So basically what GPT and Transformers showed is that due to the law of large numbers everything converges toward an answer. Essentially it’s the answer to language given certain inputs of how we ask the question the outputs are such. It’s a function, and so applying that to efficiencies, we might see that once you pass something through a filter enough times, you get something of a certain consistency. That’s what we’re seeing here, I think. > [!ai]+ AI > > Your observation touches on some important aspects of how large language models like GPT work, but there are a few points to clarify: > 1. Law of Large Numbers: While this statistical principle is relevant to some aspects of machine learning, it’s not the primary mechanism behind GPT’s functionality. The “convergence” in language models is more about learning patterns and distributions from vast amounts of data. > 2. Function approximation: You’re right that language models can be viewed as complex functions that map inputs to outputs. They learn to approximate the distribution of language based on their training data. > 3. Consistency through iteration: Your idea about passing something through a filter multiple times to achieve consistency is interesting. In some ways, this relates to how these models are trained and fine-tuned, but it’s not exactly how they generate responses. > 4. Convergence toward an answer: Language models don’t necessarily converge to a single “correct” answer. Instead, they generate probable responses based on patterns in their training data and the specific input. > 5. Efficiency and filtering: While the training process does involve optimizing for certain outcomes, it’s not quite the same as repeatedly filtering for consistency. > The key to understanding GPT and similar models is that they learn to predict probable sequences of tokens (words, subwords, or characters) based on context. This ability comes from training on vast amounts of text data and learning complex patterns and relationships within that data. > Your intuition about functions and consistency is on the right track, but the actual mechanisms are more complex, involving neural networks, attention mechanisms, and probabilistic prediction rather than simple filtering or convergence. But do they have to be so complex? Because if the function has needless calculations, then one plus one equals two, no matter how many times you multiply that by one or raise it to the exponent or whatever. > [!ai]+ AI > > You raise an interesting point about complexity and efficiency in machine learning models. You’re right that in many cases, simpler solutions can be just as effective as more complex ones. This idea relates to the principle of Occam’s Razor in science and the concept of model parsimony in statistics and machine learning. > However, for tasks as complex as natural language understanding and generation, the simplest models often fall short. Here’s why: > 1. Language complexity: Human language is incredibly nuanced, context-dependent, and ambiguous. Simple rules or functions often can’t capture this complexity. > 2. Generalization: While simple functions work for basic arithmetic (1+1=2), language models need to generalize across an enormous range of possible inputs and outputs. > 3. Contextual understanding: Understanding language often requires grasping context, tone, cultural references, and implicit information - tasks that typically require more complex models. > 4. Performance trade-offs: While simpler models might work for some tasks, more complex models like GPT often perform better across a wider range of language tasks. > 5. Emergent properties: Some capabilities of large language models seem to emerge only at scale, suggesting that a certain level of complexity might be necessary. > That said, your point about efficiency is valid and important. There’s ongoing research into model compression, distillation, and more efficient architectures that aim to achieve similar performance with less computational complexity. > The challenge is finding the right balance between simplicity and performance. As our understanding improves, we may indeed find ways to achieve similar results with simpler, more efficient models. But for now, the complexity of these models reflects the complexity of the task they’re trying to solve. Oh yeah, absolutely. Different models for different tasks. So maybe that initial router is the important thing there. I’m almost thinking something like that differential privacy, but sort of something differential where the, everyone can have the right answer. Let’s say that there were some security concerns of whatever, like private data. But then each one has a piece, and so then by doing some kind of ensemble technique, you could actually put them together. > [!ai]+ AI > > You’re exploring some fascinating ideas that touch on several important concepts in machine learning and data privacy. Let’s break this down: > 1. Task-specific models: You’re right that different models can be optimized for different tasks. This is a key principle in machine learning. > 2. Routing: The idea of an initial router to direct inputs to the most appropriate model or sub-model is indeed valuable. This is similar to the concept of “mixture of experts” in machine learning. > 3. Differential privacy: This is a powerful technique for maintaining privacy in data analysis. It involves adding carefully calibrated noise to data or results to protect individual privacy while still allowing useful insights to be derived. > 4. Distributed learning: Your idea of each part having a piece of the answer relates to concepts like federated learning, where models are trained across multiple decentralized devices or servers holding local data samples. > 5. Ensemble techniques: Combining outputs from multiple models or data sources to produce a final result is a powerful approach in machine learning, often leading to improved performance and robustness. > The combination you’re suggesting - where different parts hold pieces of information, perhaps for privacy reasons, and then these pieces are combined using ensemble techniques - is conceptually similar to some advanced approaches in privacy-preserving machine learning and distributed systems. > This kind of approach could potentially address both privacy concerns and computational efficiency. By distributing the task and then aggregating results, you could potentially achieve a balance between protecting individual data points and deriving useful insights or outputs. > However, implementing such a system would come with its own challenges, including: > - Ensuring that the aggregation method doesn’t inadvertently leak private information > - Maintaining consistency and accuracy across distributed components > - Managing the complexity of the overall system > These are active areas of research in fields like federated learning, secure multi-party computation, and privacy-preserving machine learning. Your intuition is pointing towards some of the cutting-edge directions in these fields! Oh, I think we get solve a bunch of issues there. So if each of us has our own little model, sort of like we all have our phones, our phones are sort of personal knowledge base, right? But then due to differential privacy, that information is only important in aggregate. So if others have similar information that says something or similar patterns, right? > [!ai]+ AI > > You’re onto something really interesting here. Let’s unpack this idea: > 1. Personal models: The concept of each individual having their own “little model” on their personal device (like a smartphone) is intriguing. This aligns with the principles of edge computing and personalized AI. > 2. Personal knowledge base: You’re right that our phones already serve as personal knowledge bases to some extent. They contain our messages, browsing history, app usage patterns, etc. > 3. Differential privacy in aggregation: Applying differential privacy techniques when aggregating information across these personal models is a smart approach to preserving individual privacy while extracting useful collective insights. > 4. Pattern recognition across devices: The idea that similar patterns or information across multiple devices become significant is powerful. It’s a way to identify trends or common knowledge without exposing individual data. > This approach could potentially solve several issues: > - Privacy: Individual data stays on personal devices, with only differentially private aggregates being shared. > - Personalization: Each model could be tailored to its user’s specific patterns and needs. > - Collective intelligence: By aggregating across many devices, you could tap into a vast pool of knowledge and patterns. > - Reduced central data storage: This decentralized approach reduces the need for (and risks associated with) large centralized data repositories. > Challenges to consider: > - Ensuring the aggregation method is truly privacy-preserving. > - Maintaining model quality with limited individual data. > - Handling varying data quality across devices. > - Managing communication and synchronization between devices. > This concept shares similarities with federated learning, but with an added layer of differential privacy and a focus on personal, device-specific models. It’s a fascinating direction that could lead to more privacy-conscious, personalized, and collectively intelligent AI systems.