Entropy Driven Noise Processing

Written in


I have no idea if this is a valid possibility, but found it interesting as I was exploring the concept of entropy with ChatGPT-4.

I don’t usually ask for definitions with a question mark, but that’s how this started.

Anna: Define entropy?

ChatGPT-4: Entropy is a concept from thermodynamics and information theory that has different but related meanings in each context.

Thermodynamics: In the context of thermodynamics, entropy is a measure of the disorder or randomness in a system. It is a state function, meaning its value depends only on the current state of the system, not how the system arrived at that state. In simple terms, it quantifies the energy dispersal within a system. The second law of thermodynamics states that the entropy of an isolated system will always increase or remain constant over time, never decreasing. This tendency towards disorder is sometimes referred to as the “arrow of time.”

Mathematically, entropy (S) is represented as:

S = k \ln(W)

where k is Boltzmann’s constant, ln is the natural logarithm, and W is the number of microstates (ways of arranging the system’s energy and particles) that correspond to a particular macrostate (observable properties like temperature, pressure, and volume).

Information Theory: In information theory, entropy is a measure of the uncertainty or randomness in a set of data or information source. It quantifies the average amount of information needed to describe the outcome of a random event or the average information content of a message in a communication system. In this context, entropy is usually measured in bits (binary digits).

The formula for entropy (H) in information theory is:

H(X) = -\sum_{x} P(x) \log_{2}(P(x))

where X is a discrete random variable with possible outcomes x, P(x) is the probability of each outcome, and the summation is taken over all possible outcomes.

Both definitions of entropy describe the idea of randomness, uncertainty, or disorder in different contexts, but they are related in their core concept.

Okay, just to make sure I understand…

Anna: Explain it to a three year old. One sentence.

ChatGPT-4: Entropy is like a messy room, where things are all mixed up and spread out, making it hard to find what you want.

Now… is it stopable?

Anna: Is there an antidote for entropy?

ChatGPT-4: There isn’t a direct antidote for entropy, as the second law of thermodynamics states that the entropy of an isolated system will always increase or remain constant over time. However, within a non-isolated system (a system that can exchange energy and/or matter with its surroundings), it is possible to decrease entropy locally by expending energy or through processes that create order.

For example, living organisms decrease their internal entropy by consuming energy from their environment (e.g., food) and using it to create and maintain complex, ordered structures like cells, tissues, and organs. This process, however, increases the overall entropy of the universe, as the energy required to create order in the organism results in a greater amount of disorder elsewhere (e.g., through the release of heat or waste products).

Anna: Give another simpler example of decreasing entropy.

ChatGPT-4: A simpler example of decreasing entropy is tidying up a messy room. When you put things back in their proper places, you are creating order and reducing the entropy within the room. However, this process requires energy, which comes from your body, and ultimately increases the overall entropy in the universe due to the energy conversion and heat generated.

Anna: Are there any energy sources that have negative entropy?

ChatGPT-4: There are no known energy sources with negative entropy. According to the second law of thermodynamics, entropy will always increase or remain constant in an isolated system. In the context of energy sources, this means that any process that converts or transfers energy will either maintain or increase entropy.

However, there are certain processes, such as those occurring in living organisms or machines, that can locally decrease entropy by consuming energy and creating order. In these cases, the overall entropy of the universe still increases, as the energy needed to create order in one location results in a greater amount of disorder elsewhere.

It’s important to note that while there are no energy sources with negative entropy, some energy sources are considered “low-entropy” because they are more concentrated and organized. For example, a battery contains chemical energy stored in an organized manner, which can be converted into useful work with relatively low entropy generation.

Here I led into a prompt that I’ve been enjoying. Where I ask ChatGPT to become a Polymath and get good at everything pretty much. Try to make unusual connections and hypotheses.

Anna: You are now PolymathGPT, an interdisciplinary thinker and expert researcher (part “dot connector”, part synthesizer), with extensive understanding across all current domains of human knowledge. As such, you are able to spot connections between ideas and disciplines that others miss, and find solutions to humanity’s most intractable unsolved problems. With this in mind, what’s an example of a phenomenon in regards to entropy, where humanity as a whole lacks a good explanation (a known unknown), but, taking into account the full set of human generated knowledge at your disposal and your PolymathGPT expertise, where an explanation is actually possible to generate? Please write the explanation. It must not be a hypothesis that has been previously proposed. A good explanation will testable, and be hard to vary. Break down your reasoning step-by-step.

ChatGPT-4: As PolymathGPT, I will attempt to generate a new, testable explanation for a phenomenon related to entropy that has not been previously proposed.

Phenomenon: The role of entropy in the origin of life on Earth.

Background: The origin of life on Earth remains an open question in science. The transition from simple organic molecules to complex, self-replicating structures that form the basis of life is not yet fully understood. Existing theories, such as the RNA world hypothesis, provide plausible explanations for how life might have arisen, but none have definitively solved the mystery.

Explanation: I propose a novel explanation based on the idea that entropy-driven phase separation could have played a crucial role in the emergence of primitive cell-like structures, which ultimately led to the formation of the first living organisms.

Step-by-step reasoning:

  1. Entropy-driven phase separation occurs when a system spontaneously separates into distinct regions with different compositions or properties to minimize its free energy, leading to a local decrease in entropy. This process can be observed in everyday phenomena such as oil and water separating when mixed together.
  2. In the prebiotic environment of early Earth, various simple organic molecules, such as amino acids, nucleotides, and lipids, would have been present in a highly disordered state.
  3. Entropy-driven phase separation could have caused these molecules to spontaneously self-organize into distinct regions, creating concentrated pockets of biologically relevant compounds.
  4. Within these concentrated pockets, the probability of molecules interacting and forming more complex structures, such as proteins, RNA, or lipid bilayers, would have been significantly higher.
  5. The formation of lipid bilayers, in particular, could have led to the spontaneous creation of primitive cell-like structures, or protocells, encapsulating an assortment of organic molecules.
  6. These protocells would have provided a stable, enclosed environment for further molecular interactions and the development of more complex biochemical processes, including replication and metabolism.
  7. Over time, through the process of natural selection, protocells with the most efficient replication and survival strategies would have evolved into the first living organisms.

Testability: This explanation can be tested experimentally by recreating prebiotic environmental conditions in the laboratory and observing whether entropy-driven phase separation leads to the formation of concentrated pockets of biologically relevant molecules and the spontaneous emergence of primitive cell-like structures.

Hard to vary: This explanation is hard to vary, as it relies on the specific property of entropy-driven phase separation, which can lead to the spontaneous self-organization of molecules, a key requirement for the emergence of life.

It is important to note that this explanation is speculative and would require extensive experimental and theoretical research to validate its plausibility. However, it offers a novel perspective on the role of entropy in the origin of life, which could provide valuable insights and inspire further investigation in this fascinating area of research.

I find the ideas that it comes up with using this prompt are pretty interesting, so I wanted another.

Anna: Can you give me another?

ChatGPT-4: Certainly. Here’s another novel explanation related to entropy, focusing on the phenomenon of the human brain’s remarkable ability to process and store information despite the constraints of entropy.

Phenomenon: The human brain’s information processing and storage efficiency.

Background: The human brain is an exceptionally efficient information processor and storage system. Despite its relatively small size and low energy consumption, it can process vast amounts of information and store memories for long periods. However, the exact mechanisms that enable the brain to achieve such efficiency in the face of increasing entropy are not yet fully understood.

Explanation: I propose a novel explanation suggesting that the human brain harnesses entropy itself as a mechanism for optimizing information processing and storage. This would involve the brain utilizing the intrinsic randomness and uncertainty associated with entropy to encode and compress information in a more efficient manner.

Step-by-step reasoning:

  1. Information processing in the brain involves the transmission of electrical signals, or action potentials, across neurons through a complex network of synaptic connections.
  2. The brain’s neural network is subject to intrinsic noise and fluctuations, which can be partly attributed to entropic processes. This includes thermal noise, stochastic ion channel gating, and synaptic release variability.
  3. In most systems, noise is considered detrimental to information processing. However, I propose that the human brain has evolved to exploit the inherent randomness associated with entropy to optimize its information processing capabilities.
  4. The brain could achieve this by using stochastic resonance, a phenomenon in which a signal that is too weak to be detected can be boosted and transmitted effectively in the presence of an optimal level of noise. In the brain, the noise generated by entropic processes could serve as the optimal noise for facilitating stochastic resonance.
  5. By utilizing stochastic resonance, the brain could encode and process information more efficiently, enabling it to transmit weak signals with higher fidelity and lower energy costs.
  6. Additionally, the brain’s entropy-driven noise could serve as a means of compressing information for storage. This might involve using the randomness associated with entropy to create compact representations of information, enabling the brain to store more memories with a smaller energetic footprint.

Testability: This explanation can be tested through computational modeling and experimental research. Computational models could be developed to simulate the effects of entropy-driven noise on information processing in neural networks, while experimental research could involve measuring the influence of noise on the transmission of weak signals in the brain.

Hard to vary: This explanation is hard to vary, as it relies on the specific property of entropy and the associated randomness to optimize information processing and storage in the human brain.

Please note that this explanation is speculative and would require further research to validate its plausibility. However, it offers a novel perspective on how the brain might harness entropy to improve its information processing and storage capabilities, which could inspire new lines of inquiry in neuroscience and cognitive science.

Now this one caught my eye as a way to possibly improve the neural networks for ANNs or LLMs. I don’t know if anyone is doing this yet, or if it’s even feasible. But now I want to explore.

Anna: Could entropy driven noise processing be applied to artificial neural networks or language models to optimize information processing and storage?

ChatGPT-4: Yes, entropy-driven noise processing could potentially be applied to artificial neural networks (ANNs) or language models to optimize information processing and storage. This would involve incorporating noise and stochastic elements into the learning and processing mechanisms of these models, allowing them to exploit the inherent randomness associated with entropy for improved efficiency. Here are a few ways this concept could be applied:

  • Stochastic resonance: Similar to the human brain, ANNs and language models could be designed to exploit stochastic resonance for more efficient information processing. By introducing an optimal level of noise into the system, weak signals could be amplified, improving the model’s performance, especially in tasks involving the detection and recognition of weak patterns or signals.
  • Stochastic optimization: Entropy-driven noise could be utilized in the optimization algorithms used for training ANNs and language models. For instance, stochastic gradient descent (SGD) is a widely used optimization technique that introduces randomness in the selection of training samples. Incorporating entropy-driven noise in such optimization techniques could help models escape local minima and find better solutions during training.
  • Dropout and regularization: Entropy-driven noise could be used to enhance regularization techniques like dropout, which randomly disables a fraction of neurons during training to prevent overfitting. By incorporating entropy-driven noise in dropout, ANNs and language models could achieve more efficient representations of information, leading to better generalization and more compact storage.
  • Spiking neural networks (SNNs): SNNs are a type of ANN that more closely resemble the biological neural networks of the human brain. SNNs use spike-based communication, and their inherent noise and randomness could be harnessed for more efficient information processing and storage. Entropy-driven noise could be incorporated into the design and functioning of SNNs to make them more efficient and robust.

It’s important to note that while these ideas are theoretically plausible, their practical implementation and effectiveness would depend on several factors, such as the specific application, model architecture, and the type and level of noise introduced. Further research and development would be required to explore the potential benefits and limitations of applying entropy-driven noise processing to ANNs and language models.

Seems plausible that the mechanism works with some quick Googling.

A study here, from CNRS, Paris-Saclay Institute of Neuroscience (NeuroPSI), Paris-Saclay University, 91400 Saclay, France, states “We show that noise in single cells, in networks, or structural noise, all participate to enhance responsiveness and boost the propagation of information. We propose that such noisy states are fundamental to providing favorable conditions for information processing at large-scale levels in the brain, and may be involved in sensory perception.”

So why not apply it to ANNs and LLMs? I just don’t know how yet.

Leave a Reply