energy laws, but it refers to value, it is therefore directly linked to economics. Equivalent exchange means zero sum games, breaking it as the homunculus means negative sum, and the final proposal from Alphonse Elric is the creation of positive sum games. Red, blue, (and possibly green) are associated with this meaning in general culture, like the Western stock markets, the Jedi lightsabers, and the colors of transmutation energy.
The only way to meet Truth is through the portal of Truth, composed by the knowledge in alchemy. This frontally opposes to epistemological anarchism. In the end, Edward Elric exchanges his portal for his brother, this does not mean that there are alternative paths to reach to the truth, but that there are some things that are more valuable for Edward Elric than truth and alchemy, e.g. his brother. Friendship being more valuable than knowledge, and similarly medicine (main focus of von Hohenheim) being more valuable than epistemology (main focus of Father).
The philosopher stones allow “ignoring” the law of equivalent exchange. In fact, they are exchanging human lives for something. This is something that science does not usually do, but economy exchanges time for money (is that equivalent exchange?) and gets things done through the use of men-month. It is very clear in the reference of humans as resources, something that does not concern science, but is the core to economy, capitalism, and businesses. Similarly, Tucker is not pushed by science, but by economy.
Godwin is right in this one, though, and coincidentally the greatest force stopping the Third Reich was Communism (on the other side of Briggs). Nevertheless, associating Nazism with scientism is a bit shallow. Nazism and wars in general are often associated to economic causes, like hyperinflation, and the oil that we would find in Ishbal if it was not for the location for the transmutation circle. Father even has the monopoly of alchemy in Central, being able to block it.
By focusing so much on truth and value, the series focus mostly on axiology, ethics, and economy. Criticizing faith is a bit out of place when God (as Truth) appears in the second episode and then some more. What we find is mostly a critique of bullshit vendors that will get something valuable from people (their time, effort, money, or something else) in exchange for something delusional in nature. Nevertheless, when asking for a philosophical perspective, it is easy to find some criticism to science and any other competing epistemological approach that there could be, more on that in the remainder of the post.
About science and philosophy
Mad people committing atrocities in the name of science is not what science is. Epistemological anarchism is an easy position to defend, as it is solipsism, nihilism, and in general any denial. We can also say that, from the perspective of pragmatism or functionalism, lies may work just as well as truths. But in the end, there can only be one truth, and we try our knowledge to get as close as possible to such truth, then falsifiability, verifiability, and in general any check of the verisimilitude of a particular claim is required to accept something as true. Doing otherwise means failing, and while different speculative bubbles may last for different periods of time, the price of errors does similarly increase over time. It is not possible to escape the truth for ever, but it is possible to deny it for long periods of time, always with awful consequences.
There are some things that we do not know how to measure or observe (directly or indirectly), but we can do so and we must strive to do so, as the only things that cannot be observed indirectly have no effect in our world, and either they do not exist or are irrelevant, innocuous.
There are many problems in the knowledge acquired with science, mostly because it relies on humans, errare humanum est. But science is more than a body of knowledge, it is a mindset, and a very positive one. No other discipline has achieved such a great success and improved human life quality as much as science. While science has been corrupted by capitalism, this is not a problem of science itself, but of lack of science to apply in areas like economy, ethics, axiology, game theory, politics, and utility theory.
The scientific method has been defined in such rigid ways that not even scientists follow it. Many variations and new methods emerged to deal with problems in more scientific ways, and they should be recognized as such. Ultimately, it is a form of reinforcement learning, that tries to make predictions about observable outcomes, if the predictions are right, then we say that it works; and if they do not, then we have to revise the theory or model. Even if all models are wrong, some are useful, and not all are equally useful. For example, consider a person that is blind. Philosophy may help that person to accept it and deal with it, while science may help that person to see. Among the two options, I think that one is infinitely more useful than the other.
Finally, nothing among what is relevant and philosophy or any other discipline does cannot be done with science. The only property that philosophy has over science is the capability of accumulating bullshit that does not go through the hard scrutiny and skepticism of science, i.e. discuss what is not observable and verifiable. That is why science works, and others cannot say the same. In fact, I would go as far as to assert that, if it works, then it is science, because being able to observe that it works (so as to assert it) is enough to apply science to its study, and check how it works, when it works, and in which ways it works best.
PS: In retrospective, after spending so many hours to write this post, I will certainly not comment on each episode separately. In addition, I plan on sticking with 500 words posts. There are many points that could have been elaborated further, but that would mean doing three or more posts. This is just too much, for me and for you (someone got this far? Please comment to confirm).
1. Add noise in the training to avoid overfitting
One common problem in deep learning, artificial neural networks (ANNs) and in general machine learning is overfitting. Due to overfitting, machine learning algorithms focus on a few specific features of the data and do not generalise well to new cases of the data.
As a consequence, by analysing how the ANN works, it is feasible to trick it into mistakes. Having some noise that precisely makes the ANN mistake a panda for a vulture is unlikely statistically, but shows that they can make big stupid mistakes. If ANNs are used more and more often, then this kind of mistakes becomes more and more statistically probable, and eventually happens more and more often.
A simple solution is to add noise in the training set, so that ANNs are more resilient to noise and less prone to overfitting. Noise can come in many different varieties, from white noise, to anything else. In the particular case of ANNs used for image processing, due to the spatial component of the dimensions, different types of distortions can be applied, e.g. lens distortions. This is not common, AFAIK.
2. Analyse, synthesise, generalise
Currently, ANNs do some analysis of the data, even if implicit in their adjustment of the weights. They are very specific, hence the overfitting.
Approaches like self-organising maps allow synthesising what has been learned from the examples, and then make the ANNs more simple, by removing neurons and connections that perhaps were not so useful. This is particular relevant for the optimisation, as deep learning models can become very complex and computationally expensive.
Self-organising maps seem to be more concerned with adding new neurons and connections, though. If this is used for the analysis then nothing good is going to result from that, but a worse overfitting. This capability of making an ANN more complex has to be used to generalise the network to more cases and more complex problems.
3. Learn from few examples
AlphaGo could defeat Lee Sedol in a quite consistent way (four out of five games). On the one hand Lee Sedol learned a lot from those games, especially the four he lost, but probably from all five. On the other hand, the learning that AlphaGo performed on those five games is probably negligible, and it is very likely that it did not learn anything at all in the games it won.
AlphaGo learned from millions of games before facing Lee Sedol, more games than humans could ever play in their lives (or would probably want to play, given the chance to live just for that). Five games are not going to make a big difference, and probably they should not after the extensive experience. While this has a positive side in our chances to defeat Skynet, it is disappointing when we expect AI to do more complex and general things.
There are approaches that use fewer training cases and learn more from them, support vector machines and case based reasoning being two of them. In the end, connectionists and deep learning experts may find something useful in other AI approaches, if they are open for collaboration.