Rethinking Artificial intelligence with buffers

In the past it was assumed that Artificial Intelligence is an algorithm, for example a recursive genetic algorithm which improves itself, or a mathematical optimization algorithm for model predictive control. Such a bias organizes the research into a certain direction and ignores possible alternatives.

The opposite bias is shown in the picture which consists of a buffer who connects two systems. There is no Turing machine anymore needed which executes an algorithm, but they are separate systems who are communicating with each other. The buffer is some sort of traffic router in a computer network and is the source of artificial intelligence. The router ensures that both systems are using the same protocol, namely grounded language.

Even if this definition is vague and nothing but a picture, it allows to treat artificial intelligence with a new perspective. Classical algorithm theory can be ignored and signal processing and linguistics becomes a greater importance. The goal is to use the buffer pattern as a starting point for all the future AI research, including robotics, computer vision and planning.

Artificial intelligence with oracle turing machines

 Classical turing machines are executing algorithms, therefor the artifical intelligence must be located within an algorithm. There is an extensive list available of all possible algorithm but none of them is providing AI.[1]

There are some algorithms available which are mentioned in the context of AI like automated planning, Mathematical optimization and neural networks, but its not possible to take one algorithm from the list and use it for robot control.

What is needed instead is an opposite computional model different from a turing machine called an oracle turing machine. Even if the mathematical background of such a Super Turing machine is very complex, the principle can be explained as a Turing machine which communicates with an external system. This ability to communicates allows to offloadwing Artificial intelligence.

For robotics application, an oracle turing machine is usally implemented as a teleoperated robot. The robot stops in front of an obstacle and asks the oracle what to do next. The oracle is the human operator who decides that the robot needs to move around the obstacle on the left pathway. This command is executed by the robot.

In contrast to a normal turing machine, an oracle turing machine doesn't process an algorithm but it communicates. Communication means to solve problem by asking someone else outside of the own system. The higher instance is better informated about the situation, a human operator is equipped with a powerful vision system and has a lot of knowledge to solve most robotics problems. Such kind of knowledge is hard to program into an algorithm, so the robot needs to ask the operator for help.

There is a detail problem available in oracle turing machines which is the communication protocol. The turing machine and the oracle need to established a shared communication protocol which allows them to receive and submit messages in a language. This language needs to be invented first.

[1] https://en.wikipedia.org/wiki/List_of_algorithms

Offloading of Artificial intelligence

 AI research in the past was mostly a failure. All the programming languages, projects, neural networks, expert systems and algorithms didn't work. Even if handbooks available like "Russel/norvig: AIMA" these books doesn't contain valuable information but they are collected wrongt theories.

To overcome all the chaos a new paradigm needed, which can be coined as Offloading of Artificial intelligence. The idea is to reduce the robot's control software to the minimum so that its only a receiver for external commands. And the intelligence, the vision system, the algorithms and so on are offloaded to an external entity which is a human operator. The human perceives the scene with its eyes, uses its domain knowledge, takes a decision and then presses a joystick. The signal is send to the robot who is moving the servo motor.

The main advantage of such a minimal robot is its simplicity. There is no need to implement advanced AI algorithms, or write complex software systems but the robot is no longer responsible for the task.

Such kind of teleoperated robot works with two important principles: a) existing theories from mathematics, computer science and psychology are no longer valid b) the only open question is how to design the human to robot communication interface.  For example the signals can be transmitted with a cable, wireless, with a joystick, with speech or with a text interface.

In classical robotics in the past, the bias was that the intelligence inside the robot. The robot consists of a microcntroller, the microcntroller runs a software, the software executes an algorithm and the algorithm consists of artificial inteliigence. In the new paradigm "offloading AI" the robot is reduced to a non thinking device similar to a RC car which receives commands from an external source, similar to a Super turing machine which recieves commands from the oracle. Such a mental short cut allows to explain what intelligence is: intelligence is a signal from the environment. Innstead of generating intelligence inside the robot, the robot needs to receive and interpret the signal.

Offloading intelligence means, that there is a physical distance between the source and the target. The source of intelligence is the human in one location, while the receiver is the robot in the other location. Between them there is a cable. Such a distributed robot system will create a new problem which is how to submit the signal from the source to the target. By answering this question its possible to get a better understanding of intelligence. It transforms a closed system into an open system. The attention gets moved away from the robot itself towards the cable between human and robot.

The failure of AI related programming language

The promise of 5th generation programming language was to formulate AI related problems on a higher abstraction level. Examples like Prolog, Domain specific language and robot control APIs were invented to simplify the programming. Unfurtunately the concept was never accepted in the reality. The reason is, that a programming language is targetted towards the internal behavior of a robot or a computer, and the internal system has no knowledge about the external world.

Let me give an example. Suppose a domain specific language for robot control is invented which consists of statements like:

robot.move()
robot.stop()
robot.chargebattery()
robot.robotpickup()

In theory this DSL sounds logical the problem is to parse such a language with a computer program. A statement like robot.move() can't be converted into low level actions. Its only a mock up without executable programming code. The reason has to do with the difference between internal structure of a system and external environment. The statement "robot.move()" makes only sense if there is a simulation in which the robot can move, rotate and stop. In a normal robot program written from scratch there is no such simulation available, but the memory aka the RAM of a computer program is empty.

Programming language for high level robot control doesn't work, because a programming language is the wrong tool for such a purpose. Programming language like C, Java, Rust or Python are great for technical implementation of ideas but they can't generate artificial intelligence. What is needed is not a programming language but a communication protocol similar to GUI interface. Typical examples for such an interface are the Maniac Mansion verbs shown on the bottom or the vocabulary of a text adventure. These interfaces are not realized as programming language but they are widgets on the screen created for human to machine interaction.

Of course, there is a need to write a computer program which checks if the user is moving the mouse over a verb and presses the button. But such kind of program can be formulated in classical programming language like C/C++ or Python, Because the task for the program is very low level and has to do with recognizing the mouse position and display text on the screen. These tasks have to do with computer programming the core sense because the program defines how to blit pixels to the screen and which sort of game loop is reacting to the user input.

Closed systems without symbol grounding

Before the advent of human to machine communication there was a different paradigm avaialble which is a closed sytem. A robot was imagined as a self-sufficient system which never receives or submits information to the environment but operates by its own logic realized in software and hardware. Typical questions for such a closed system are:

- how fast is the microcontroller in terms of RAM and Mhz
- which programming languages was used, e.g. Lisp, C/C++
- Is the microcntroller running with 5 Volt or 12 Volt
- how many lines of code has the robot control system
- which mathematical algorithm was implemented in the software
- what is the CPU consumption and the runtime of the algorithm
- is the robot using an inverse kinematics solver
- was a genetic algorithm used

These questions are asked from a classical computer science perspective which includes a mathematical, physical and computer based understanding of robotics. Its about the internal structure of a robot and ignores the environment of the system. Instead of analyzing the task e.g. a warehouse logistics problem or a kitchen cooking problem, the focus is put on the machine itself and their hard- and software.

The main property of a closed system is its inability to communicate with the environment. Its assumed that no information, energy or matter can pass between the robot and the outside world. Possible interaction like teleoperation are ignored or reframed as anti-pattern. For example a typical assumption was, that teleoperation is the opposite of automation and therefor its not needed in robotics.

Even if the robot is working by technical meaning the robot can't solve a certain complex problem because closed systems are only able to manage repeating tasks but fail in more complex applications. Artificial intelligence problems like robot control are exclusively complex task which requires a huge amount of communication from a system with its environment.

The success of Artificial Intelligence since 2020

 The acceptance of new technology is measured with the user count. In the 1980s expert systems and robotics was available but nobody was using it. These software and hardware was only known in dedicated AI labs.

In contrast large language models available since 2020 are used by large amount of users world wide. Here is a timeline with the estimated number of users:

2020, <1 million
2021, 5 million
2022, 120 million
2023, 500 million
2024, 1200 million
2025, 2100 million
2026, 3500 million

Symbol grounding and the art of communication

For decades from 1950 until 2010 it was an untold bias that Artificial Intelligence gets realized as a closed system. This bias was formulated in an explicit fashion by the artificial life community in the 1990s. A simulated ecosystem gets populated with cellular automata which are evolving by itself into more advanced creates. The goal was, that with more processing power and genetic algorithm it would be possible to create intelligent computer code.

It should be mentioned that all the projects like cellular automaton, self evolving robots and computer simulation have failed. Its not possible to create intelligent structures this way. The reason for this failure wasn't recognized because it has to do with the untold bias of a closed system.

Cellular automaton are imagined as algorithm driven non communicative systems. They are not exchange matter, energy or information with the environment but they exist in a sandbox which acts as a impenetrable wall.

In contrast open system are able to exchange information, energy and matter with the environment. In case of robots this is equal to teleoperation. The robot receives a command from the operator and submits back status information. Such kind of information exchange was missing in artificial life projects and its the reason why the projects are a dead end.

in 1990 the Canadian cognitive scientist Stevan Harnad published a paper in which he introduces the grounding problem. Grounded language is important for communication systems, and such systems are always open systems. Instead of constructing a sandbox in which the AI can evolve by itself, there are communication layers between a robot and the environment which are described by symbols in a mini language.

Computer systems can be categorized into algorithm driven closed system, so called Turing machines, and communication driven open systems which are Choice Machines or interactive machines. The internet is a typical example for an open system based on the TCP/IP layered protocol. The same principle allows to create intelligent robots.

With this longer introduction its possible to explain why AI has failed in the past. The assumption was that intelligence is an algorithm which is running in a closed sytem. The improved understanding formulated by Harnad et. al. is that intelligence is communication needed for human to machine interaction.

Let us go back to cellular automaton and closed system because it allows to recognize a dead end in academic research. Cellular automaton were introduced by John von Neumann as demonstration for a self replicating system. A cellular automaton is a computer algorithm which is running on a 2d grid. The algorithm never communicates with the environment, its not a "Karel the robot" playground but a cellular automaton follows its own rule. The hope was that with the correct starting condition plus an evolutionary algorithm it would be possible to generate life within a computer, similar how biological life has evolved.

Stephen Wolfram has improved the idea into 1d cellular automaton which are working the same way like a Turing machine. His contribution was that such 1d automaton are described from a strict mathematical perspective. Such a rigid mathematical closed system is unable to communicate with the environment, therefor its the opposite of artificial intelligence in a modern understanding.

The good news is, that the Artificial life community has shown that their approach is a dead end. There is no need to explore the approach with more effort but the assumption itself of algorithms running inside a closed system can't generate intelligence. 

From turing machines to artificial intelligence

 A turing machine is a closed system which executes an internal algorithm but doesn't receives sensory data. This restriction allows to describe turing machines in an elegant mathematical fashion and makes it easy to implement turing machines in hardware. Unfurtunately, the inability of turing machines to receives external input for example from a human or an oracle, will prevent artificial intelligence because closed systems can only solve well definied routine problems but fail in advanced creative subjects.

Oracle turing machines or choice machines have the ability to process external information. Realizing such an advanced turing machine in software is surprisingly easy. A robot may stop on a junction and ask the human operator with a multiple choice drop down field what the robot should do next. The human operator can select "move left" or "move right". The selection of the human is submitted to the robot as external guidance and the robot can continoue his mission.

Such kind of robots are sometimes called an open system because the robot communicates with the environment. Instead of executing a predefined program the robot interacts with a higher instance. Such kind of interactive computing can't be described in classical algorithm terms anymore but the man to machine communication is at foremost a communication process from a sender to a receiver.

Modern artificial intelligence is dealing exclusively with open system which are able to communiate but not with closed system which can execute only a predefined algorithm. Open systems are more powerful but also harder to describe because of the mentioned communication language with the external world.