LaTeX Questionnaire

Version history:
- April 10, 2023, added more questions
- April 16, 2023, increased questions to 70+
- April 23, 2023, increased questions to 80+
- May 5, 2023, increased questions to 90+
- May 25, 2023, increased questions to 100+

Please answer the following 100+ questions from the perspective of an LaTeX expert!

1 General
- Is there a built-in spelling correction?
- Can the hyphenation be deactivated?
- How many disc space is needed for a LaTeX distribution on the hard drive?
- Does the BibTeX format supports Unicode characters?
- Is LaTeX standardized?
- What is the preferred output media (e.g. physical paper, screen, mp3 audio)?

1a Programming
- In which programming language was the TeX software created?
- Which amount of programming skills are required to create LaTeX documents?
- Are programming skills required to create LaTeX packages from scratch?
- Are you using self created undocumented TeX macros within the document?
- How complicated is it to learn the MetaPost language?

2 File export
- Can MS-Word open a LaTeX document?
- Is it possible to export into the PDF/A document format?
- Is it difficult to copy&paste text from a LaTeX generated PDF document into other programs?
- Is LaTeX a good choice for creating non-pdf documents like blog entries and API documentation?
- Can Lualatex render a .docx file into the pdf format?
- Which kind of layout bugs are visible after converting LaTeX documents into the docx format with pandoc?

2a Team work
- Is LaTeX recommended for interdisciplinary teams with a focus on high productivity?
- What is better: a poorly written dissertation formatted in LaTeX or a well written one typesetted in MS-Word?
- Is it easy for newbies to become familiar with LaTeX?
- Was the LaTeX system created for the masses, especially for computer newbies?
- Has LaTeX a built-in version control system to record and track changes?

2b epub
- How to export into the epub format?
- How important is a fully justified layout for ebooks (e.g. low, medium, high)?
- How many ebub books are fully justified?
- Is the Plos One megajournal formatted in fully justified mode?

2c Lyx
- Is the "track changes" option in Lyx the same as the "track changes" menu in the Overleaf editor?
- Can a .lyx file be edited in a normal text editor?
- Is it possible to open a Lyx file from someone else on the local computer?
- Is Lyx better than MS-Word?

2d DVI
- Is the xdvi viewer able to render .docx files?
- Is there a DVI previewer for ecomstation (OS/2 Warp) available?

2e MS Word
- Does the average audience prefers MS Word or LaTeX generated documents?
- Do you know a Word template which imitates LaTeX?
- How does a LaTeX document looks different from a template based MS-Word document?
- What is the typographic quality of justified paragraphs in MS Word (e.g. poor, medium, perfect)?
- Suppose a dissertation thesis was written in MS Word, is the layout left-aligned or fully justified?

3 GUI
- Is there a GUI for entering mathematical equations?
- Can a table be created graphically?

4 Fonts
- Is it possible to use fonts from the operating system?
- Which steps are required to activate the Linux Libertine Font?
- Is a font size of 10.5pt possible?
- What is wrong with Computer Modern (e.g. not available as Type1, was dismissed over Times New Roman)?

4a Images
- Is it possible to position images manual?
- Can an image be positioned over existing text?
- Can pdflatex handle SVG images?
- What is the purpose of floating an image in the text?

4b Columns
- Is there a three column mode?
- Are the columns register true?

4c Layout
- Does a LaTeX formatted document looks cleaner than the MS-Word counterpart?
- Is it possible to create documents which are looking different from a LaTeX paper?

4d Glue
- Does optimized vertical space between paragraphs improves the layout?
- Are micro typographic extensions a must have?
- Is it possible to implement the Knuth Plass algorithm outside of TeX in a different software?
- Is there an implementation available for the Knuth Plass algorithm in the stack based Forth83 language?

5 Publishing houses
- Is LaTeX used by professional publishers?
- Is LaTeX superior over Adobe Indesign?
- Is the workflow the following: An author sends a Word document to a publisher and then the publisher converts it into Latex to streamline it with the internal workflow?
- Is it normal, that a journal asks the author for substantial rewriting?

5a Preprint server
- Is it easy to submit a LaTeX formatted document to a preprint server?
- What is your opinion about preprint servers which accepting only MS Word docx files?
- Can you name three papers at Arxiv, not created with LaTeX?
- How many academic papers are created with LaTeX (e.g. 30%, 60%, 90%)?
- Is the citation count for LaTeX formatted documents higher than for MS Word equivalents?

5b Document format
- How to make a peer review for a LaTeX document?
- is the .tex file format a zip container which can store text and images both?

6 Justified paragraphs
- How to activate left aligned paragraphs in LaTeX?
- Why is LaTeX recommended, if the rule is to format the manuscript left aligned?
- What is harder to realize from a technical perspective: left aligned paragraphs or fully justified text?
- Does a justified column needs less space, than the left aligned counterpart?
- Can a fully justified column be realized without adjusting the inter word spaces and without hyphenation?
- Has the PDF format the ability to store different word spaces which is needed for a justified line of text?
- Do justified paragraphs are looking great on a smartphone?

6a Sorting game
Please sort the following items by their typographic quality from low to high:
- MS Word fully justified, LaTeX left aligned, LaTeX fully justified, MS Word left aligned.
- Adobe indesign left aligned text, Quarkxpress left aligned text, MS Word left aligned text, LaTeX left aligned text

6b Reasons for justified text
- Is the idea behind the LaTeX software to imitate the Berthold diatronic typesetting machine including it's ability to produce justified text?
- In the time until the year 1990, which percentage of printed newspapers were using justified paragraphs (e.g. 30%, 60%, 90%)?

7 Accessible typography
- Is there a study available which proofs, that fully justified text is easier to read than left aligned text?
- Are documents with a complex layout (multiple columns, justified text, small font size, difficult language, serif fonts) a sign of excellence?
- Was the DVI format created to display text on different screen sizes which includes smartphones and Desktop PCs with flexible line breaking?
- Is a justified paragraph including hyphenation compatible with screenreaders like Jaws?
- is the PNG Graphics format the optimal output device for a LaTeX document (e.g. dvipng -D 300 main.dvi)?
- Does it make sense to use hyphenation on smartphone displays?

7a Checklist for accessible PDF documents
- Are the paragraphs left aligned?
- Was hyphenation deactivated?
- Is it a single column layout?
- Is there a 12pt sans serif font combined with 1.5 linespacing?
- Is the language simple English without complex vocabulary?

7b pdftohtml
- What is the result of using "pdftohtml in.pdf out.html" for a LaTeX generated pdf file (e.g. looks nasty, looks great)?
- Is the pdftohtml tool able to detect multiple columns, removes the hyphenations and fixes the interword spacings of LaTeX so that the generated html file looks great?
- How to convert a random pdf file from Arxiv into plain text?

7c LaTeX and Accessibility
- Can pdflatex generate a PDF/UA file?
- What is the quality of the CTAN accessibility package (e.g. low, medium, high)?
- If LaTeX is only a printer driver and the generated documents do not include structure and tagging, what does it mean for accessibility?
- Which OCR software can convert a pdf document into a LaTeX file?

8 Print typography
- Which program will produce the better print typography (e.g. MS-Word, LaTeX)?
- Which percentage of Arxiv papers are getting printed on physical paper?
- Are printed academic journals the preferred media for knowledge distribution?
- What are the costs to print out a single copy of a book which has 300 pages?

8a Web typography
- Is LaTeX trying to produce web typography which looks great on computer screens?
- Is LaTeX ready for digital publication in the PDF format?

8b Wall of text
- Which paragraph formatting will produce a wall of text (left aligned or fully justified)?
- Can a LaTeX document can have images to reduce the amount of text?
- What is the reader experience for a wall of text (e.g. easy to read, hard to read, unreadable)?

The difference between a pocket calculator and GPT-3 enabled Artificial Intelligence

In contrast to AI based software a pocket calculator was understood by the public quite well. Such technology is available at least since the 1970s and lots of books are explaining the inner working. The core element is a CPU which gets programmed in Assembly. And then the machine allows the user to enter a task like “2+4”. From a technical perspective a pocket calculator contains of the CPU which is in easiest case an 8bit model, there is some sort of onboard RAM and very important a program which takes the input of the user and sends instructions to the CPU.

Creating yet another pocket calculator in hardware is easy. Also it is possible to write a python software which emulates a pocket calculator. There are endless amount of software and hardware components available for this purpose and they can be explained easily to newbies. It is more complicated than normal mathematical but the technology is not very advanced.

In contrast, a modern gpt-3 driven Artificial Intelligence works very different to a pocket calculator. Even the system contains of hardware and software components it can't be grasped with the traditional terms used in computer science. Also it is more complicated to newbies what a neural network is about. The inner working of AI can be summarized the following way.

The AI software is able to convert back and forth from natural language into numerical arrays. The input of the user gets converted into numbers, then the system is doing something with the numbers, and the output is converted back into natural language. This number-word engine is the core element in Artificial Intelligence. it allows to solve any problem. Over decades it was unknown how to do so, and it was even unclear if such a transition is needed. Creating word embeddings is sometimes called the symbol grounding problem. For example the word cat is not only a sequence of single characters (C + A + T) but cat is represented in a conceptual space as a number next to other words like dog, mice and so on.

The surprising situation is, that after solving the word embedding problem, it is quite easy to construct a human level Artificial Intelligence. If a problem was reformulated as a numerical mathematical problem, existing computer technology can be applied to it which means the information are stored in the main memory and there are routines which are doing something. The only bottleneck is the transformation back and forth from words to numbers. The core element of any advanced Artificial Intelligence which allows to rebuild the gpt-3 software from scratch is a word embeddings algorithm. Such a software component takes an English sentence and converts it into a mathematical vector. The details of this sophisticated technology are not understood very well and it is a very new approach.

Determine prime numbers with a questionnaire

The classical approach to calculate the prime number works with trial division which is implemented in the programming language by choice on a modern PC. The speed depends mainly on the compiler efficiency plus some handcrafted performance improvements in the source code. The roesetta code website povides a good introduction into the subject [1].

What is missing in this classical approach from the past are heuristics which have nothing to do with programming itself but are reducing the problem space in a general way. A possible option for doing so is a questionnaire formulated in natural language. The interesting situation is, that such an approach has nothing to do with solving the original problem with computer programming but it is about reformulating the original problem. Here is an example:

1. Is the tested number greater 2?

2. Does the number ends with 0,2,4,6,8?

3. Is the number a multipie factor of the prime numbers from 2 to 100?

4. What is the Digit sum (add each single digit)?

5. Is it possible to store the prime sieve from 2 to 1000 on the computer system?

What the algorithm to determine the prime numbers has to do is to answer these questions. The answer is stored in an array in the format [yes,no,yes,26,no]

In response to a answer set the algorithm will choose a certain strategy to determine the prime number. The tool of a questionnaire allows to reference to domain specific knowledge in the context of prime number generation. There are lots of other questions available but for reason of simplicity it makes sense to start with only five of them.

The advantage of using a questionnaire to store domain knowledge is that it allows to formulate the knowledge in a machine readable and in a human friendly format at the same time. The wisdom is divided in chunks which are following the question - answer paradigm. The resulting array of answers can be processed by a computer easily.

References

[1] https://rosettacode.org/wiki/Sequence_of_primes_by_trial_division

Tribute to the FreeBSD operating system

 

In contrast to the Linux operating system, Freebsd is a less known software which should be introduced. The most important period was until the year 1990 in which BSD Unix was the defacto standard for Unix systems. During the 1980s, Linux wasn't invented yet and very advanced projects like the Sun has emerged. Unfortunately, the period since 1990 is less successful for the BSD operating system. First problem was that BSD was splitted into different sub projects like Freebsd, netbsd, openbsd and dragonfly bsd. Second problem was that since the year 1995 the University Berkeley stopped the development of code. So the BSD project has become a non academic project.

FreeBSD itself is running well so it is too early to describe it as a dead end, but many projects closely related to FreeBSD has become obsolete. The mentioned company SUN which was famous for Workstations and the Java technology has stopped its operation in 2010. The OpenSolaris operating system which is working with ZFS and dtrace was canceled in 2009. Another interrupted side projects are PC-BSD (canceled in 2018) and perhaps netbsd which has a very low amount of commits in the last year.

Let us take a closer look at FreeBSD itself. It the largest of the remaining BSD projects and has around 11000 commits per year. In comparison, the Linux kernel has around 74k commits per year. The user experience for FreeBSD on the desktop is disappointing. Most wlan cards are not supported, and the drivers for sound cards are poorly maintained. The user share who are prefering FreeBSD over Linux on the desktop is likely very low. Second problem with the FreeBSD ecosystem is that because of historical reasons the code isn't available under a GPL license which stands in contrast to other large Open source projects like Gimp or the Gnu c compiler.

How Artificial Intelligence was discussed in the 1990s

With the advent of Deep learning and humanoid robotics the AI community has demonstrated that everything is possible. Software is able to parse natural language, play video games and can control robots. The surprising success of AI technology wasn't expected by most researchers in the past. There is a difference how AI related problems are discussed in the past from today's perspective.

Before it makes sense to explain more recent algorithms there is a need to a take a look back how AI problems were analyzed 30 years ago in the 1990s. The obvious difference is, that the in past the amount of optimism was lower. In the late 1980s there was an AI winter available, which means that even computer experts were disappointed about the capabilities of neural networks and expert system. The reason why is that during this time a certain sort of questions were asked.

A typical problem discussed in the early 1990s was the n-queen problem which can be solved with a back tracking algorithm. At this time, such a problem was treated as a state of the art Artificial intelligence problem. Many obstacle were available before such a problem can be solved. First challenge was to get access to a reasonable fast computer. A normal Commodore 64 homecomputer was too slow for computer science problems and it was difficult to program the sourcecode. The more effective way in implementing computer science problems was an MS DOS PC in combination with the Turbo pascal programming language. So most of the effort in the 1990s was directed towards getting such sort of PC setup running. SImple tasks like installing the pascal compiler or write a hello world program with the IDE was some sort of advanced programmed task.

Suppose the AI programmer in the early 1990s has mastered all the requirements and was able to write simple programs in Pascal. The next challenge was to explore the direction called Artificial Intelligence. The n-queen problem is some sort of benchmark to test out search algorithms. The task is to find the positions of 8 queens on the chess board by simulating all the possible game state. And the only valid method to solve the problem was complete enumeration. Complete enumeration means to calculate thousands of thousands different possibilities and test if the solution fits to the constraints.

A typical runtime behavior of such an algorithm is, that it will occupy all the CPU resources over hours. The turbo pascal program gets first compiled into a binary .exe file and then it has to run for 3 hours without interruption. During that time the progress is shown on the screen as a percentage number which is counting upwards very slowly. At least in the 1990s such kind of problem solving strategy was state of the art Artificial Intelligence.

It should be mentioned, that in most cases the n queen algorithm hasn't figured the answer to the problem. Either something with the pascal program was wrong, or the state space was too large. The logical consequence was that the problem remains unsolved. That means, technology in the 1990s was not able to solve the problem. If simple n-queen problems remain unsolved, more advanced tasks like controlling a robot are also out of reach for the 1990s software developers.

Complete enumeration and backtracking algorithms are typical examples for non-heuristics problem solving strategies. In the 1990s these algorithms were used as the only strategy to address AI related problems. They are simple to explain and simple to implement. They need a huge amount of CPU time and it can be shown that they can't solve any serious problem. This outcome was used as a proof that a certain problem is np-complete which means, that computers in general are not able to solve it. Even if the computer is 10x faster it is not fast enough to traverse the state space in a reasonable amount of time.

Most of the AI programmers in the 1990s were educated with such a bias. The reason was that more advanced problem solving algorithm were unknown during this time.

 

AI in the 1990s vs 2020s

There are reasons available why AI has evolved over the years. The following table shows a direct comparison of the tools.

	early 1990s	2020s
Programming language	Turbo Pascal	Python
Operating system	MS DOS	Linux
Hardware	Intel 286 PC	multi core CPU
Algorithm	backtracking	heuristics
Problem	n-queen	Sensor grounding
Computer books	printed	Internet

There is no single reason why modern AI research works so much better but it is combination of factors. Most software in the 1990s was programmed on slow PC with complicated to use programming languages. In contrast, most programming works under stable Linux like operating system in combination with the easy to handle python language. This reduces development effort. Another major difference is the access to computer science books. In the early 1990s it was nearly impossible to read a state of the art book or paper about Artificial Intelligence. In the 2020s it is pretty easy to find such information online.

The logical consequence is, that over the years the researchers have created more advanced AI related projects. Former challenges are solved and lots of discoveries were made. The chance is high that this development is constant. That means in 20 years from now, lots of improvements are visible. Perhaps future programmers will laugh about current technology like the Internet or the python programming language.

A seldom described difference between AI research in the 1980s vs today are the problem which are addressed. Suppose the idea is to solve the n-queen problem. Then a certain sort of algorithms and programming languages are selected. Before the problem can be solved, somebody has to explain why it is important. From a technical perspective it is possible to solve more recent problems like “sensor grounding” on 1980s hardware and software. That means a simple MS DOS PC in combination with the pascal programming language can be utilized to realize an advanced robot. But, in the 1980s nobody was aware of such thing like the symbol grounding problem. Even the idea to realize a line following robot wasn't invented during this time.

On the other hand it is funny to see, that with modern hardware and software the well known nqueen problem remains unsolved. Even on a quadcore CPU and a 64bit operating system it is not possible to traverse the entire state space to find the correct positions for eight queens.

Zettelkasten Questionnaire

Please select a random Zettel from your analog slip box and answer the following 80+ questions.

1 Shape of card
- Is the paper lined (e.g. lined, blank, squared)?
- What color is the paper (e.g. white, blue, light red)?
- How thick is the paper (e.g normal index card 180gsm, thin paper 80gsm)?
- In which condition is the card (e.g. excellent, worn down, damaged by fire)?
- What abnormalities are there (e.g. paperclip on top, glue on back, extra postit notes)?
- Has the card holes on the left for a personal organizer (e.g. ISO 838, 6 ring binder)?

1a Longhand Scripture
- Was the text written in longhand (e.g. longhand, typewriter, PC printer)?
- If longhand, what sort of pen was used (e.g. pencil, fountain pen, roller ball)?
- if typewriter, which sort of typewriter was used (e.g. mechanical, electrical)?
- if PC, which sort of PC printer was used (e.g. inkjet, laser, dotmatrix)?
- What is the color of the ink (e.g. blue, black)?
- Is it readable for others (e.g. very good, medium, poor)?
- Are the characters connected which makes it harder for OCR recognition (e.g. yes, no)?
- Is the ink visible well (e.g. high contrast, fade writing)?

1b Layout
- What is the direction of the writing (e.g. portrait, landscape)?
- Are the lines written in two column mode (e.g. one column, two column)?
- Was a highlighter used for some words (e.g. no, yes in yellow)?

1c Size of card
- What is the size (e.g. DIN A6, A7, 3x5”)?
- How was the paper cut (e.g. pre-cut paper, by scissor, knife at ruler, paper cutting machine)?
- If a cutting machine was used, which type exactly (e.g. safety rotary, guillotine)?
- How many sheets of paper were cut at the same time (e.g. 0, 1, 2, 3)?
- is the card folded like DIN A5 folded to A6 (e.g. no, yes)?

2 Content
- How many words are written down (e.g. 50)?
- Does the Zettel has a picture (e.g. none, diagram, pencil drawing, mindmap, abstract drawing, photorealistic artwork)?
- Is there a table in the body (e.g. no, yes)?
- Is there a writing on the backside too (e.g. no, a bit, very much)?
- What is written on the backside (e.g. bibliographic reference, normal note, direct quote)?
- What is the language of the card (e.g. German, English, Lingua Latīna)?
- Is it a hub note or a normal note (e.g. normal_note, hub_note, bib_note, register_card)?

2a Reason for creation
- At which place was the card created (e.g. in the library, at home, at work)?
- Was the Zettel used for writing a new book (e.g. new book, new novel, none)?
- What is the purpose (e.g. recipe collection, excerpt notes, lecture notes, plotting a story)?
- How many people have seen the card (e.g. only me, one other, many)?
- Was the card used during a presentation in front of an audience (e.g. no, yes)?
- Has the content a practical nature (e.g. practical, theoretical)?

2b Format of Bibliographic reference
- Are there bibliographic references (e.g. 0, 1, 2)?
- What is the citation style of the reference (e.g. [AuthorYear], [1], (a))?
- is the page number written near the reference (e.g. no, yes)?
- Where is the Author-Title-year information located (e.g. on the backside, at bib card, in external database)?
- is the reference stored in a digital database too (e.g. zotero, bibtex, none)?

2b1 Content of bibliographic references
- What sort of reference is it (e.g. a paper, a book, a podcast)?
- Who is the author of the reference (e.g. Mark Twain)?
- Is the reference publicly available (e.g. in the internet, only in printed format)?
- Is there a direct quote from a reference on the card (e.g. no, yes)?
- Is the Zettel judging with a negative connotation about the bibliographic reference (e.g. negative, neutral, positive)?
- What is the language of the bibliographic references (e.g German, English, Lingua Latīna)?

2c Time
- When was the Zettel created (e.g. January 2023)?
- Does the Zettel contains of a written date (e.g. no, yes)?
- If there is a date, how was it created (e.g. manual pen, rubber date stamp)?
- If there is a bibliographic reference, from which year is it (e.g. 2014)?
- Were information added later (e.g. no, with 1 day delay, with 1 week delay)?
- How long does it take to write the Zettel in longhand (e.g. 5 minutes, 10 minutes)?
- How many minutes would it take to type in the Zettel into a computer keyboard (e.g. 4, 6)?
- Dropping the Cettel from 1 meter, how long does it take until it hits ground (e.g. 0.5 seconds, 1 second)?

2d Links
- Are there are outgoing links to other cards (e.g. 0, 1, 2)?
- Are there are incoming links from other cards (e.g. 0, 1, 2)?

2e Named entity recognition
- Does the note contains of person names (e.g. no, yes)?
- If yes, is he male (e.g. male, female)?
- Are locations like a city mentioned in the note (e.g. no, yes)?
- if yes, which continent is it (e.g. Europe, America, Asia, Africa)?
- Are dates available like a year or a certain time (e.g. no, yes)?
- if yes, which century is it (e.g. 21th, 20th, 19th)?
- Are product names mentioned (e.g. no, yes)?
- if yes, what is the price range (e.g. below 100 US$, more than 100 US)?
- Does the Zettel contain of measurement units like kilogram or meter (e.g. no, yes)?
- If yes, what purpose is it (e.g. measure biology, measure physics, social indicators)?

2e1 Part of speech tagging
- Does a sentence contains of a question mark to label a misunderstood subject (e.g. no, yes)?
- How many nouns are on the card (e.g. 10)?
- Is there a personal comment on the Zettel (e.g. no, yes)?

3 Indexing
- Was the card indexed with a tag for faster recall (e.g. no, yes)?
- Are some words underlined (e.g. 0, 1, 2)?
- Has the card a colored tab (e.g. no, yes)?
- What is the general subject (e.g. Math, Art, Business, Sociology)?
- Give two keywords which are describing the Zettel (e.g. Euclidean geometry)
- How important is the Zettel to understand the general subject (e.g. low, medium, high)?
- What is the word count of the title (e.g. 2, 4, 6)?

3a Machine readable
- Was the card scanned for backup reasons (e.g. no, as jpeg file, including OCR)?
- if the card was scanned, what is the dpi resolution (e.g. 150dpi, 300dpi)?
- Is there a barcode or similar identification system (e.g. none, barcode, qr-code, rfid)?
- Has the edge some holes (e.g. edge-notched card, none)?
- Is the information on the card the same as in a digital PKM software (e.g. no unique, yes a copy)?
- Was carbon paper used for write through on a second card for backup reasons (e.g. no, yes)?

3b Luhmann ID
- What is the position of the Luhmann ID on the card (e.g. top left, top right)?
- What is the format (e.g. alphanumeric, IP number like, with slash after first digit)?
- If the ID is nested, how deep is it (e.g. 3 levels, 6 levels)?
- What is the classification system (e.g. self created, Dewey decimal classification)?
- Has the card a fixed position in a stack (e.g. no, yes a juxtaposition)?
- Does the ID contains of a minus - sign to sort the card before the parent card (e.g. no, yes)?

There is no AI singularity yet

The main criteria for an upraising of robotics is mass production of intelligent machines. If advanced biped robots are created with only 1 units it is a boring research-only robot. Such a single unit robot has no relevance to the pubiic but it is a demonstration only project to write a paper about it.

True robotics is equal to increase to amount of units drastically. If millions of customer around the world are motivated to buy a certain product, that it is competitive. In case of robotics the situation is relaxed. The amount of cobots shipped for automation reason is only 45k units worldwide. The amount of miles driven by self driving cars is also very low.

Other household robots like vacuum cleaners and kitchen robots are not produced on a mass scale. Despite its attention on computer fairs the public demand for such technology not there.

Let us imagine who an AI takeover will look like without any real robots. It is a paradox situation because it is the opposite. An AI takeover described in the literature means usually that millions of human robots are produced on a mass scale so every household own at least one. Such a scenario is unrealistic. What is available instead are early prototypes similar to mechanical automaton in the 18th century. These robots are demonstrating the technical skills of university and research institutions but they are created with only 1 unit. This is similar to the famous fictional robot in Star Trek TNG which was also produced in a low amount of units.

What is available today are mass produced classical electronics devices lie smartphone, laptops and watches. But this technology can't be described as intelligent but it is normal pre-singularity innovation.

The opeq question is where robots are not mass produced yet? To answer the question lets take a look at the previously mentioned cobots. Cobots are industrial robots which are more powerful than normal automation technology. In most cases cobots are used for intermediate pick&place tasks at the conveyer. in contrast to a famous myth such a task has a low priority in the industry. Even if cobots are working great they are not used frequently. The amount of annually shipped cobots is 45k worldwide whcih includes all brands and all sort of cobots. so it ic compared to the importance of the industry similar to nothing.

If cobots are not used in the reality, how exactly are the conveyor belts automated? Right it is a rhetorical question. In most cases normal automation technique is used which doesn't need Artificial Intelligence but it is working purely mechanically. And the remaining tasks are highly complex and can't be automated soon. So we can say that the industry struggles even with easy to built cobots. It is unlikely that more advanced human robots are produced on a mass scale soon. Let us make a prediction in numbers who the year 2030 will look like.

the amount of cobots will grow by 5% annually. That means around 65k cobits are getting produced in the future per year. Not a single human worker is getting replaced by this little amount of technology. The chance is high that many of these cobots not even installed on a production facility. The same prediction can be made for self driving cars. The chance is high that because of regulation problems existing autopilots will remain offline and new cars are produced without any sort of AI. The only automation technique which is produced on mass-scale is an automatic door opener. That means, the owner can press a button and the car will unlock the door even if the owner is 1 meter away.

Such an easy to describe outlook sounds a bit boring for an audience which is fear that the robot revolution has already started. Most of the so called Singularity is only available in science fiction literature. The reality is much more conservative in introducing AI technology.

Typing speed for analog index cards

The main cause why modern computer users are shy to start an analog Zettelkasten is because they are believing in the myth of a highly efficient electronic data. The assumption is, that typing something on a keyboard is very fast and that URL and bibliographic references can be added by simple copy&paste to the knowledge database.

The underlying assumption behind the average Obsidian PKM user is, that there a no costs at all in creating all the nodes in the second brain while in contrast analog note taking is perceived as very complicated. The understanding is, that for creating an analog note somebody has to search endless time for a pen and needs lots of hours until all the index cards are written in long hand.

It is a widely known fact that Niklas Luhmann has created all the 90k index cards by hand. But Luhmann has worked in a time before the advent of the IBM PC. The prefered way of creating a Luhmann like Zettelkasten since the 1990s is of course a modern Windows or Linux computer which has many advantages of a luhmann style note taking system.

To get a better understanding why analog note taking is even in the year 2023 the most effieient way in creating a knowledge base, a short look in the figure may help. The spreadsheet compares the amount of data and the needed effort in minutes to create an analog vs. a digital zettelkasten.

The obvious difference is the typing speed in chars. It is a fact, that a computer keyboard allows to type in something much faster than an analog pen. On the second hand the difference is smaller than expected. If the user is reducing the amount of written of cards for each byte and also reduces the amuunt of daily cards he will need less overall time for creating new notes.

Let us slow down the situation a bit and describe why digital notetaking takes so long. According to the table lots of cards are created each year. Even if the typing speed is high, these cards are requiring manual effort. The user has to invest around 64.4 hours per year to write down 1095 digital note cards. It is not possible to reduce the needed time because there is a limit in the typing speed on a computer keyboard.

In contrast to a famous myth, the amount of effort for creating a digital card index is not zero but it will produce endless amount of hours in which the user has to type in something. And the example with only 3 newly created cards is simple one. If the user puts more effort into the Obsidian PKM software he will need more hours.

The main advantage of analog note taking is, that the user knows in advance that it is complicated to write in longhand. So he will think twice before he creates a new card and will reduce the information to a minimum. This allows to reach a higher productivity than with digital note taking. In the figure the overall needed time per year is smaller for analog note taking. That means, Analog note taking can safe time compared to digital note taking.

The main cause for the paradox situation is because of the slow typing speed in general. No matter if a human is preferring an analog pen or a digital computer keyboard, the possible speed is very slow. A typing speed of 170 chars per minute is equal 23 bit/s. In comparison, outdated analog modems are using 14400 bit/s as the average speed. Even if somebody is able to type in something very fast, his writing speed ridiculous slow. The world record on a computer keyboard is around 800 chars per minute. That means even the best user in the world will need endless amount of time until 1000 and more nodes in the Obsidian PKM software have been created.

The assumption is that the slow typiong speed in general plus the small difference to analog writing speed explains why a paper based Zettelkasten makes sense. It allows to use the ressources in an optimal way and avoids wishful thinking. Humans, no matter if they are prefering analog or digital note taking are not capable in creating hundred of index cards every day but in the optimal case there are only 2 or 3 newly created cards possible.