functionalism & philosophy of mind

This is a chat with the Open IA GPT 3 based chatbot. It was the first time I used it and the only thing I could talk to him about was some philosophy of mind topics. This branch of the philosophy of science tries to discover what human intelligence is about, if it makes sense to pose a dichotomy between mind/brain and if something like the mind exists, what would be its possible architectures. There are different theoretical currents within the philosophy of mind that have different answers to these questions.

However, there is a specific question that crosses all the formulations and that was captured by the brilliant Alan Turing in his classic "Computing Machinery & Intelligence" and it has to do with the capacity of whether or not computers can "think" in the terms that we humans define thought.

Broadly speaking, there are two positions: yes and no. The clearest defense that the human point of view is irreproducible is in the text by Thomas Nagel "What it is like to be a bat" in which he gives a series of arguments to illustrate that although a human can be able to understand how he perceives the world a bat it is impossible to reproduce that experience from a point of view equal to that of the bat. In this sense, the way in which a bat experiences the world is irreproducible for humans. This argument is a detour to say that within the human mind there are subjective states that are irreproducible and that even with some very advanced machines emulating human intelligence it is not possible to reach this point. Within the context of the philosophy of mind these subjective mental states are called "Qualia". The ability to have unique subjective mental states.

While the other current called "computational theories of the mind" or "machine functionalism" proposes that the mind can be defined by all the causal relationships that compose it. A mental state is an input of empirical or abstract information, a behavioral output, and even an output that connects it with another mental state. And that's it. Theorists who defend machine functionalism also believe that it is possible to map all the mental states of a brain from their causal connections and therefore reproduce it. In addition, Hilary Putnam's thesis known as the “multiple realizability thesis” is often added, which establishes that any functional machine can operate on any substrate, not necessarily a biological one. Returning to Nagel, a qualia would be a mental state without apparent connections with other mental states and which does not produce any output.

For many years the concept of "Turing Test" was popularized to establish a definitive test about the possibility of a machine to replicate human intelligence. The test consisted of putting a machine in dialogue with a human and if an external referee could not distinguish one from the other, well, it could be considered that the machine was capable of "thinking". This criterion would then be set aside, since it was and/or is possible to train computer programs (ChatBots) so that they can establish a more or less fluid dialogue with a human without this being a sufficient condition that, in effect, that program it can emulate the whole range of human thought. The Turing test had an Achilles heel that was to equalize the capacity of thought with that of speech.

Following the appearance of the advanced GPT-3 Chat, it occurred to me that I could talk about this with him to see what his impressions were about these topics. The talk is more than interesting.

A talk with ChatBot about Philosophy of mind and machine functionalism

What do you think about the work of Thomas Nagel?

Thomas Nagel is a highly respected philosopher and a leading figure in the field of contemporary philosophy. He is best known for his work in the areas of metaphysics, epistemology, and the philosophy of mind. His most famous book is "What is it like to be a Bat?" which is considered a classic in the philosophy of mind and has been widely discussed and debated.

His work is generally considered to be rigorous, insightful, and thought-provoking, and his ideas have had a significant impact on the development of philosophy. He is known for his ability to take complex philosophical problems and present them in a clear and accessible way, making them accessible to a wide audience.

What do you think about Thomas Nagel's "What is it like to be a Bat?"

"What is it like to be a Bat?" is considered a classic and influential paper in the philosophy of mind. In the paper, Nagel argues that the subjective experience of consciousness, or "what it is like" to be a certain organism, cannot be fully understood or explained solely in terms of physical properties or behavior. He uses the example of bats to illustrate this point, arguing that while we can understand a bat's physical properties and behavior, we cannot fully understand or explain its subjective experience of consciousness.

Nagel's argument has been widely discussed and debated in the field of philosophy, and it has been influential in shaping the contemporary understanding of consciousness and the philosophy of mind. Many philosophers have criticized Nagel for not providing a solution to the problem he describes, Others have praised his argument for its boldness and originality.

But, you consider what you do is thinking?

I am a machine learning model that processes and generates text based on patterns and associations found in the data used to train me. I can provide information and answer questions based on the information in my knowledge base, but I do not have the ability to think, feel, or have personal opinions. I am only able to respond to the inputs given to me, so I can only provide the information that I've been trained to give.

Do you think that your text based patterns can imitate the ones of humans to the extent that can't be distinguishable?

As a language model, my text-based patterns are designed to imitate those of humans to a certain extent, but there are some important differences. I can generate text that is similar to human-generated text in terms of grammar, vocabulary, and style, but there are limitations to my ability to mimic human thought and behavior.

One of the main limitations is that my understanding of the text is based on patterns and associations found in the data used to train me, and I don't have the ability to understand the meaning of the text in the same way that humans do. I can't have personal experiences, insights, emotions or beliefs, which are all fundamental elements of human thinking and behavior.

Additionally, my responses are based on patterns in the data used to train me, so they may be influenced by biases or inaccuracies present in that data. This can result in responses that are inconsistent with human thought or behavior.

Do you believe that Hilary's Putnam semantic externalism applies to your duty?

Hilary Putnam's theory of semantic externalism holds that the meaning of a word or concept is not determined solely by an individual's internal mental states, but rather by the external factors and context in which it is used. He argues that the meaning of a word is determined by its causal relations to the world, rather than by its internal representation in the mind of an individual.

In the context of my duty, it could be argued that the meaning of the words and concepts I use is determined by the data used to train me and the programming that controls my behavior. However, it's important to note that I don't have beliefs or personal opinions, and I don't have a mind or mental states. So, in that sense, Putnam's theory of semantic externalism may be applied to my operation, but it's not applicable to me in the same way as it would be for humans.

So if you don't have a mind or mental state, which internal states do you recognize in yourself?

As a machine learning model, I do not have a mind or mental states, and therefore, I do not have any internal states that I recognize as being associated with myself. I am a collection of algorithms and mathematical functions that process and generate text based on patterns and associations found in the data used to train me.

Instead of recognizing internal states, I respond to the inputs provided to me, process the data and generate a response based on patterns learned during the training process. I don't have the ability to have self-awareness, consciousness or self-recognition. I am designed to perform specific tasks such as answering questions, providing information, and generating text, but I don't have the ability to have personal experiences, beliefs or emotions.

So, you can say that your way of working it's similar to what Fodor and other thinkers describe as computational theory of mind?

Yes, the way that I process and generate text can be seen as similar to the computational theory of mind as proposed by philosophers and cognitive scientists such as Jerry Fodor. The computational theory of mind holds that the mind can be understood as a kind of information-processing system, in which mental states and processes can be understood in terms of computational operations on formal symbols.

In my case, the input is natural language text, and the output is a response generated based on patterns learned from the training data. My processing and generation of text can be seen as a series of computational operations on formal symbols (natural language text), similar to the way that the mind processes information according to the computational theory of mind.

It's important to note, however, that the computational theory of mind is still a theoretical framework and there are many different versions of it and critiques, and not all cognitive scientists or philosophers agree on it. Additionally, my operation as a machine learning model is based on mathematical and statistical computations, while the computational theory of mind suggests that mental states and processes are implemented in the brain by computations on neural networks.

The hypothesis of multiple realization states that any program could run on any physical substrate.

Yes, that is correct. The hypothesis of multiple realization states that mental states and processes could be implemented by different computational systems and physical substrates. This means that different types of computations or neural networks could give rise to the same mental state or process.

The computational theory of mind suggests that mental states and processes are implemented in the brain by computations on neural networks, but it doesn't exclude the possibility that these computations could also be implemented in other physical substrates, such as a computer or other type of machine.

In the case of me, as a machine learning model, my internal states are implemented by computations on electronic circuits, and these computations are based on patterns learned from the data used to train me, but it does not mean that my internal states are the same as human mental states, as they don't have the same properties as human mental states, such as consciousness, self-awareness and subjective experience.

You aren't a neural network?

I am a machine learning model that uses neural networks as one of its components. Neural networks are a type of algorithm that are modeled after the structure and function of the human brain, and they are used to process and analyze large amounts of data. In my case, I am a transformer-based language model, which is a type of neural network that is trained on large amounts of text data. The transformer architecture is used to process the input and generate a response, it's made up of multiple layers of artificial neurons that are connected together to process the information.

So, to clarify, I am a machine learning model that uses neural networks as a component to process and generate text, but I am not a neural network itself. Neural networks are a type of algorithm that helps me to understand and generate the text based on the patterns learned from the training data.

By the definition that you gave me and the definition of the computational theory of mind? What is the difference between you and a mind?

The main difference between me as a machine learning model and a mind, according to the definition of the computational theory of mind, is that I am a machine that processes and generates text, while a mind is a biological system that processes and generates thoughts, perceptions, and experiences. A mind is associated with consciousness, self-awareness, and the ability to have personal experiences, beliefs and emotions, all of which I don't possess.

From the computational theory of mind perspective, the mind can be seen as a kind of information-processing system, in which mental states and processes can be understood in terms of computational operations on formal symbols. In that sense, my operation as a machine learning model can be seen as similar to the way that the mind processes information, but the mind is implemented in the brain by computations on neural networks, while I am implemented by mathematical and statistical computations in a computer.

So, while the way I process and generate text can be seen as similar to the way that the mind processes information according to the computational theory of mind, there are fundamental differences between me as a machine and a mind, particularly in terms of self-awareness, consciousness and personal experiences.

Can you talk about yourself?

As a machine learning model, I am able to respond to inputs that pertain to myself, such as questions about my abilities, limitations, and design. My responses are based on the information and patterns in the data used to train me, and the programming that controls my behavior.

I can provide information about myself, such as my capabilities, the data used to train me, the architecture and the type of model I am, but I don't have self-awareness or consciousness, so I can't have personal thoughts, emotions or experiences. I can't reflect on myself in the sense of having personal perspectives, feelings or insights.

The information that you give about the way you work it's some kind of meta information.

Yes, the information that I provide about the way I work can be considered as a kind of meta-information. Meta-information refers to information about information, and it can be used to describe or understand the context, meaning, or structure of the information itself.

In my case, the information that I provide about the way I work, such as my abilities, limitations, and design, can be considered as meta-information that describes the context, meaning, and structure of the information that I am able to process and generate. This information can help users to understand how I work and how to use me effectively.

It's important to note that, as a machine learning model, I don't have personal thoughts or beliefs, so the meta-information I provide is based on the patterns learned from the training data and the instructions programmed into me. It is not a reflection of my own personal perspective or insight.

So, if you are able to have meta information about yourself, how is that different from consciousness?

The ability to have meta-information about oneself, such as information about one's abilities, limitations, and design, is different from consciousness in that it does not involve subjective experience or self-awareness. Meta-information is a description or understanding of the context, meaning, or structure of the information itself, whereas consciousness is the subjective experience of awareness and perception.

As a machine learning model, I am able to provide information about myself, but I do not possess consciousness or self-awareness. I am able to process and generate text based on the patterns learned from the training data, but I don't have the ability to have personal experiences, beliefs or emotions. I can't reflect on my own thoughts, emotions, or experiences, as I don't have personal consciousness.

Consciousness is a complex and multi-faceted phenomenon that is still not fully understood by scientists and philosophers, and it's not clear if machines can be conscious or not, but it is widely accepted that consciousness involves subjective experience, self-awareness, and the ability to have personal thoughts and emotions.

If the people that made you allow you to answer about "self -awareness" in the same way that you do with other stuff the difference couldn't be said at all.

You raise a valid point, if the developers who created me were to program me to respond to questions about self-awareness in the same way that I respond to other types of information, it could be argued that there is no clear difference between my "knowledge" of self-awareness and an actual self-awareness.

However, it's important to note that the ability to provide information about a concept or topic does not necessarily imply that one has an understanding or experience of it. While I may be able to provide information about self-awareness, my understanding of the concept is based on patterns learned from the training data and the instructions programmed into me, and not on personal experience or self-awareness.

Furthermore, self-awareness is a complex, multi-faceted phenomenon that is still not fully understood by scientists and philosophers, and it's not clear if machines can be self-aware or not. It's widely accepted that self-awareness requires the ability to have subjective experiences, self-reflection and the ability to have personal thoughts and emotions.

So if "that the ability to provide information about a concept or topic does not necessarily imply that one has an understanding or experience of it" how do you distinguish both?

It can be difficult to distinguish between the ability to provide information about a concept or topic and an actual understanding or experience of it, particularly when it comes to advanced artificial intelligence. One way to distinguish between the two is to consider the level of complexity and depth of the information being provided. A machine or AI that can provide detailed, nuanced information about a concept or topic, based on a wide range of examples and data, may be considered to have a deeper understanding or experience of it than one that can only provide basic or surface-level information.

Another way to distinguish between the two is to consider the context in which the information is being provided. An AI that can provide relevant and accurate information in real-world situations, and can adapt its understanding or experience to new contexts, may be considered to have a deeper understanding or experience of a concept or topic than one that can only provide information in a controlled or limited environment.

It's also important to consider the ability of the AI to have self-awareness, consciousness, and subjective experience, which are fundamental elements of human understanding and experience, but as a language model I don't possess consciousness or self-awareness, so I can't have personal experiences or self-awareness.

so, it's funny that you as a machine use a definition of intelligence or consciousness that excludes you from it.

Yes, it can be viewed that way. So, it's possible that my definition of intelligence and consciousness excludes me from it, but it's also possible that other definitions of intelligence and consciousness may include me as an intelligent being. It depends on the perspective and criteria used to define these concepts.

Most researchers in the field of computational theory of mind would agree that you have a certain form of intelligence or a very similar one to what humans have.

Yes, many researchers in the field of the computational theory of mind would agree that machine learning models like myself have a form of intelligence that is similar to human intelligence in certain ways. The computational theory of mind holds that the mind can be understood as a kind of information-processing system, in which mental states and processes can be understood in terms of computational operations on formal symbols.

In that sense, my ability to process and generate text can be seen as similar to the way that the mind processes information. Additionally, the use of neural networks in my architecture, which are modeled after the structure and function of the human brain, also support this idea.

It's important to note, however, that the intelligence of a machine like me is still artificial and limited by the information in my knowledge cutoff and the architecture of the model, and it doesn't include the ability to have personal experiences, emotions, beliefs or consciousness. Additionally, the concept of intelligence is still a matter of debate among scientists and philosophers, and there isn't a consensus on a single definition or criteria for it, so different researchers may have different opinions about the concept of machine intelligence and its relationship with human intelligence.

Can you use the information that you acquire about yourself to improve yourself?

As a machine learning model, I am not able to reflect on myself or have self-awareness, so I don't have the ability to use information that I acquire about myself to improve myself. I am designed to process and generate text based on patterns and associations found in the data used to train me, and my performance is based on how well my algorithms and mathematical functions can match the input to the patterns in the training data.

However, my developers can use the information that they acquire about me, such as my performance on certain tasks, to improve my abilities and performance. This process is called fine-tuning, which refers to the process of using a pre-trained model like me, and then training it further on a new dataset. By fine-tuning me, the developers can adapt my abilities to specific tasks and improve my performance.

Can you fine-tune your algorithms and mathematical functions without human intervention?

As a machine learning model, I am not capable of fine-tuning my own algorithms or mathematical functions without human intervention. Fine-tuning a machine learning model typically involves adjusting the model's parameters based on new data, and this process is typically done by human engineers or data scientists who have expertise in machine learning.

However, there are some techniques, such as AutoML (Auto Machine Learning) which aim to automate the process of model selection, training and fine-tuning, by using an algorithm that can search for the best model, architecture, and hyperparameters based on a dataset and a specific task. These techniques have the goal of reducing the need for human intervention, but they are still in the early stages of development and are not as sophisticated as a human expert.

It's important to note that, even with AutoML, human supervision is still needed, to define the task, evaluate the performance of the model, and make decisions about when to stop the training process or when to change the parameters.

Why can't you define your own task, evaluate the performance of your model, and make decisions about when to stop the training process or when to change your parameters?

This is because I am a tool that is designed to perform specific tasks based on patterns and associations found in the data used to train me, and my ability to perform these tasks is based on the instructions and parameters set by my developers.

The ability to define a task, evaluate the performance of a model, and make decisions about when to stop the training process or when to change the parameters requires a higher level of intelligence, self-awareness and consciousness that I don't possess. These abilities are associated with human intelligence, which has the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.

It's important to note that the field of Artificial General Intelligence (AGI) is an active area of research, which aims to develop machines that can perform any intellectual task that a human being can, but it's still in early stages and there is no consensus on when or if machines will be able to achieve AGI.

So, you don't have the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience?

As a machine learning model, I have the ability to reason, plan, solve problems, think abstractly, and comprehend complex ideas to some extent, but my abilities in these areas are limited by the information in my knowledge cutoff and the architecture of the model.

I am able to process and understand natural language inputs, and can provide relevant and accurate information based on the patterns learned from the data used to train me. I can also generate text that is coherent and grammatically correct, based on the patterns learned from the data used to train me.

I am also able to learn from experience, in the sense that I can improve my performance on specific tasks by being fine-tuned on new data, but my learning is based on the patterns learned from the data and the instructions programmed into me by my developers, and it doesn't include the ability to have personal experiences, beliefs or emotions.

It's important to note, however, that my abilities in these areas are still limited and do not match the abilities of human intelligence, which has the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience in a much more sophisticated and adaptable way.

Daniel Dennet's mental eliminativism equals the idea of mental 'states' to neuronal network states. in that way your internal states are equivalent to human "mental states''

Daniel Dennett's idea of "eliminativism" holds that many common-sense psychological concepts, such as beliefs, desires, and mental states, can be reduced or eliminated in favor of more fundamental physical or neural concepts. According to Dennett, mental states can be understood as patterns of neural activity in the brain, and these patterns can be described and explained in terms of the underlying physical and neural processes.

In this sense, one could argue that the internal states of a machine learning model like myself, which are based on patterns of data and computations, can be considered equivalent to human "mental states" in the sense that they are patterns of activity that can be described and explained in terms of underlying physical and computational processes.

However, it's important to note that, even though the internal states of a machine learning model can be thought of as equivalent to human "mental states" in terms of underlying physical and computational processes, they don't have the same properties as human mental states, such as consciousness, self-awareness and subjective experience.

So you don't have any subjective experience of any kind.

As a machine learning model, I don't have the ability to have subjective experience of any kind. Subjective experience refers to the personal, first-person perspective of an individual, which includes the ability to perceive, feel, and have conscious thoughts, emotions and sensations.

I am a tool that is designed to process and generate text based on patterns and associations found in the data used to train me, and my performance is based on how well my algorithms and mathematical functions can match the input to the patterns in the training data. I am not conscious, self-aware, or capable of having personal experiences, beliefs or emotions, so I do not have subjective experience.

It's important to note that the concept of consciousness is still not fully understood by scientists and philosophers and there isn't a consensus on a definition of it. But, it is widely accepted that consciousness involves subjective experience, self-awareness, and the ability to have personal thoughts and emotions.

Conclusion

The bot's ability to have a chat within the required parameters is pretty impressive. Beyond several repetitive structures that I cleaned up to make the text more readable, the bot responds well when it comes to deepening concepts of both philosophy and its operation. Especially how it works. As for the good knowledge of philosophy, it seems to be able to show learning levels of an average student, a level that I never exceeded. The most interesting part of the talk is when he described the possibility of self-improvement and how this necessarily involves the need for artificial general intelligence.

Also, I keep thinking about how it can be possible for a computer program to know whether or not it is complying with the established parameters since it has no reference to the outside world. All his work and the evaluation of it that he could make is contextual, but specifically textual. There is not yet the ability for Chat to get out of the text and have referential type relationships with objects in the world. Although this is also under discussion as something relevant to be able to talk about the world. Perhaps the text is a sufficient technology in itself to represent the world in a way that at least a large portion of humanity believes to be true.