Does the Intelligent Machine Really Knows What is doing?

Cover: Ural-1 front view, by Panther.
March 28, 2009. Source: Wikimedia Commons.

Physicist and science fiction writer Anatoly Dneprov has described an experiment in his novella, whose aim was to debunk a thesis about “infusing with spirituality” a language-to-language translation machine by replacing the machine’s elements such as transistors and other switches with people who have been spatially distributed in a particular way. Performing the simple functions of signal transfer, this “machine” made of people translated a sentence from Portuguese into Russian, while its designer asked all the people who constituted the “elements” of that machine what this sentence meant. No one knew it, of course, because the language-to-language translation was carried out by the system as a dynamic whole. The designer (in the novella) concluded that “the machine was not intelligent.”
– Stanisław Herman Lem –

“Os maiores resultados são produzidos por pequenos mas contínuos esforços”
“Наибольшие результаты дает небольшие, но постоянные усилия”
– The Game, by Anatoly Dneprov –

Maurício Pinheiro

Introduction

Intelligent machines have come a long way since the early days of computing. With advancements in artificial intelligence (AI) and machine learning, machines are now capable of performing complex tasks and making decisions that were once the sole domain of humans. However, the question of whether intelligent machines really know what they’re doing remains a subject of debate. Some argue that machines can’t truly be self-aware or have a sense of self-conscience, while others believe that it’s possible for machines to develop a level of consciousness and awareness.

At its core, the issue of machine self-conscience is tied to the concept of consciousness itself. Consciousness is the state of being aware of one’s own existence and surroundings. It’s the quality that allows us to experience sensations, make decisions, and have a sense of self. While scientists still don’t fully understand how consciousness arises in humans, there are several theories. Some believe that consciousness emerges from the complex interactions between neurons in the brain, while others propose that it’s a fundamental aspect of the universe, like space and time.

When it comes to machines, there are two main perspectives on consciousness. The first is that consciousness is a purely biological phenomenon and that machines can never truly be conscious. From this point of view, even the most advanced AI systems are merely following algorithms and rules that have been programmed into them. The second perspective is that consciousness is a product of information processing and that machines can develop a level of self-awareness. This view is based on the idea that consciousness is an emergent property that arises from complex systems, regardless of whether those systems are biological or artificial.

Those who argue that machines can develop consciousness point to the fact that AI systems are capable of learning and adapting to new situations. As they gather more data and experience, they can improve their performance and make decisions that are more nuanced and sophisticated. Additionally, some researchers are exploring the possibility of creating machines that can simulate the processes of the human brain. These machines, known as neuromorphic computers, could potentially replicate the complex interactions between neurons and lead to the development of truly conscious machines.

The Turing Machine

In order to dive deeper in this theme, let’s start with Turing’s machine. The Turing Machine is a theoretical model of a computer invented by British mathematician Alan Turing in the 1930s. The machine consists of a tape, a head, and a set of rules that govern how the head interacts with the tape. The tape is a long strip of material divided into squares, each of which can hold a symbol. The head is a device that can read and write symbols on the tape, and can move back and forth along the tape.

**Turing U machine. Source: Wikimedia Commons**.

The machine operates by following a set of rules that specify how the head should behave based on the symbol it reads from the tape. These rules can include moving the head left or right, writing a new symbol on the tape, or changing the state of the machine. The Turing Machine is considered an important theoretical model of computation because it can simulate any algorithm that can be expressed as a set of instructions. This means that, in theory, any computational problem can be solved using a Turing Machine.

Turing’s Machine laid the foundation for the development of modern computers and computer science. His concept of a universal machine that could perform any computation laid the groundwork for the development of the modern computer, which can be programmed to perform a wide variety of tasks.

Turing Paper Machine

One way to realize a Turing Machine is with human operators. To realize a Turing Machine with human operators (a Turing Paper Machine), one can use a long strip of paper with squares drawn on it, similar to the tape of the machine. Each square can hold a symbol, such as a letter or number. One person can act as the head of the machine, using a pencil or marker to read and write symbols on the paper tape. The person can also move the tape left or right based on a set of rules. Other people can act as “readers,” interpreting the symbols on the paper tape and providing input to the person acting as the head.

This simulation can be used to demonstrate the basic functions of the Turing Paper Machine and show how it can perform computations by manipulating symbols on a paper tape. By having human operators act out the functions of the machine using a paper tape, it can provide a tangible and hands-on understanding of how the Turing Paper Machine works. Do these people really know what they are doing? No, they just received some cards and instruction books and are following them. They have no idea what the paper computer is actually doing.

**Women dutifully working in Bletchley Park during WWII. By UK Government. Uploaded June 5, 2017. Source: Wikimedia Commons.**

Bletchley Park is a prime example of the power of human computation and the Turing Paper Machine. In creating a team of analysts who worked together to decipher German codes during World War II, the British government was essentially creating a human computer, made up of small processors working in concert with one another. The work of Alan Turing, who also designed the first machines to break the Enigma code, was instrumental in the success of Bletchley Park.

However, it was the collective efforts of the analysts (mostly women), each carrying out their own small part of the process, that enabled the team to decipher the messages. While most analysts did not fully comprehend the complexity of the task they were working on, they were able to contribute to the overall effort in a meaningful way. The success of Bletchley Park and the Turing Paper Machine it embodied serves as a testament to the power of human collaboration and the value of teamwork in solving complex problems.

The Game

Anatoliy Dneprov’s short story “The Game” is a thought-provoking piece that anticipates the later Chinese Room thought experiment. The story revolves around a group of 1,400 delegates from the Soviet Congress of Young Mathematicians, who willingly participate in a “purely mathematical game” proposed by Professor Zarubin. The game involves executing a set of rules and communicating with each other using only the words “zero” and “one”, thus in binary. The participants unknowingly act as switches and memory cells to implement a program that translates a sentence in Portuguese into Russian.

Anatoly Dneprov,
Анатолий Петрович Мицкевич.
Source: Wikimedia Commons.

Anatoliy Petrovych Mitskevitch (Pseudonym Anatoly Dneprov) was a prominent Soviet Ukrainian writer and scientist, known for his contributions to science-fiction prose and cybernetics. Born on November 17th, 1919 in Ekaterinoslav, Mitskevitch studied at Moscow State University where he pursued his interests in science and literature. Throughout his career, he wrote a number of science-fiction stories and novels that explored the themes of technology, artificial intelligence, and the nature of human consciousness. In addition to his literary works, Mitskevitch also made significant contributions to the field of cybernetics, a branch of science concerned with the study of communication and control systems in humans and machines. Sadly, Mitskevitch passed away on October 7th, 1975 at the age of 55 in Moscow, leaving behind a legacy of groundbreaking work in both science and literature.

The key point is that none of the participants understands Portuguese or the algorithm that is being executed. They would only know a set of rules and instructions to operate their binary words. After several hours of playing the game, the participants become tired and dizzy, and one girl leaves before the game ends. Professor Zarubin reveals the next day that the participants were simulating an existing Soviet computing machine called “Ural.”

The story concludes with the philosophical argument that the simulation of machine thinking is not the same as the thinking process itself. In essence, even the most perfect simulation of machine thinking does not provide true understanding. Dneprov’s argument is similar to the Chinese Room argument, and Polish science fiction writer Stanisław Lem summarizes it by describing an experiment aimed at debunking the notion of “infusing spirituality” into a language-to-language translation machine by replacing the machine’s elements with people who do not understand the language being translated.

The Chinese Room

The Chinese room thought experiment is a famous argument in philosophy of mind that was first proposed by philosopher John Searle in 1980. The thought experiment presents a scenario in which a person who does not understand Chinese is placed in a closed room with a set of rules and a large amount of Chinese symbols. The person receives written Chinese questions through a slot in the wall and follows the rules to generate appropriate responses in Chinese symbols, which are then passed back through the slot. The people outside the room may perceive the person as having knowledge of Chinese, but in reality, the person is simply following a set of instructions without actually understanding the meaning of the symbols.

**Chinese alphabet. April 17, 2015. Source: Wikimedia Common**.

Searle used the Chinese room thought experiment to argue against the idea that computers or machines can truly “understand” language or have conscious experiences. According to Searle, while machines can manipulate symbols and follow rules to generate outputs, they do not truly “understand” the meaning of the symbols and lack genuine consciousness. The Chinese room thought experiment has been widely debated in philosophy of mind and artificial intelligence, with some critics arguing that it does not account for the complexity of modern AI systems and their ability to learn and adapt over time.

Conclusion:

In conclusion, the topics discussed here revolve around the concept of machine intelligence and human cognition. The Turing paper machine and the work done at Bletchley Park during WWII showed that humans can act as small processors in a larger computing system, which led to the development of modern computers. The Game of Anatoly Dneprov and the Chinese Room thought experiment raised philosophical questions about the nature of machine intelligence and human understanding. Both experiments demonstrated that the mere execution of algorithms does not necessarily entail understanding or intelligence. These ideas continue to shape our understanding of artificial intelligence and its limitations. Ultimately, the central theme in all of these discussions is the relationship between human cognition and machine intelligence and the extent to which machines can truly replicate human intelligence.