Gründe gegen Linux auf dem Desktop

 Innerhalb der Linux Community gibt es ein breites Unverständnis gegenüber Windows User. Linux wird als das technisch bessere System definiert und folglich wird das Winbdows Ecosystem verspottet und es wird unterstellt dass Windows ein Auslaufmodell wäre.

Anstatt an diesen Gedankengang anzudocken und gebetsmühlenartig zu wiederholen was die Vorteil von Linux auf dem Desktop sind, sollen an dieser Stelle einmal die nachteile von Linux genannt werden mit dem ziel dass Windows Benutzer gestärkt werden.

Der wichtigste Grund gegen Linux ist, dass dort die beliebte Datenkbank-Anwendung MS Access nicht lauffähig ist, es gibt ferner kein Opensource pendant was dem Funktionsumfang nahe kommt. Lediglich für MS Word und MS Excel gibt es etablierte Linux Alternativen, für die Desktop_datenbank fehlte bisher die nötige Manpower um ein Open Source Projekt durchzuführen.

Eine Desktop Datenbank wie MS Access ist ferne nicht irgendeine Büroanwendung ähnlich wie ein texteditor oder ein Malprogramm, sondern mit besagter Software kann man ohne Programmieren zu müssen leistungsfähige Frontend und Backend EDV Anwendungen erstellen. Datenbanken mögen für Physiker und Mathematiker entbehrlich sein die eher Programmiersprachen und Mathematik-Software einsetzen, jedoch sind Datenbanken im Geschäftsumfeld die Brot und Butter Anwendung. Der Computereinsatz von Unternehmen ist ausnahmslos datenbank orientiert. Ohne Übertreibung kann man sagen, dass MS Access die Killerapplikation für Compüuter im Büroumfeld darstellt, fehlt diese Applikation sind alle weiteren Vorteile unwichtig.

Linux hat jedoch noch weitere Nachteile. Zunächst einmal ist es auf Consumer-PC nicht vorinstalliert. Wer einen neuen PC im Handel kauft wird dort lediglich Windows 11 vorinstalliert vorfinden. Technisch kann man das Problem natürlich mit einem selbst erstellten USB Bootstick lösen, jedoch wird der Umsteiger schnell erkennen, dass es im PC Fachgeschäft für Linux generell keine Software verfügbar ist. Sämtliche kommerzielle Software von Softwarefirmen die an Privat und LBusinesskunden verkauft wird, wurde dezidiert für das MS Windows Betriebssystem entwickelt.

es gab früher einmal auch kommerzielle Linux software die im Fachladen als Package verkauft wurde, jedoch hat sich das konzept nie durchsetzen können. Die PC Welt ist deshalb zweigeteilt: es gibt einmal kommerzielle Software entwickelt von Firmen für das Windows system und dann gibt es noch Open Source software die es nur online im Internet gibt, die aber nicht kommerziell vermarktet wird. Dadurch stehen normale 'Anwender die auf Linux setzen, plötzlich allein da. Sie müssen sich das nötige Fachwissen mühsam anlesen, sie müssen die Software online herunterladen und wenn es dabei Probleme gibt erhalten sie in Foren den Rat doch einfach auf eine andere Linux Distribution zu wechseln, weil die angeblich viel besser wäre.

Für den Durchschnittsanwender, der einen PC mit vorinstallierten Windows verwendet gibt es keinen Grund auf Linux zu wechseln. Er wird dort nur Nachteile erleben, und wegen der steilen Lernkurve an einfachsten Aufgaben scheitern wie der Installation eines Spieles oder dem Öffnen eines Word Dokumentes. MS Windows ist in sämtlichen Punkten überlegen und bildet unverändert den Industriestandard für Desktop PC sowohl bei Privatanwendern als auch in der Geschäftswelt.

The information layer in the DIKW pyramid

The lowest layer in the DIKW pyramid is the data layer which can be desribed easily. There are raw sensor data like distance, temperature, gps coordinates which are stored in a numerical format. The next layer in the pyramid, the information layer, is harder to describe. A working thesis is, that the information layer consists of [tags].

For the example of a warehouse robot, the tag cloud would be: [roomA, roomB, roomC, shelfNorth, shelfsouth, shelf1, shelf2, obstacle, battery, chargingstation, barcode, path, left, right, speed, direction, batteryempty, order]

Of course the tag list is not complete, there are additional tags available but for reason of simplication this might be a starting point. These tags are providing context because after selecting one tag, possible alternative tags are not activated. For example, the goal for the robot might be [roomB] but not [roomA, roomC]. The robot might rotate to [left] but not to [right]. So the context of a tag are always the tag which might be possible but are not activated at the moment.

All the tags are creating a semantic network. In contrast to a full blown ontology or AI frames, a tag based information is more minimalist. Every tag can be activated or not similiar to the tags in a blogging post for annotating a document.

The interesting situation is, that there is an intersection available between low level sensor data and mid level tagging cloud. For example:

- gps sensor -> [roomA]
- gps sensor -> [direction]
- distance sensor -> [obstacle]

For desribing the robot's behavior both layers (data and information) are important. The robot needs to log the numerical raw sensor data and also the robot needs to annotate the current sensory perception with semantic information.

What we can say for sure is, that tagging information doesn't belong to the lowest data layer. A sensor like a gps sensor has no builtin tagging mechanism. The sensor doesn't know the position of a certain shelf, or doesn't know if the robot is in roomA or in roomB. What the gps sensor knows instead are precise x/y coordinates. The reason is, that the sensor hardware is able to generate such data. Its up to a higher instance in the DIKW pyramid to process these data.

Quiz: The DIKW Pyramid (Data to Information)

The symbol grounding problem can be explained with a DIKW pyramid. The following quiz has the domain of a warehouse robot.

Instructions: Match the raw sensor output (Data) to the correct operational meaning (Information).

1. A Time-of-Flight (ToF) sensor returns the integer 20. The system context defines the unit as centimeters. What is the information?

    A) The robot's battery is at 20%.
    B) An object is detected 20 cm in front of the sensor.
    C) The robot has picked up 20 items.
    D) The robot is moving at 20 km/h.

2. The internal IMU (Inertial Measurement Unit) registers a sudden spike of 9.8 m/s² on the X-axis while the robot was supposed to be stationary. What is the information?

    A) The robot is successfully charging.
    B) The robot has reached its top speed.
    C) The robot has likely been struck or tilted unexpectedly.
    D) The floor is perfectly level.

3. A barcode scanner on the gripper returns the string SKU-9921-RED. What is the information?

    A) The gripper is currently empty.
    B) The robot needs to be rebooted.
    C) The item currently held is identified as a "Red Small Widget."
    D) The ambient light in the warehouse is too red.

4. The wheel encoders report 500 rotations, while the visual odometry (camera) reports 0 meters of forward progress. What is the information?

    A) The robot is moving faster than expected.
    B) The wheels are slipping on a slick surface (e.g., oil or plastic).
    C) The robot has arrived at the loading dock.
    D) The camera lens is dirty.

5. A thermistor near the motor controller reads 95°C. The operating limit is 80°C. What is the information?

    A) The motor is warming up to optimal temperature.
    B) The warehouse heating system is turned on.
    C) The motor controller is overheating and at risk of failure.
    D) The robot is in a refrigerated zone.

6. A pressure-sensitive safety bumper sends a High/1 logic signal to the CPU. What is the information?

    A) The robot is clear of all obstacles.
    B) The robot has made physical contact with an object or person.
    C) The battery voltage is stable.
    D) A new software update is available.

7. The battery management system (BMS) reports a voltage of 19.2V on a 24V rated system. What is the information?

    A) The battery is fully charged.
    B) the battery is at a "Low" state and requires docking soon.
    C) The robot is currently plugged into a wall outlet.
    D) The sensors are calibrated correctly.

8. An acoustic sensor detects a frequency of 110 dB during a lifting sequence. What is the information?

    A) The lifting mechanism is operating silently.
    B) The warehouse music is too loud.
    C) There is an abnormally loud grinding noise in the lift gears.
    D) The item being lifted is very light.

Correct answers

    1. B (The number 20 + unit cm = distance information)
    2. C (Sudden acceleration on a stationary axis = impact/tilt information)
    3. C (String data + database lookup = object identity information)
    4. B (Rotation data vs. no movement data = traction loss information)
    5. C (Raw temperature > threshold = critical status information)
    6. B (Binary signal from a bumper = collision detection information)
    7. B (Voltage level compared to system rating = energy status information)
    8. C (Decibel level + operational context = mechanical fault information)

Language as ghost in the machine

The term "Ghost in the machine" is usually referencing to artificial intelligence which allows a robot to do useful things. The body is the robot's hardware and therefor the software is soul realized in software. With such an understanding, Artificial Intelligence is an advanced computer program based on AI related algorithms.

But what is if this working thesis is wrong? The assumption is, that AI can't be realized in hardware and also not in software. But Artificial intelligence is similar to a ghost very hard to identify. Its basically natural language. In other words, the ghost is an English dictionary. Its not stored in the software itself, because language is a communication pattern used as in between technology.

Natural language will become only visible if a human speaks with a robot. The human formulates a request like "move forward" and the robot responds to this request. Therefor, the AI isn't stored in the robot itself, but its located in air between human and robot. This would explain why it has much in common with a ghost which also has no measurable location in the reality. But a ghost is located beyond, which is the environment of the reality, or some sort of hyperspace.

Let us try to describe language from a more scientific perspective. A statement like "grasp the apple, please" has no physical size. Words are not part of the visible reality, but they are abstract symbols located in the oral space. Despite this missing physical borders, language is part of the reality because language is used for many purposes. In a mathematical sense, language is a communication technology used in a sender to receiver interaction. Such a protocol has much in common with a ghost like behavior. If language gets submitted over radio waves it has some similarity to a supernatural phenomena.

What makes language interesting for artificial intelligence is, that without language a robot is not able to think. Without language, robots are reduced to a pocket calculator which can execute an algorithms but isn't understanding the meaning of objects in the reality like a table, an apple or a plate. The ability to parse natural language is equal to implement artificial intelligence.

Playing pong videogame with a perception buffer

 

In addition to the previous post here is another example with a working AI based on a buffer. The game has two modes: normal mode which runs the simulation and a pause mode which shows AI information including the perception buffer, the action buffer and the predicted trajectory. The text box shows the known information from the game engine which are the ball position, its velocity and other information. the action buffer stores information what to do next. This information is submitted to the paddle.

Because of the simplicity of the pong videogame, the AI master the challenge with ease, it will move the paddle towards the correct position.

From a technical perspective the buffer was realized with a python dict for storing the information in a key/value syntax. Creating such a dictionary and showing the content on the screen is very simple. The innovation has to do with the assumption that such a buffer modulates the communication process. The AI brain isn't imagined as a sophisticated algorithm, but its a database which holds information as natural language. This will generate multiple subtasks like a) how to convert the game state into a perception buffer b) how to translate the perception buffer into the next action and c) how to submit the content of the action buffer back to the game engine. 

Computer programming vs. AI programming

 Computer programming is the art of software creation. It has to do converting a real world problem into executable program code like Java or C/C++- A typical example is to program a pong videogame, or improve a database management system.

Modern computer programming since the 2010s does't reinvents the wheel but its using existing operating systems, programming languages and libraries. For example videogames are written with the help of a 2d game library, and database systems are created on top of existing SQL databases.

Programming has always the goal of creating software and modify existing software which is running on a computer. All the modern technology like the Internet, word processing software, and database software is the result of well engineered software applications.

Despite the importance of programming in computer science the discipline has a blind spot because its not possible to program an AI software or write a software for a robot. Many attempts in writting robot software in C/C++ and Java were presented in the past, but most of them have to be called a failure. It seems, that artificial intelligence is working different from classical software engineering principle. Its not possible to reuse existing software libraries or take advantage of existing programming languages. Even the most powerful programming language avaialble which is Python in combination with the latest mathematical libraries is useless for realizing a robot project. The reason is, that software programming describes the world as computer centric. The attention is always directed toward a computer and towards its ability to execute a software. For example the Python interpreter provides a list of commands. Programming means to arrange these commands to a fixed structure which is a computer program, namely in classes in subroutines. Then the program canb e exucuted. The problem is that such a program won't realize artificial intelligence.

There is a single programming excersise available which demonstrates the transition from classical software programming towards artificial intelligence which is activitity recognition in motion capture. This specialized problem has its roots in computer animation and was first mentioned in the 1970s. The task is to annoate the movements of the mocap markers with textual names like sitting, jumping, walking and so forth.

Computer programming is focussed on the CPU of a computer. The computer has to solve a problem, e.g. adding two numbers or search in a database with a search algorithms. In contrast, the activity recogntion task works with a communiation paradigm similar to an internet protocol. The idea is to convert low level data into high level data. Such a communication system is an open system, which is seldom described in the programming literature. The reason is that communication is referenced to external parties located outside of a computer. 

Classical programming works with the algorithm paradigm as a theoretical understanding. The algorithm is executed on the machine and solves a problem. In contrast, communciation oriented programming works with the sender to receiver paradigm. There is no algorithms needed but there is a message which is delivered over the network. Programming a robot is similar to implementing a communication protocol, there is also a sender, a receiver, a message and a protocol. And the robot never runs an algorithm, but the robot receives a message.

Roboter steuerung mit der DIKW Pyramide

Obwohl die DIKW Pyramide in der Literatur häufig diskutiert wird, ist ihre Anwendungsmöglichkeit innerhalb der Robotik nur selten dokumentiert. Als motivierenden Einstieg hier ein Beispiel für einen Warehouse roboter. Auf der untersten Ebene (Daten) fallen folgende Messwerte an:

- Geokoordinaten: "X: 194.5 / Y: 10.2"
- Prozentwert: "12%"
- Barcode scan: "ID: 00056789"
- Temperatur eines Servomotors: "42°C"
- Geschwindigkeit: "0.2 m/s"
- Sensor Schaltzustand: "Bit 1 = On" 
- Zeitstempel: "12.05.2025 / 10:02:01"

Diese Daten sind Rohdaten wie sie von Sensoren ermittelt werden, also über gps triangulation, barcode reader oder von einem Temperatursensor.  Eine tiefergehende Bedeutung haben diese Daten nicht, sondern sie werden nur mitgeloggt und in einer Datenbank als numerische Werte gespeichert.

Zur Steuerung des Roboters interessanter ist die nächste höhere Ebene der DIKW Python: Information.

- Batterieladezustand ist gering, bezug zu 12%
- Standort des Roboters ist Regel 8, Fach A. Bezug zu Geokoordinaten
- Paket mit 00056789 ist eine Palette mit Glasflaschen, Bezug zu Barcode scan
- Motorüberhitzung droht, Bezug zu 42°C

Die Zuordnung von Daten zu Information erfolgt mit Hilfe von weiteren Datenbankeinträgen. Darin sind Textinträge gespeichert wie "Motorüberhitzung droht" und Bedingungen wann diese zutreffen. Die Informationsebene ist nicht als numerische Daten gespeichert sondern besteht aus kurzen Sätzen in natürlicher Sprache. Höhere Ebene in der DIKW Pyramide beinhalten abstraktere Formulierungen die Expertenwissen beinhalten und für die Aufgabe des Roboters wichtig sind.

Technisch gesehen ist eine DIKW Pyramide ein DAtenbank-MAnagement system, worin die Daten/Informationen auf unterschiedlichen Tabellen verteilt sind und über Regeln zusammengefügt werden. Der Inhalt der Datenbank wird in Echtzeit aktualisiert. Auf der höchsten Ebene (Wisdom) ist die Steuerung des Roboter dann sehr simpel. Man sendet einen natürlich-sprachlichen Befehl wie "Fahre zum Regel C und hole die Glasflaschen und bringe sie zu Regel B". Dieses High level Kommando wird dann übersetzt in konkreten Befehle an den Roboter.

Robot control with a DIKW pyramid

Symbol grounding is about moving down and moving up along a dikw pyramid. This allows to hide the details and expand the details of a subject. For the example of a warehouse robot the dikw pyramid can be implemented as a python dictionary which shows only the upper layer and the bottom layer:

dikw_pyramid={
  "wisdom": {
    "Go to the loading bay and clear the blockage.",
  },
  "data": { 
    "lidar_dist": 0.5, "weight_kg": 25.0, "coords": (12.4, 45.8)
  },
}

The raw sensor data are feed into the data layer and are formmated as numerical values. In contrast the wisdom layer of the pyramid stores the voice commands formulated in English sentences. The task for the symbol grounding engine is to translate between these layers. This is realized by instruction following (from top to bottom) and activitity recognition (from bottom to top).