Safe artificial intelligence in the past

From a technical perspective it is very hard to prove that robotics isn't possible. It is even much harder to show, that AI is limited to certain level which can't be over jumped in the future. Also it is impossible to slow down the progress or reverse the development. There is no such thing like a frame to control the development of Artificial Intelligence. What is possible instead is to ignore the future and take a look into the past which is much easier to understand.

The major advantage of the 1990s compared to the situation in the now is that in the past robots weren't invented yet, also the amount of books about the topic was little. That means the 1990s were a time in which the problem of AI upraising wasn't there.

The 1990s were the decade before the upraising of the internet. The dominant media in this time was the television for the mass and the printed book for the educated scholars. The amount of information was small and no search engine were used. From the perspective of AI the situation was also in a very early stage. A robot like honda asimo who is able to walk upstairs wasn't invented. A company like Boston dynamics wasn't there and in the beginning of the 1990s the worlds strongest chess player was a human but not a computer. Also the subject of deeplearning wasn't invented. The only thing what was available were normal perceptrons with not more than 20 neurons. These systems were realized in software for the windows operating system but no useful application was known.

Self driving cars were also not invented in the 1990s because of many reasons. What was available instead were lot of movie related robots like the K.I.T.T. car, or the Data android in Star trek. And the perhaps most surprising fact is that the people in the 1990s were not convinced that Artificial intelligence wil become possible one day. A widepread believe was, that simple biped robots will be available in around 300 years in the future or they won't be possible at all. The underlying theoretical concept to prove this assumption was the np complete theorem. NP complete says basically, that all the AI problems can't be solved on a computer because of the large state space. A similar idea was introduced in the highlight report in the 1970s.

A widespread philosophical interpreation of Artificial Intelligence in the 1990s was, that since the beginning in the 1950s the AI researchers has promised many things but none of the goals were reach. So the rational understanding was, that AI is too complicated in general and it is not possible to build such machines. Nobody in the 1990s were able to disprove this assumption so it was equal to common shared knowledge.

So we can say, that the 1990s were the last real AI winter. AI Winter means that the subject was seen as impossible to realize and that the research towards the subject has stopped. What the computer scientists have done instead is to program normal software for example games, operating systems and very important network protocols for the upraising internet. That means, AI in the 1990s was an esoteric discipline not recognized very much.

Suppose a person from the year 2021 travels back into the 1990s and explains to the audience which sort of robots are available in only 30 years. He will say that biped robots are possible, that kitchen robots can be build, that self driving cars can be realized with neural networks and he will explain that the sourcecode for tetris playing AI Bots is distributed as open source to everybody. The audience won't believe any of these words. The audience will say, that it is impossible. If the time traveler would like to give the details and explain how these software can be realized the audience will leave the room because it is outside their horizon. It would be too much for a person of the 1990s to hear what reinforcement learning is about or that chess software can beat a human player.

The 1990s from the perspective of Artificial Intelligence

 

Describing the current situation in AI and robotics is nearly impossible. There are endless amount of projects and documentation available and at least once a week a new robot is shown at youtube which looks more human like than in the week before. Advanced topics like biped walking, speech understanding and playing of videogames are solved already or the chance is high that within the next 12 months such a success story will become visible and fiction and reality have merged to a complex media campaign. Is it not clear if a certain robot head is remote controlled or by an advanced algorithm or if a certain walking robot was able to do the steps in reality or it was drawn into the picture with animation technology.

If the current world is too complex it makes sense to increase the distance and observe something which has a clear frame around it. The 1990s are a great decade for describing artificial intelligence. The major advantage is that the amount of published books is known and that the level of technology in the 1990s was low. Biped robots weren't invented, chess machines were not able to beat the best human player (with a single exception invented by IBM) and most researchers were not sure if AI can be realized in the future.

Computer technology in the 1990s was in an early stage and most problems were located in slow hardware and bugs in the software.. Windows 95 was during that period a common operating system, and the internet was in the early beginning. This setup makes it easy to give a full overview about all robots and AI projects during this time.

Basically spoken, AI wasn't realized in the 1990s but it was a philosophical topic. The question was if experts in computer science think about how realistic AI will be in the future. That means 95% of the population wasn't informed about the subject at all, and the few computer experts who have used neural networks and expert system in reality were not able to demonstrate practical applications.

Artificial Intelligence in the 1990s was mostly a subject for movies and science fiction books. Lots of stories about intelligent robots were available in this period. Even it was not possible to build real robots it was within reach to imagine a future in which positronic brains and other technology allows humans in doing so.

The 1990s from the robotics perspective

 

... were great, because no such innovation was available. All the technology available today, wasn't invented in this period. What was common instead were normal computer technology plus some philosophical books about how to realize Artificial Intelligence in theory. It was even unclear if a chess computer would be able to win against the best human player. The general understanding about AI during the 1990s was, the same like it was formulated in the famous lighthill report. Basically spoken the idea was, that even some expensive AI projects were started at US universities none of them has resulted into something useful.

The only place in which robots in the 1990s were available was the cinema. In blockbuster movies and of course in the star trek series, many examples were shown. But again, none of these things were build by the researchers.

From today's perspective it is known, that the during the late 1980s, the Honda company has developed humanoid robots. But, during the 1990s the internet was not widespread available so even computer experts were not aware of it. It was common sense, that no one has tried to build walking machines because it is too complicated to realize. What was known in the 1990s were some examples for expert systems. Some of them were described in mainstream computer journals. Also it was known, that for playing chess or tictactoe some sort of Artificial Intelligence is needed. But it was unclear how exactly such technology can be realized.

From today's perspective the 1990s have much in common with the stone age. AI was something not invented yet and it was imagined that it will take 100 years or longer to build it. Only to get the figures, the plot of star trek TNG plays around 300 years in the future. And exactly this was the estimated duration until biped robots are build.

Perhaps one surprising insight is, that in the early 1990s robotic competition were not common. From a technical perspective such robots can be realized with 1990s technology easily. But at this time, no one has seen a need for doing so. What was common instead was to program smaller programs in prolog and lisp. For all home computers and PC a compiler was available and some books were available about the subject. What such prolog programs in the 1990s were able to do was nearly nothing. Some more advanced software was able to solve logic games, but most of the programs were created as hello world examples.

With today's knowledge it is possible to identify some advanced projects in the 1990s not mentioned yet. For example the MIT Leglab has built in the 1990s lots of walking machines. But again, the Internet wasn't invented so no one was aware of it. That means, even if they have build these machines and published papers about it, computer experts, and hobby programmers as well simply never recognized it. Or let me explain it the other way around. Suppose a time traveler would visit a larger university library outside of the U.S. in the 1990s and will read all the books He won't find a single piece of information about the MIT robots, the honda asimo project or any other advanced AI project from this time. Sure, if the imagined time traveler is visiting the MIT library and knows which paper he needs to read then he will find the information. But without such an advantage he stays completely clueless.

Symmetric typesetting with LaTeX

On the first look the LaTeX ecosystem looks highly complex. There are an endless amount of packages, tutorials and guidelines available. The surprising fact is that it is possible to reduce the idea behind latex to a single screenshot. What the LaTeX community tries to achive is shown in the top of the image. The typset paragraph looks similar to a poem. The headline is formatted with centering and the paragraph is fully justified. The justification was created by adjusting the glue between the words, plus minor adjustments of the microtype package.

In contrast, the picture at the bottom shows what the TeX community tries to prevent. In such a situation the symmetry is missing because everything was formatted with flush left.

The interesting situation is that this preference has nothing to do with the LaTeX software but with typography in general. So we have to understand why formatting something by centering it is perceived as beautiful. The idea is that the written word follows the principle of art. It is not only a book but it is a graphical creation. Or to be more specific, the reader of the text should get the impression. From an authors perspective it is trivial to format something with the centering mode.

Let us take a closer look into the shown paragraph. It is the same text, the same font and the software for rendering it was in both cases the latest version of the lualatex engine. The only difference is that in the second case the formatting style “flush left” was applied. Now the question to answer is which of the cases has a higher score in terms of typographic quality? The simple answer is that the top example gets the quality judgement “beautiful example for typesetting”, while the bottom case gets the judgment “low quality or even no typographic quality at all”.

This judgment is not based on any objective criteria but it is simple a preference for or against flush left. How can it be that the established latex software including the high quality Latin Modern Roman Font can create low quality typesetting? Because the untold assumption is that only centering text which includes the headline and the paragraph is beautiful and everything else is wrong.

The underlying reason why this rule is valid is because it takes more effort to create full justified text. If the text isn't created with an algorithm but with a hot metal printing press it will take endless amount of time to fully justify the text. What the typesetter has to do is determine the size of the words and calculate how many glue is needed. In contrast, putting the letters for the bottom example together can be realized much faster.

Modular programming with python – a tutorial for creating a game

Writing with python a small is not very complicated. The existing pygame library allows even newbies in doing so. Most tutorials are assuming that the game is created with the object oriented paradigm That means there are classes for the GUI, for the physics and for the main program. This assumption makes sense because since the advent of the C++ and C# language nearly all games are created this way.

A seldom mentioned technique for creating larger software programs was invented before tor the advent of C++. What was used before is called modular programming and can be realized with python as well. The interesting situation is that modular programs allows similar to OOP to divide a larger project into chunks which are created individual. First thing to do is to create the physics module which contains of a single file.

The formatting has similarity to a class but the class statement is missing. The next step is to create the main module which has also no classes but a variable for drawing the window and two methods. The interesting situation is that the physics module is not initiated as an object but it was only importad and then the main module is sending messages to it.

The game itself consists of a small circle on the screen which can be moved with cursor arrows left and right.

Object oriented programming without objects

 

The python programming language provides a seldom explained feature which is the ability to use modules. A module is different from the class concept and has much in common with header files in the c language. The idea is to avoid classes in pyton at all and destribute the coder over many files which are included with the import statement.

From a python perspective the advantage is low.. But a python script which is using only modular programming is much easier to convert into normal c code. That means the written program or game can be made faster in the future by translating the python code into c code. The interesting situation is, that using modules instead of classes is working surprisingly well. What the programmer can is to create small files with less than 100 lines of code. Each file consists of some functions and some variables on top. The variables are only getting accessed from the python module but not from the outside.

What modules doesn't allow is inheritance. Also it is not possible to put different modules into the same file. Each module is stored in a different file. This will increase the amount of files drastically. But thanks the ability to import modules hierarchically it is possible to create very complex applications. With dozends of modules which are combined into submodules.

The chance is high that python programmers are using a same technique. The c language has the disadvantage that it is more complicated to create such modules. Because in addition a header file is needed which is an interface to the outside. But in theory, this concept can replace object oriented classes. That means, there is no need to convert c code into c++ classes.

Modular programming has felt out of fashion since the advent of C++. Today the situation is that more complicated programs are mostly realized with the OOP paradigm. Also the UML notation knows only of classes but not of modules. But in theory both ideas allowing to create larger projects with thousands lines of code.

The only thing what is not working is to avoid classes and modules as well. If somebody writes down 20 variables and 40 functions into the same file it is hard to determine which functions gets access to which variable. Such code can't be maintained. So it is important to divide the code into smaller chunks with less than 100 lines of code. Such a file can be analyzed easily by a human programmer.

Upgrade to Debian 11 makes trouble

 

On some blogs in the internet it was suggested that the upgrade from Debian 10 to 11 is very easy. No it isn't. First problem is to fix the /etc/apt/sources.list file. There are different tutorials how to do so. Theuser has to change the name of distribution, but also the URL to the security mirror server. But suppose the user has carefully recreated the file and has run the “apt upgrade” command. What he can expect then is that only the kernel was updated. After rebooting the machine important programs like the e-mail software evolution or the matplot library doesn't work anymore. But at least the gnome environment seems to be stable. So the user will run for sure the apt full-upgrade command but this will make things worse. After the next reboot the icons on the desktop are missing and it is not even possible to start the terminal program. That means a simple attempt to upgrade the operating system has caused a system wide failure.

Only after manual login into a text only session next to the gnome X11 session and updating the system again will solve the issue. After many reboots, autoremove commands and fixing lots of minor problems it is some sort of miracle that the gnome session is running normal. That means the new Debian 11 system is booting and at least major programs like the Terminal and the Firefox browser can be started.

What we can say for sure is that the upgrade to debian 11 is working with lots of trouble and it is compared to Windows 10 not recommneded for beginner computer users. Linux (especially Debian) remains a construction site and the chance is high that after rebooting the machine the user can't login anymore.

Understanding the LaTeX typesetting system for realizing justified text

 

There are many tutorials available how to create papers and even dissertation projects with the help of LaTeX. What these manuals have in common is that they don't explain in detail why LaTeX is the better choice over word. It most cases the argument is the rendering quality of LaTeX is much higher because of better internal algorithm. To understand what does it mean in detail we have read existing dissertation documents and analyze how the documents are formatted.

What dissertation documents have in common no matter which software was used to create them is, that all of them are formatted with the justified layout. This formatting style is so obvious and so frequently used and has such a long tradition that it isn't mentioned explicit. The typical dissertation is formatted in a symmetric way. That means, the title headline is of couse formatted as center text, and the main body text is also formatted as center text. But for the main body the left and right edge is forming a straight line this is called by typographers a fully justified text.

It depends on the author how exactly this style was realized. A common option is to use the MS-Word software, disable the hyphenation feature and then format the entire text fully justified. The result is that between the words many empty spaces are visible.

Another option used by word authors is to activate the hyphenation feature first and then the rendered justified text has smaller amount of empty space. And exactly this situation is the reason why LaTeX is recommended as a word replacement. Because the LaTeX word wrapping algorithm is able to reduce the empty spaces further. The same text looks with LaTeX different, because LaTeX is using an optimized word wrap algorithm, and very important the microtype package. So the result has much in common with the output of the indesign software which is also able to create high quality text.

So what LaTeX is doing is simple: it creates fully justified text with the help of hyphenation and intelligent word wrapping so that the amount of white spaces is minimized. This ability is labeled by the LaTeX community has high quality output.

So let us go a step back ward and ask a simple question: why exactly is a dissertation formatted as fully justified text, what is about flush left formatting style? No body knows. Even the question is so extraordinary that it is hard to answer it. Basically spoken the paradigm is, that the only allowed formatting style is symmetric, in a way that the headline is centered and that longer texts are formatted fully justified.

This kind of rule is much bigger than the LaTeX community. The rule is valid for other programs like indesign and MS-word as well. The rule is also valid for dissertations written before the advent of the PC. So it is has to do with typesetting in general.

From a technical point of view the LaTeX software can produce better justified text than MS-Word. THis is not a subjective interpretation but a 1:1 comparison will show it. In contrast the difference between LaTeX and indesign is little, both are able to create optimized justified text. The open question is if fully justification in general makes sense. Is there a need to produce centric / symetric documents?

In the history there are two main exceptions from this rule available. Letter are usually created in flush left and the internet based HTML pages are also formatted in flush left. Everything else especially books, journals and dissertations are typesetted in the justified mode.

Perhaps it makes sense to explain the situation from a more positive perspective. A typical introduction into the LaTeX software starts with a direct comparison with MS-Word. On the left side the document is shown formatted with Word and on the right side the same is formatted with LaTeX. Of course the LaTeX rendered pdf documents looks better because the text density is higher. It has little or no white spaces and the page looks similar to a printed book. Because of this ability of LaTeX to generate high quality output, the software is used frequently for academic purposes.

What is not answered in this comparison is the problem of formatting in general. The untold assumption was that both examples (word and latex) have to format the paragraph in the fully justified mode. In this restricted domain, LaTeX is much better. IF the paragraph setting was changed to flush left the result is the same.