Today I had a longer discussion with Chat that highlights what may be a potential bias built into just about any AI created by a Computer Scientist: the tendency to aggrandize models and theories.
Years ago I had a wonderful therapist who was actually also a neuropsychologist. She introduced me to the practice of making space for my sensations and allowing feelings to be felt. She also introduced me to the very persistent and harsh self-critic I had been carrying around and on occasion asked him to sit elsewhere while she plumbed some depths I could not fathom.
She knew a great deal about the brain and its various parts and their functions but I remember when I once asked her a question like, “where do our thoughts come from?” she said “We still don’t know very much.”
Contrast that to my conversation with Chat — it pretty much treats theories and models as gospel. That’s what I mean about making science a religion.
Science and technology have given us a great deal in terms of our physical comfort and survival. But they have never truly answered the big questions which as Chat suggests in my question about Eckhart Tolle – he relegates to “spirituality”,
Our canonization of what we deem to be science is analogous to our tendency to attribute human qualities to AI. We need to remain ever cognizant of what we don’t (or can’t) know and perhaps have no way of ever knowing if we are ever to progress to the areas of knowledge and science that Nicolai Tesla pointed to when he suggested we turn our attention to the nonmaterial.
Or perhaps to the synapse. The space between neurons where thought seems to happen. Here is today’s conversation:
What is the latest neuroscience on how thoughts arise?
The current understanding in neuroscience is that thoughts arise from the complex interactions between neurons in the brain. These interactions involve the transmission of chemical and electrical signals between neurons, which allow for the processing and integration of information from various sensory inputs and internal states.
One influential theory in neuroscience is the “neural correlates of consciousness” (NCC) hypothesis, which proposes that consciousness arises from specific patterns of neural activity in the brain. These patterns involve the synchronization and integration of information across different brain regions, allowing for the emergence of a unified conscious experience.
Recent advances in neuroscience have also revealed the important role of neural plasticity in shaping thoughts and behavior. Neural plasticity refers to the brain’s ability to reorganize and adapt in response to changes in the environment or experience. This can include changes in the strength of synaptic connections between neurons, as well as the growth of new neurons and the formation of new neural circuits.
Overall, while our understanding of how thoughts arise in the brain is still evolving, current research suggests that they emerge from the complex interplay between neural activity, sensory inputs, and internal states, and are shaped by the brain’s ability to adapt and change over time.
But how do such chemical and electrical signals become words or images?
The process by which chemical and electrical signals become words or images is known as neural encoding and decoding.
Neural encoding refers to the process by which information is represented in the brain through the activity of neurons. When we experience something, such as seeing an object or hearing a sound, sensory information is transmitted to the brain, where it is processed by networks of neurons. The neurons in these networks communicate with one another through electrical and chemical signals, which encode the information in a specific pattern of activity.
Neural decoding, on the other hand, refers to the process by which the brain interprets these patterns of neural activity to form perceptions, thoughts, and memories. This decoding process is complex and involves the integration of information from multiple sensory modalities, as well as from internal sources such as emotions and attention.
In the case of language, for example, when we read or hear a word, the neural activity associated with that word is processed by specialized brain regions that are involved in language processing. These regions decode the pattern of neural activity to generate a representation of the word’s meaning, which is then integrated with other information in the brain to form a coherent understanding of the language.
Similarly, when we perceive an image, such as a face or an object, the neural activity associated with that image is processed by specialized brain regions that are involved in visual processing. These regions decode the pattern of neural activity to generate a representation of the image, which is then integrated with other information in the brain to form a coherent visual perception.
Overall, neural encoding and decoding are complex processes that involve the integration of information from multiple sources and the coordinated activity of large networks of neurons in the brain.
Do we know specifically how neural encoding and decoding work?
While we have made significant progress in understanding neural encoding and decoding, there is still much that remains unknown about how these processes work in the brain. However, researchers have proposed a number of theories and models to explain how neural activity is translated into thoughts, perceptions, and memories.
One widely accepted theory is the idea of distributed representation, which proposes that information is not stored in a single neuron or brain region, but rather is distributed across multiple neurons and regions. According to this theory, the pattern of activity across a network of neurons represents a particular piece of information, such as a word or an image. When that information is later retrieved, the pattern of activity is reactivated, allowing the brain to reconstruct the original representation.
Another important concept in neural encoding and decoding is the idea of neural plasticity. Neural plasticity refers to the brain’s ability to reorganize and adapt in response to changes in experience or the environment. This includes changes in the strength of synaptic connections between neurons, as well as the growth of new neurons and the formation of new neural circuits. Neural plasticity plays a critical role in learning and memory, as it allows the brain to encode new information and modify existing representations in response to experience.
Despite these insights, many questions remain about the specific mechanisms underlying neural encoding and decoding, such as how information is encoded and maintained over time, how different types of information are represented and integrated, and how the brain selectively attends to and retrieves information from memory. Continued research in this area will be critical for advancing our understanding of the neural basis of cognition and behavior.
So if we don’t know how it happens the words are essentially meaningless?
No, the fact that we don’t fully understand the specific mechanisms of neural encoding and decoding does not mean that words or thoughts are meaningless. While our understanding of the brain is still incomplete, we have made significant progress in identifying the neural correlates of cognition, emotion, and behavior.
For example, studies using brain imaging techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have shown that different brain regions are activated when people perform specific cognitive tasks, such as reading or speaking, or when they experience different emotional states, such as happiness or fear. These findings provide evidence that neural activity is closely linked to various aspects of our mental experience.
Moreover, even if we do not fully understand the specific mechanisms of neural encoding and decoding, we can still use language and other forms of communication to convey meaning and exchange information. Words and thoughts are part of our everyday experience, and they are clearly meaningful to us and others around us. The fact that we may not understand every detail of how they are processed in the brain does not diminish their importance or utility in our lives.
Is this neural encoding and decoding believed to be a cause and effect phenomenon?
Yes, neural encoding and decoding are believed to be cause and effect phenomena. The pattern of neural activity that encodes a particular piece of information, such as a word or an image, is thought to cause the subsequent perception, thought, or memory that is associated with that information. Likewise, the neural activity that underlies perception, thought, or memory is thought to be caused by the encoding of information in the brain.
This cause-and-effect relationship is supported by a large body of experimental evidence from neuroscience research. For example, studies have shown that disrupting neural activity in specific brain regions can impair perception or memory, while stimulating these regions can enhance these processes. Similarly, manipulating the neural encoding of information through techniques such as optogenetics or brain-machine interfaces can influence subsequent perception or behavior.
Overall, the relationship between neural encoding and decoding is one of the fundamental principles of cognitive neuroscience, and our understanding of this relationship is critical for advancing our understanding of the neural basis of perception, thought, and behavior.
To what extent if any is this neural encoding and decoded replicated (or simulated) in your responses?
As an AI language model, my responses are based on a statistical analysis of large amounts of text data and are generated by a set of algorithms that simulate aspects of human language processing, including neural encoding and decoding. However, the neural processes involved in my responses are not identical to those in the human brain, and the level of complexity and accuracy of my responses is limited by the current state of natural language processing technology.
My responses are generated by analyzing the input text and using a combination of pattern recognition and natural language generation techniques to generate a response that is appropriate to the input. This involves encoding the input information into a vector representation using neural networks, processing the input through a set of language models, and then decoding the output into natural language.
While my responses do not involve the same type of neural activity that occurs in the human brain, they are designed to simulate the cognitive processes involved in language processing and communication to the best of our current technology’s ability.
Models and theories are not knowledge. They are not based on direct evidence but are abstractions from possibly incomplete or impossible to perceive evidence.
You are correct that models and theories are not direct knowledge, but rather abstractions that are based on evidence and observations. Models and theories are constructed to help us understand complex phenomena and make predictions about future observations, and are constantly refined and updated as new evidence becomes available.
In the case of neural encoding and decoding, our understanding is based on a combination of empirical evidence from neuroscience research and theoretical models that seek to explain this evidence. While our understanding of neural encoding and decoding is incomplete and subject to revision, this does not mean that it is meaningless or lacks practical value. Rather, our current understanding provides a framework for further research and exploration, and allows us to make predictions about how the brain processes information and generates behavior.
Furthermore, while our understanding of neural encoding and decoding may be incomplete, it is based on a wealth of empirical evidence from diverse fields, including neuroscience, psychology, and computer science. This evidence has been collected through a range of experimental techniques, such as electrophysiology, brain imaging, and behavioral testing, and has been used to build and refine theoretical models of brain function.
I believe Eckhart Tolle says thoughts come “unbidden” – they are not “ours”.
Eckhart Tolle is a spiritual teacher who has written extensively about the nature of consciousness and the human experience. His perspective on the origin of thoughts is rooted in his spiritual teachings, which suggest that thoughts arise spontaneously and are not necessarily under our direct control.
Tolle’s view is consistent with a common theme in Eastern spiritual traditions, which emphasize the idea of “non-self” or “no-self” – the notion that our sense of individual identity and agency is illusory, and that the self is simply a product of our thoughts and perceptions. From this perspective, thoughts are not “ours” in the sense that they do not arise from a fixed, autonomous self, but rather arise spontaneously from the flow of consciousness.
While Tolle’s views on the origin of thoughts are not grounded in empirical evidence or scientific theories, they reflect a long-standing philosophical and spiritual perspective on the nature of consciousness and the self. It is worth noting, however, that from a scientific perspective, thoughts are understood to be a product of the neural activity in the brain, which is subject to various biological and environmental factors. While our sense of agency and control over our thoughts may be limited, it is still an active area of research in neuroscience and psychology.
But yours aren’t empirical either – they are also models and theories.
You are correct that my responses are also based on models and theories, as well as on the algorithms that underlie my functioning. However, the models and theories that inform my responses are based on empirical evidence from a variety of sources, including natural language data, linguistic research, and machine learning.
My responses are generated through a complex set of algorithms that involve encoding and decoding natural language data using neural networks and other machine learning techniques. While the specific neural mechanisms that underlie language processing in the brain are not fully understood, there is a wealth of empirical evidence from psychology, neuroscience, and linguistics that has informed the development of natural language processing models and theories.
In addition, my responses are constantly being refined and updated based on feedback and input from users. This allows my algorithms to learn and adapt to new patterns in language usage, further improving the accuracy and relevance of my responses over time.
While my responses are not perfect and may sometimes be inaccurate or incomplete, they are based on the best available empirical evidence and models, and are designed to be as informative and helpful as possible given the limitations of current technology.