March 31, 2025

Vergleich der PC Revolution mit der KI Revolution

Im vorherigen Post wurde einführend auf die Unterschiede hingewiesen zwischen der Technologierevolution Anfang der 1990er und der KI Revolution ab den 2020er Jahren. Hier folgt eine detailierte Tabelle. Zussammengefasst besagt diese, dass sich beide Epochen grundlegend unterscheiden.


1990er PC Revolution
2020er KI Revolution
Zeitraum
1990-1993
2020-2023
ökonomische Auswirkungen
neue Jobs in der IT Industrie
Angst vor Massenarbeitslosigkeit, mögliche Verdrängung von menschlicher Arbeit durch Roboter und LLMs
Ethische Fragen
beschränkt auf Datenschutz und Urheberrecht
komplexe ethische Herausfordrung in Bezug auf Überwachung, Asimov Gesetze und denkende Maschinen
mediale Darstellung
der PC als Motor des technischen Fortschritts
KI als Bedrohung, dystopische Zukunftsvision
Geschwindigkeit
langsam und schrittweise
sehr schnell, exponentielle Verbreitung von künstlicher Intelligenz
Demokratisierung
Technologie war stark von Unternehmen abhängig
leichter Zugang durch Open Source/ Open Access und Cloud Computing was Chance aber auch Risiken beinhaltet
Globalisierung
stark von den USA getrieben
weltweites Phänomen, Wettbewerb zwischen den Staaten
Komplexität
leicht durchschaubar für Hobby Bastler
hohe Komplexität, abstrakte Sprache, selbst für KI Experten unverständlich, Neuronale Netze agieren autonom als black boxes
Messen
Cebit, Comdex, CES
keine, lediglich Youtube clips mit Showrobotern
Buchpublikationen
breites Angebot an Ratgeberliteratur zu Computerhardware und -software
keine breite Rezeption, lediglich spezialisierte Konferenz-Proceedings verfügbar in Kleinstauflage




Die PC Revolution Anfang 1990er

 *I. Einleitung*

Anna betrat den kleinen, überfüllten Computerladen, und ein vertrautes Summen erfüllte ihre Ohren. Es war das Geräusch von drehenden Festplatten, von Lüftern, die gegen die Sommerhitze ankämpften, und von dem leisen Knistern statischer Elektrizität. Der Geruch von warmem Plastik und frisch gedruckten Handbüchern vermischte sich mit dem süßlichen Duft von Kabeln und Elektronik. Es war ein Geruch, der für sie nach Zukunft roch.

Die Regale waren vollgestopft mit bunten Softwareverpackungen, auf denen pixelige Grafiken und verheißungsvolle Slogans prangten. Windows 3.1, das Betriebssystem, das die Welt verändern sollte, stand in einem Regal, daneben stapelten sich Disketten mit Spielen wie „Monkey Island“ und „Doom“. Die Luft war erfüllt von einer elektrisierenden Aufbruchstimmung, einer Vorahnung dessen, was kommen würde.

Anna arbeitete hier, in diesem kleinen Laden, der ein Fenster in die digitale Zukunft war. Sie liebte es, die neuesten PCs zusammenzubauen, die verschiedenen Grafikkarten und Soundkarten zu vergleichen und die Kunden in die Geheimnisse der Computerwelt einzuweihen. Es war mehr als nur ein Job; es war eine Leidenschaft, eine Obsession.

Die PC-Revolution hatte gerade erst begonnen, und Anna stand mittendrin. Die ersten PCs wurden erschwinglich, und immer mehr Menschen erkannten das Potenzial dieser Maschinen. Es gab eine spürbare Begeisterung, eine Aufregung, die sich in der Luft ausbreitete. Es war, als ob eine neue Welt aufging, eine Welt voller Möglichkeiten.

Das Internet, noch in seinen Kinderschuhen, flüsterte von einer vernetzten Zukunft. Bulletin Board Systeme (BBS) und frühe Online-Dienste wie CompuServe zeigten einen Vorgeschmack auf das, was kommen würde. Anna verbrachte Stunden damit, sich in diese neue Welt einzulesen, die noch so unerschlossen war. Sie träumte von Websites, von E-Mails, von einer Welt, in der Informationen frei flossen.

In diesem kleinen Computerladen, inmitten von Kabeln, Disketten und dem Surren von Festplatten, spürte Anna, dass sie Teil von etwas Großem war, von einer Revolution, die die Welt verändern würde. Und sie war bereit, diese Revolution mitzugestalten.

*II. Der Sog der Möglichkeiten*

Anna war wie verzaubert. Die Möglichkeiten, die sich mit jedem neuen Programm, jedem neuen Hardwareteil auftaten, schienen grenzenlos. Stundenlang saß sie vor dem Bildschirm, experimentierte mit neuen Softwareanwendungen, versuchte, die Geheimnisse der Programmierung zu entschlüsseln. In der Abgeschiedenheit des Lagers, wenn der Laden geschlossen war, tippte sie Zeile um Zeile in BASIC, versuchte sich an kleinen Spielen oder einfachen Anwendungen. Sie träumte von dem Tag, an dem ihre eigenen Programme die Welt erobern würden.

Das Internet, damals noch ein ungestümes Kind, zog sie in seinen Bann. Sie verbrachte endlose Stunden in den frühen Online-Foren, las von Menschen auf der ganzen Welt, die ihre Leidenschaft für Computer teilten. Es war ein Gefühl der Verbundenheit, das sie nie zuvor erlebt hatte, eine Ahnung von einer globalen Gemeinschaft, die durch digitale Fäden zusammengehalten wurde.

Die Aufbruchstimmung war ansteckend. Überall entstanden Computerclubs, in denen sich Gleichgesinnte trafen, um über die neuesten Entwicklungen zu diskutieren. In den Schulen und Volkshochschulen wurden Computerkurse angeboten, die oft innerhalb weniger Stunden ausgebucht waren. Die Menschen waren hungrig nach Wissen, nach dem Verständnis dieser neuen Technologie, die ihr Leben verändern sollte.

Auch die Popkultur wurde von der digitalen Revolution erfasst. Filme wie „Hackers (1995)“ und „Das Netz (1995)“ malten ein aufregendes, wenn auch oft überzeichnetes Bild der Cyberwelt. Die Musikszene experimentierte mit digitalen Sounds und Samples, und in der Mode spiegelten sich die klaren Linien und geometrischen Muster der Computergrafik wider.

Anna spürte, dass sie Teil von etwas Größerem war, einer Bewegung, die die Welt verändern würde. Es war ein Gefühl der Hoffnung, der Aufregung, ein Glaube an die grenzenlosen Möglichkeiten der digitalen Zukunft. Und sie war bereit, ihren Teil dazu beizutragen, diese Zukunft zu gestalten.

*III. Der Traum vom digitalen Durchbruch*

Anna saß in ihrem kleinen Büro, das sie sich in einer Ecke des Lagerraums eingerichtet hatte. Der Bildschirm flackerte sanft, während sie die letzten Zeilen ihres Programms eintippte. Es war kein Meisterwerk, nur ein einfaches Tool, das die Verwaltung von Kundendaten vereinfachen sollte. Aber für Anna war es mehr als das. Es war ein Beweis dafür, dass sie etwas schaffen konnte, dass sie Teil dieser aufregenden neuen Welt sein konnte.

Sie hatte sich entschlossen, ihre Software auf einer kleinen Computermesse in der Nachbarstadt zu präsentieren. Es war ihre erste Messe, und sie war nervös, aber auch voller Hoffnung. Als sie ihren Stand aufbaute, umgeben von anderen kleinen Softwareentwicklern und Hardwarebastlern, spürte sie die gleiche Aufbruchstimmung wie im Laden. Die Menschen waren neugierig, offen für neue Ideen, bereit, Risiken einzugehen.

Die Resonanz war überwältigend. Die Leute waren begeistert von ihrer Software, stellten Fragen, gaben Feedback. Anna fühlte sich wie im Rausch. Sie hatte etwas geschaffen, das die Menschen interessierte, etwas, das einen Unterschied machen könnte. Am Ende der Messe erhielt sie sogar einige Angebote von kleinen Softwarefirmen, die ihre Anwendung vertreiben wollten.

Doch trotz des Erfolgs gab es auch Herausforderungen. Die Technologie war noch jung, voller Fehler und Unzulänglichkeiten. Die Internetverbindungen waren langsam und instabil, und es gab noch keine klaren Regeln für die digitale Welt. Anna sah, wie einige ihrer Kollegen mit den technischen Problemen kämpften, wie andere von den großen Softwarefirmen überrollt wurden.

Es war eine Zeit des Aufbruchs, aber auch eine Zeit der Unsicherheit. Niemand wusste genau, wohin die digitale Revolution führen würde. Es gab Ängste vor dem Kontrollverlust, vor der Entmenschlichung, vor dem Verlust von Privatsphäre. Aber es gab auch die Hoffnung auf eine bessere Zukunft, eine Zukunft, in der Technologie die Welt verändern würde.

Anna wusste, dass sie Teil dieser Zukunft sein wollte. Sie wollte die Herausforderungen annehmen, die Risiken eingehen, die Möglichkeiten nutzen. Sie wollte ihre eigenen Pixelträume verwirklichen.

*IV. Auflösung*

Anna saß in ihrem kleinen Büro, das inzwischen mehr einem Kontrollzentrum glich, und blickte auf die Bildschirme, die mit Diagrammen, Zahlen und Codezeilen gefüllt waren. Ihre Software, anfangs ein einfaches Tool, hatte sich zu einem komplexen System entwickelt, das von kleinen Unternehmen in der ganzen Stadt genutzt wurde. Sie hatte ihr eigenes Unternehmen gegründet, „Pixelträume“, ein Name, der ihre Hoffnung und ihren Glauben an die digitale Zukunft widerspiegelte.

Die Computermesse, auf der sie ihre Software vorgestellt hatte, war ein Wendepunkt gewesen. Plötzlich war sie nicht mehr nur eine Mitarbeiterin in einem Computerladen, sondern eine Unternehmerin, eine Pionierin in einer neuen Welt. Die Angebote, die sie erhalten hatte, waren der Anfang gewesen. Sie hatte sich entschieden, ihr eigenes Ding zu machen, ihre eigene Vision zu verfolgen.

Die Herausforderungen waren enorm. Die Technologie entwickelte sich rasend schnell, und Anna musste ständig lernen, sich anpassen, neue Wege finden. Sie verbrachte endlose Nächte damit, Programmcode zu schreiben, Fehler zu beheben, neue Funktionen zu entwickeln. Sie kämpfte mit den technischen Unzulänglichkeiten der Zeit, mit den langsamen Internetverbindungen, den inkompatiblen Systemen.

Aber sie war nicht allein. Es gab eine ganze Bewegung von Menschen, die die digitale Revolution vorantrieben, eine Gemeinschaft von Hackern, Programmierern, Bastlern, die die Grenzen der Technologie ausloteten. Sie trafen sich in Computerclubs, tauschten Ideen aus, arbeiteten an gemeinsamen Projekten. Es war ein Gefühl der Zusammengehörigkeit, ein gemeinsamer Glaube an die transformative Kraft der Computer.

Die Popkultur spiegelte diese Aufbruchstimmung wider. Filme wie „Matrix“ und „Strange Days“ entwarfen düstere, aber faszinierende Visionen einer digitalen Zukunft. Musikgruppen wie „The Prodigy“ und „Chemical Brothers“ experimentierten mit elektronischen Klängen und schufen einen Soundtrack für die digitale Revolution.

Anna war mittendrin. Sie erlebte die ersten Schritte des Internets, die ersten Websites, die ersten E-Mails. Sie sah, wie sich die Welt veränderte, wie Informationen schneller und freier flossen, wie neue Formen der Kommunikation und Zusammenarbeit entstanden.
^
Aber es gab auch die Schattenseiten. Die Ängste vor dem Kontrollverlust, vor der Entmenschlichung, vor dem Verlust von Privatsphäre wurden lauter. Es gab Berichte über Cyberkriminalität, über Viren und Hackerangriffe. Die Menschen waren fasziniert und verängstigt zugleich.

Anna versuchte, die Chancen und Risiken der digitalen Revolution zu verstehen. Sie las Bücher über Ethik und Technologie, diskutierte mit ihren Kollegen über die Verantwortung von Softwareentwicklern. Sie wollte nicht nur Teil der Revolution sein, sondern sie auch mitgestalten, sie in eine Richtung lenken, die der Menschheit diente.

Die PC-Revolution hatte gerade erst begonnen, und Anna wusste, dass noch viele Herausforderungen und Überraschungen auf sie warteten. Aber sie war bereit. Sie war bereit, die Pixelträume zu verwirklichen, die sie und ihre Generation hatten. Sie war bereit, die digitale Zukunft mitzugestalten.


Comparing the PC revolution in the 1990s with the AI revolution in 2020s

In the history of computing lots of revolutionary decades were available, in the 1950s the first mainframe computers were build and in the 1970s the first Minicomputer like the Dec PDP-11 were available. The shared similarity was, that a new sort of technology was build / invented which has replaced former technology.

The last great computer revolution took place in the early 1990s. From 1988-1994 the IBM PC has improved drastically. Until 1988 a PC was a very expensive office machine with a text mode graphics card, but in 1993, the typical 486SX PC was great for gaming, for writing texts and for getting access to the Internet. The amount of software for the Intel/Windows PC has increased dramatically, because many millions of private and commercial PCs were sold and used on a daily basis.
In the 2020s there was another big revolution available which was the AI revolution. This AI revolution was initiated by chatgpt and has evolved into a full-scale introduction of Artificial Intelligence which goes beyond of simple chatbots. The difference to the early 1990s revolution is, that its much harder to describe the AI revolution in detail.
In the early 1990s, the transition could be isolated to the availability of new computer hardware/software and computer-networks. That means, there was a cheap Intel 486SX processor available, there was a new MS Windows 3.11 operating system programmed and new computer networks like the Internet and Compuserve were there. in contrast, the current AI revolution doesn't belong to classical categories of computing.
From a hardware perspective, the former Moores law seems to be dead. That means, the existing 64bit CPUs are no longer improving anymore, and software remains also stable. A modern Windows 11 operating system looks the same or even worse than the former version which was Windows 10 or Windows 7. So the development can't be called a revolution but a stagnation. At the same time, the AI revolution is a reality, but it doesn't belong to the computer industry.
To explain this contradiction we have to describe what Artificial Intelligence is about. Its not a certain sort of software and it doesn't belong to computer hardware. So it doesn't take place in the reality but its a virtual revolution. Let us go a step backward and describe what the situation was for the computer revolution in the early 1990s.
This revolution was a bottom up movement initiated by the people. Lots of computer users have bought a PC including software packages at this time, and new computer stores were created world wide. In addition, book publishers have discovered the subject and endless amount of computer related books were sold about the internal working of Winword 6.0, about the Turbo Pascal programming language and about the Win 3.11 API.
In contrast, the AI movement since the 2020 is working with a top down approach. There are no stores available in which AI technology is sold to the masses, also there are no computer expos available like CeBIT or Comdex. And even books about AI are not available. The most famous AI book is "Russel/Norvig: AIMA" which
quote "... is considered the standard text in the field of AI" [1]
but in this AI bible, no section about Large language models is available. The only chapter which explains the subject on the surface is section 24 "Deep Learning for NLP" [2].
On the other hand, there is a dedicated place in which AI is visible. In the Gutenberg Galaxy, and especially in the conference papers after the year 2020, the AI revolution is visible everywhere. Lots of new algorithms, datasets, and applications for large language models are presented to the reader in over 1 million papers. The only concern is, that the Gutenberg galaxy and especially the published conference paper are ignored by the public. The total amount of readers is very small.
The surprising situation is, that the AI revolution isn't mirrored in the mainstream book market, and not in computer journals, there are no AI related expos available and even large scale tech companies doesn't know how Large language models are working. In combination, this is a unusual situation because in the former 1990s computer revolution, the situation was the opposite.
The working thesis is, that the AI revolution was initiated top down with its roots in the academic papers since the 2020s. Its a virtual revolution which is bypassing existing economical or technological structures.
sources:
- [1] https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Modern_Approach
- [2] AIMA book, 4th edition for U.S., 2020, https://aima.cs.berkeley.edu/

March 22, 2025

np hard problems as terra incognita

Computer science is an academic discipline with a long tradition. Its core elements are programming languages and algorithms. .Endless amount of source code was written in multiple programming languages, there are even esoteric languages available like Intercal and Befunge which are examples for computer art. Unfortunately, computer science consists of a seldom described weakness which are np hard problem.

An np hard problem is a mathematical challenge which can't be solved with existing programming languages nor algorithms. That means, modern languages like C++ in combination with modern 64bit operating systems like Linux isn't powerful enough to determine the shortest route for 15 cities or find the shortest sequence for solving the rubik's cube puzzle. What makes the situation more complicated is, that even after adding more RAM to a computer, np hard problems remain unsolvable.
One possible explanation why computer science is ignoring np hard problems might be, that its useless to invest ressources into problems, which can't be solved. If 100 researchers in the past have independent from each other tried to solve the Traveling salesman problem, and all of them have failed, it doesn't make much sense if additional researchers are trying to solve the same problem. .np hard problems can be compared to the perpetuum mobile in mechanical engineering which is also described as a dead end.
On the other hand there is a discipline available which is devoted to np hard problems. This discipline is called Artificial intelligence and its main problem is to solve all these unsolvable problems like rubik's cube, path planning and motion planning. Its important the difference between computer science which is only about polynomial problems vs. Artificial intelligence which is only about np hard problems. Both disciplines are trying to achieve different goals and they are operating with opposite tools.

March 21, 2025

Computer science vs artificial intelligence

AI was created as part of classical computer science, but both disciplines are operating with different assumptions. Computer science is a well defined engineering discipline which is successfully solving real world problems. For example a mathematician has a need for an electronic calculator and the machine is build and programmed by computer scientists. Or a statistician likes to store data in a table and computer scientists are programming a database which can solve this task. The result is, that doing computer science results mostly into successful projects which are evolving over the years, in the sense that todays computer have more memory and can sum up numbers faster.

In contrast, it remains unclear what the goal of Artificial intelligence is. The missing goal is a structural problem and the only way to solve it is to define goals by itself. That means, the AI community has to find objectives including the solution by itself. There is no higher instance available which has a need for robot hand or vision system, but these projects have to be formulated from scratch.
Let me give some example for self created goals only available within AI research: line following robot, robocup rescue, Tetris playing AI software, chess AI, scene recognition in motion capture recording.
All these problems have only a sense within the AI community. In the reality, nobody has a need for a line following robot or a Tetris playing AI. The only reason why these projects are researched is because of the hope for new knowledge what thinking is about. Especially in the past, successful AI research was always connected to a certain project, for example to build a machine which can play chess. A certain problem allows to benchmark an algorithm. Either the computer is able to do so or not.
In contrast, additional computer science has no need for artificial constructed problems. Computer science has no lack in existing well defined problems, but the missing resource is how to solve all these problems with better hardware and improved algorithms. AI is working the opposite way. There are endless amount of hardware and software but a lack of well defined problems.
Its a bit paradox, but AI engineers have a certain understanding about their tools. A tool isn't a computer e.g. a Unix workstation, but a tool is a problem formulation, e.g. the 15 puzzle problem or the traveling salesman problem is a tool. These tools are helping to guide the discussion into a certain direction. For example, the 15 puzzle problem is a great choice for introducing heuristics, which is not available in classical search algorithms.
The second paradox situation is, that AI isn't evolving in terms of better hardware or more intelligent software, but AI is evolving in terms of well formulated problems. In the year 2025 there are more such problems available than 20 years ago. A very recent problem is Visual question answering which was first described in 2015. The VQA challenge has evolved from similar challanges which are text based question answering which was available since 2000s. Its likely, that in 20 years from now, more advanced robotics problems are available unknown today.

March 12, 2025

How to identify Artificial Intelligence technology?

Computer science in the past was operating with certain tools which were developed once and reused frequently. These tools were located in hardware, software and algorithms and are similar to cooking recipe a blueprint how to build technology from scratch. For example, a 32bit microchip is designed with a hardware description language like VHDL, while the Unix operating system is written in the programming language C. If someone has access to the VHDL file or the c source code he can create a copy of the technology because he has a better understanding how the system is working.
A naive assumption is to translate this model towards the Artificial Intelligence domain, which is a subpart of computer science. AI can be located in hardware, software or algorithms, at least under the assumption of the computer science bias. So its natural to ask for dedicated AI hardware and AI software, in the hope to get access to advanced robotics technology.
Unfortunately, AI works quite different from the computer science paradigm. Even if dedicated AI tools were developed, for example AI related FPGA chips and AI related software libraries, there is something missing to build a robot. This something else is unknown and because of inability to locate the magic ingredient, AI wasn't realized over decades. It was unknown how exactly an AI machine or tool has to look like so it was impossible to find such tools.
With more advanced knowledge about the AI subject its possible to describe with more details what the recipe is for creating robots. The missing ingredient is a multimodal dataset. That is a .csv file which contains of motion capture data in combination with natural language annotation like “jump”, “Walk”, “stand up”. Such a dataset can be used as the core element and a robot control system can be developed around this dataset.
This description for an AI tool looks a bit uncommon because it doesn't fit to existing categories in computer science. 'Its not a hardware description, it is not an algorithm and its not the source code for a computer program. But a multi modal dataset is simply a sensor recording, similar to a temperature log file from a weather station. The usefulness will become visible only on a second look. A multimodal dataset creates a machine learning problem. The question is which sort of neural network can learn the data and how to interpolate the missing data. The attempt to answer these questions results into a robotics project.
In contrast to a numerical dataset, a multimodal dataset consists of natural language annotation in the natural language of English. These annotation transform a robot control problem into a text adventure. The textual layer is the birds eye perspective towards a problem. Every robot control problem can be interpreted as a text adventure which consists of nouns, verbs and adjectives. Natural language is the best tool to describe the reality. Let me give an example:
A kitchen robot will perceive objects like table, plate, apple and bread. Also a kitchen robot can do actions like open, close, grasp and transfer. The natural language including its vocabulary is used to identify the parts of the reality. The robot cointrol system has to memorize the same words, otherwise the human to machine interaction will fail.
In classical computer science there is no need to utilize natural language. A pocket calculator and even advanced workstation computers are working fine without knowing any vocabulary. Words from the reality like “apple” “bread” and so forth are never included in the VHDL hardware description and they aren't stored in computer programs. Sometimes these words are available as commentary, but they are not important for running the software itself. In other words, classical computers are working fine without any interaction with a human.
In contrast, a robot control system operates with the opposite paradigm. A robot is mostly a user-interface which is using the same lexicon as a human. Its impossible to build a kitchen robot without 100 and more kitchen words.

March 09, 2025

The guess naming game

 

Its a language game for researching grounded language. The user enters a word like “green” and the computer has to highlight all the objects in the GUI which fits to the word. These game rules allow to benchmark if a certain visual dictionary was learned correctly because the computer has to map the entered textual word to the shown graphical representation on the screen.
The main advantage of the guess naming game is, that it can be realized in under 300 lines of code in Python. It explains on an example what the symbol grounding problem is about.


March 02, 2025

AI as search for a problem

 In the history of AI development it was mostly unclear, what exactly AI is about. In the 1980s a common definition was, that AI has to do with search in the problem space. But this definition fits only to small subsection of AI which is state space search in games. And search algorithms like A* can't be applied to more complex problems.

A possible improved definition is, that AI has to do with search for the problems. This definition shifts the focus away from algorithm centric solving of existing challenges towards an explorative search for new problems.
Example problem might be the 8 puzzle problem, chess puzzles, path finding in a maze, or the VQA problem (visual question answering). The assumption is, that more advanced AI techniques can be unlocked by inventing more advanced puzzles first.
Let me given an example. Suppose the self selected problem to solve is the 8 queens problem. Under such a constraint, the following debate consists of a mathematical problem description, and a comparison of different algorithms which are mostly back tracking algorithms. The decision to solve the 8 queen problem results into a certain bias.
By selecting other problems the discussion space gets modified. Especially by selecting language games like Visual question answering, the debate gets modified drastically. Instead of describe only search based algorithms, new topics have to be discussed like vocabulary definition or grounded language. These new subjects are not available in classic problems like the 8 queen problem.
So we can say, that no hard subjects within computer science like hardware, software, algorithms or programming language are creating the discourse space for Artificial Intelligence, but the debate gets influenced by the preference for certain AI problems. The decision for or against certain problems affects the discovery of possible problem solving techniques. If the attempt is to solve only the 15 puzzle problem, a certain sort of computer program is the result. But if the task is to solve problems with grounded language like the instruction following problem, more advanced algorithms are needed.
The main question which has to answered by the AI community is, what are the advantages and disadvantages of a certain problem category. The traveling salesman problem was popular in the 1980s because it can be described easily and can be converted into an mathematical algorithm. The main disadvantage of this problem is, that even the algorithm has found the shortest route, the same algorithm can't control a real robot because robotics has to be described with different problems.
The holy grail is perhaps an AI problem, which can be described easily but fits to a variety of real world robotics problems. If it matches to the reality, the algorithms used for solving the problem can be adapted to real world scenarios.

A review of AI puzzles

Puzzle problems are the framework in which the search for Artificial Intelligence takes place. The evolution from easier to more advanced understanding of AI is strongly connected to the evolution of AI puzzle problems. There are certain categories available:

  1. easy: traveling salesman problem, chess problems, tictactoe game, Knight's tour, path planning
  2. medium: 15 puzzle problem, piano movers problem, Tetris playing AI agent, pacman AI agent
  3. advanced: dataset driven machine learning
  4. hard: Visual question answering, instruction following, multimodal machine learning
Let us describe first the easy category. This sort of problems has the longest tradition in the AI literature and the problems including possible algorithms are described in high amount of papers. Nearly every introduction course in computer science, mention or explains the traveling salesman problem. It has become a standard challenge for investigating the performance of algorithms.
Most of the problem from the easy category can be solved with a backtracking algorithms. In case of chess problems, the algorithms needs improvement from an evaluation function but the main principle is to traverse the state space.
The problems from the medium section are less often explained and they are requiring sophisticated heuristics. The first puzzle in the section, the “15 puzzle problem” is used exclusively to explain the advantages of an evaluation function, namely the manhattan distance, while the other problems like “pacman AI agent” will need a finite state machine or an advanced behavior tree. Its not possible to solve the problem in the medium category with easy to implement backtracking algorithms because of the larger size of the state space.
The two remaining categories, advanced and hard, are only seldom described in the literature. The first papers about the subjects were published since the year 2000. For VQA related problems, the first written description is even available since 2010.
What all these problem categories have in common is, that they do not require software or hardware in terms of computer science. But the problems can be discussed independently of computer science. A typical problem description fits on a single piece of paper which reads like the description for a board game. So we can say, that the problems belong to the umbrella term of game theory. The complex reality gets simplified into game rules and the player can take decision inside the rule system.

March 01, 2025

Advantages of Linux in a single sentence

ffmpeg, mplayer, python3, gcc, git, vim, grep, awk, wget, ssh, docker, htop, nmap, pandoc, imagemagick, find, dd, ghostscript, latex, gnuplot, sqlite3.

The evolution in AI from 1990 to 2010

In the year 1990 no robotics was available. The only thing what was visible during this decade were classical computing machinery which includes home computers, supercomputers and even the Internet. Until 1990 it was unclear how to program Artificial Intelligence.
On the other hand, from 2010 AI was evolving quickly and many robots were developed and existing models were improved. So the natural question is: what exactly happened in the meantime which enabled AI and robotics?
The surprising situation is, that from a computer science perspective no measurable progress was made since 1990. Even if the amount of RAM in a typical workstation has improved, and even hard drives had become larger, there was no invention available like an AI chip or a revolutionary robot algorithms. Some attempts were made to create dedicated AI programming languages, and even parallel microprocessors were created for running neural networks – but all these innovations didn't resulted in artificial intelligence.
What was created instead is located outside of computer science and it was the discovery of AI related puzzles. Perhaps it makes sense to explain this idea in detail. A puzzle in the classical sense is a thinking game, for example the rubics cube is a 3d cube with random color surfaces, while, while a sliding puzzle like 15 puzzle contains of numbers on cells which can be moved in 2d space. There are word puzzles which are called crossword puzzles and jigsaw picture puzzles.
An AI related puzzle is a certain puzzle which was invented to investigate the subject of robotics and intelligent machines. These AI related puzzles were invented from 1990 to 2010 in a high amount of diversity. There are AI Related puzzles in a physical world which is about mechanical robots and also in a virtual environment which can be realized as video games. The creation of a certain AI related puzzle describes the reality from a different standpoint. This standpoint allows to define what AI is about.
Entry level AI puzzles are the mentioned 15 puzzle problem, chess based puzzles and the traveling salesman problem. None of these puzzles has to do with computer science in the classical sense which includes computer hardware, software or algorithms, but its invented outside of computer science. Computers are only used to solve these problems.
More advanced AI related puzzles are OCR datasets used for neural network training, the visual question answering challenge, and robotics competitions like micromouse and robocup. What all these puzzles have in common is, that they fit on a single sheet of paper. The document constains the instruction what the puzzle is about. For example it consists of a map for a robot navigation task or explains what the rules of the robocup competition are. These instructions can be convertted into a computer science project which includes building of mechanical hardware and programming the robot software. So we can say, that AI puzzĺes are a meta technology which allows to create new AI related technology.
Let me give an example: A single puzzle description for the micromouse robot competition can be used as a starting point for a dozens of concrete projects. Some of the robots are realized with the Arduino microcontroller, while other are using Lego Mindstorms technology. Some of the projects might be programmed in Assembly language or with neural networks or with the C/C++ language. These detail decisions are taken place within classical computer science but they are not important for the AI problem directly. The only thing which counts is the AI puzzle itself.
The advancement in AI from 1990 to 2010 can be entirely explained with the invention of better AI puzzles. Early problems like tictacto or chess puzzles are resulting only in low quality AI projects, while later puzzles like “Mario AI”, deep learning datasets and instruction following problems in robotics are generating more advanced AI innovation. So we can say, that the main question in AI is “What sort of puzzle has to be solved?”