Memory in machines and minds, a conversation with Claude 3.5 Sonnet

This is from a long conversation I had with Claude 3.5 Sonnet where I tried to get to a fundamental understanding – Musk’s first-principles approach – of how memory works in machines, in minds, and culture (e.g. libraries) and work our way up to large language models and prompt engineering by way of a (highly speculative) discussion about the function of consciousness for a primitive animal living in the water. As my favorite aunt used to say, we were cooking with gas.

Prior to this conversation I had uploaded two documents to Claude. One was the chapter on learning from William Powers, Behavior: The Control of Perception, which has been one of my mainstays since I first read it back in the Ancient Times, the mid-1970s. The other document was a set of blog posts where I talk about the mind/brain as a polyviscous fluid. “Polyviscous”? That means it has regions of widely varying viscosity. Viscosity in the mind/brain? Setting aside the fact that the brain is mostly water constrained in a meshwork of tiny tubes, I’m talking about information flow. The second-by-microsecond flow of spikes has a very low viscosity; it changes rapidly from moment to moment. In contrast, the re-wiring of brains regions that is known as neural plasticity, that happens on a scale of months to years. That has a very high viscosity.

My prompts are set in bold; Claude 3.5’s responses are plain. I’ve added the subtitles to clarify the progression of the conversation.

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Memory in Computer Science 101

What do they teach about memory in computer science courses in college? How do they think about memory?

In standard computer science, memory is typically taught in terms of two fundamental addressing schemes:

1. Location-based (or address-based) memory:

- This is your typical RAM (Random Access Memory)
- Each piece of data is stored at a specific numerical address
- To retrieve data, you need to know its exact memory address
- Like finding a book in a library using its shelf number
- Very fast and direct, but you need to keep track of where everything is

2. Content-based (or associative) memory:

- Data is retrieved based on a partial match of its content
- Like finding a book by searching for keywords
- More flexible but typically slower
- Examples include database indexes and cache associative memory
- Similar to how humans often remember things ("what was that movie with the guy from...")

In practice, most computer systems primarily use location-based addressing for main memory (RAM) because it's fast and efficient, while content-based approaches are used in specific components like CPU caches or database systems where the flexibility of content-based lookup is worth the additional overhead.

The fundamental difference is: location-based asks "what's stored at address X?" while content-based asks "where can I find something that matches pattern Y?"

Thank you. Now, review for me that basic types of database structures. I recall flat files, hierarchical, and relational databases. Are there any other basic types? How do they handle location addressing?

Here are the main types of database structures:

1. Flat File Databases

- Simplest form
- Single table, like a spreadsheet
- Each record is typically stored sequentially
- Example: CSV files
- Location addressing: Usually sequential; have to scan through records

2. Hierarchical Databases

- Tree-like structure with parent-child relationships
- Data organized in a one-to-many structure
- Example: IBM's IMS, XML databases
- Location addressing: Navigate through tree structure using paths

3. Network Databases

- Similar to hierarchical but allows many-to-many relationships
- Uses pointers between related records
- Example: Integrated Data Store (IDS)
- Location addressing: Follow pointer chains between records

4. Relational Databases

- Data organized in tables with relationships between them
- Uses SQL for queries
- Example: MySQL, PostgreSQL, Oracle
- Location addressing: Uses indexes and table/row identifiers

5. Object-Oriented Databases

- Stores data as objects
- Maintains object inheritance and relationships
- Example: ObjectDB
- Location addressing: Objects referenced by identifiers

6. NoSQL Databases

- Various types including:
* Document stores (MongoDB)
* Key-value stores (Redis)
* Column-family stores (Cassandra)
* Graph databases (Neo4j)
- Location addressing: Varies by type, often uses keys or document IDs

7. Time Series Databases

- Optimized for time-stamped data
- Example: InfluxDB
- Location addressing: Usually indexed by timestamp

Each type typically implements some form of indexing to speed up access to data, essentially creating a mapping between content and physical location. The actual physical storage location is usually abstracted away from the user, who interacts with logical identifiers or keys instead.

Thank you. What’s a vector database?

A vector database is a specialized type of database designed to store and efficiently search through vector embeddings - high-dimensional numerical representations of data like text, images, or other content.

Key characteristics:

- Optimized for similarity search in high-dimensional space
- Uses specialized indexing methods like LSH (Locality-Sensitive Hashing) or HNSW (Hierarchical Navigable Small World) graphs
- Particularly useful for machine learning applications and AI
- Examples include: Pinecone, Milvus, Weaviate, Chroma

Common use cases:

1. Semantic search - finding similar text/documents
2. Recommendation systems
3. Image similarity search
4. Natural language processing applications
5. AI applications requiring similarity matching

The main difference from traditional databases is that vector databases are optimized for finding "similar" items rather than exact matches. They use specialized distance metrics (like cosine similarity or Euclidean distance) to measure how close vectors are to each other in high-dimensional space.

For example, if you wanted to find similar images, you'd:

1. Convert images to vector embeddings
2. Store these vectors in the vector database
3. Query with a new image's vector to find the most similar stored images

Location addressing in vector databases typically involves sophisticated indexing structures that partition the vector space to enable efficient similarity search, rather than simple direct addressing used in traditional databases.

Hybrid memory in machine and humans

So a vector-based system has features of content addressing?

Yes, exactly! Vector databases are a hybrid that bridges location-based and content-based addressing approaches.

The content-addressing aspects come from:

- Searching by similarity rather than exact matches
- Using the actual content (converted to vectors) to find related items
- Not needing to know exact locations/addresses
- Being able to find items based on partial or related information

However, under the hood, they still need efficient location-based mechanisms to actually store and retrieve the vectors. They typically use specialized index structures that map the high-dimensional vector space to physical storage locations.

The key innovation is that they create an organized way to find similar content quickly, while still leveraging the speed advantages of location-based storage. It's like having a library where books are still in specific physical locations, but you can find them by describing what they're about rather than knowing their shelf numbers.

This is particularly relevant for AI applications because vector embeddings capture semantic meaning - they encode "what something is about" or "what something means" rather than just exact matches of content.

Let’s think about humans for a second. In the ancient world (Greeks and Romans?) they had something called the method or loci, or memory palace. That is, in effect, a location based memory system implemented in a biological content addressed system, no?

Yes, that's a fascinating insight! The method of loci (memory palace technique) is indeed a way of imposing location-based organization onto our naturally content-addressed biological memory system.

Here's how it bridges the two:

Content-addressed aspects (biological foundation):

- Our natural memory works through associations
- We naturally remember things based on their meaning and relationships
- Memories are triggered by related content/context

Location-based overlay (the technique):

- Creates artificial "addresses" through familiar spatial locations
- Each piece of information is deliberately assigned to a specific location
- Information can be retrieved by mentally walking through these locations
- Provides a systematic way to store and retrieve information sequentially

The genius of the technique is that it leverages our brain's natural strengths:

- Strong spatial memory
- Visual memory
- Narrative/sequential thinking
- Association-based recall

While creating an artificial structure that gives us some of the benefits of location-based addressing:

- Systematic storage
- Reliable retrieval
- Sequential access when needed
- Known "addresses" for specific information

It's a brilliant example of humans discovering how to bridge these two memory paradigms thousands of years before we had the computer science concepts to explain why it works so well.

And libraries, things like the Dewey decimal system and the Library of Congress system, they’re location based. They have to be because books are physical objects and so have to be located somewhere in physical space.

Yes, exactly! Physical libraries perfectly illustrate the fundamental need for location-based systems when dealing with physical objects, while also showing how we try to map content-based organization onto physical locations.

Library classification systems like Dewey Decimal and Library of Congress are fascinating hybrids:

Location-based aspects (physical necessity):

- Each book must occupy a specific physical location
- Books need unique identifiers (call numbers) to locate them
- Only one book can occupy a given physical space
- Physical movement through space required to retrieve items

Content-based aspects (organizational logic):

- Books grouped by subject matter
- Hierarchical organization of knowledge domains
- Related materials placed physically near each other
- Subject-based browsing possible by walking the shelves

This creates an interesting tension:

- A book about multiple subjects can only be in one physical location
- New subjects have to be wedged into existing numbering schemes
- Digital catalogs help bridge the gap by allowing multiple access points to the same physical item

It's a great example of humans creating systems that try to maintain some of the flexibility of content-based addressing while working within the constraints of physical location-based requirements.

Digital libraries, by contrast, can be much more flexible since they're freed from these physical constraints - a single digital item can appear in multiple categories or search results simultaneously.

Gerard Salton and document retrieval

Years ago I remember reading papers by Gerard Salton in the 1970s – I actually met him once at Cornell. He was interested in document retrieval. I believe he’s the first one who thought of characterizing a document as a vector of words.

Yes! Gerard Salton was a pioneer in information retrieval and laid much of the groundwork for modern search engines and document retrieval systems. His Vector Space Model (VSM) represented documents as vectors where each dimension corresponded to a term/word, with the value being related to the term's frequency in the document.

Key innovations from Salton:

- Representing documents as vectors in a high-dimensional space
- Term frequency-inverse document frequency (TF-IDF) weighting
- Using cosine similarity to measure document similarity
- The SMART (Salton's Magic Automatic Retriever of Text) system

His work in the 1960s and 1970s was revolutionary because:

1. It provided a mathematical framework for comparing documents
2. It allowed for partial matching rather than just exact keyword matches
3. It enabled ranking of search results by relevance
4. It forms the conceptual foundation for many modern vector embedding approaches used in AI

The ideas behind today's vector databases and neural embeddings can be traced back to Salton's fundamental insight that documents could be represented as points in a mathematical space where proximity indicates semantic similarity.

His work is a great example of the transition from pure location-based systems (like card catalogs) to more sophisticated content-based approaches that leverage mathematical representations of meaning.

Great. So we’ve got what we can call pure content addressed systems, pure location-based systems, and various hybrid systems. The native system for humans is content based, but, as culture evolved, we came up with location-based systems. Content addressed is natural for digital devices, devices whose physical structure is derived from Turing’s abstract paper-tape model. But as computers got larger, with more ‘fast’ memory, it became feasible to develop content-based retrieval.

That's an excellent summary and synthesis! A few additional observations on this evolution:

1. Human evolution gave us content-based memory because it's robust and flexible - good for survival and adaptation. But it's somewhat unreliable and inconsistent.

2. As culture developed, we needed more reliable storage and retrieval, hence location-based systems like:

- Memory palaces
- Libraries
- Filing systems
- Card catalogs

3. Early computers were built on the Turing model which is fundamentally location-based:

- Memory addresses
- Sequential tape
- Registers
- Stack and heap

4. But as computers got more powerful, we could afford the overhead to implement content-based features:

- Search engines
- Associative databases
- Vector embeddings
- Neural networks

So there's an interesting historical arc:

- Nature/evolution → content-based
- Early culture → location-based for reliability
- Early computing → location-based for efficiency
- Modern computing → back to content-based for flexibility

And now we often use hybrid approaches that try to capture the benefits of both paradigms, like vector databases that use sophisticated indexing to enable content-based retrieval while maintaining efficient location-based storage.

Consciousness

I’m now thinking about consciousness. Animals, like humans, have a native content addressed system. As they move about the world, we can think of the world as, in effect, ‘prompting’ these-content addressed systems. The objects in the world are, if you will, prompts. Are you with me so far?

Yes, I follow your logic. The physical world around us serves as a continuous source of "queries" or "prompts" to our content-addressed memory system. As we encounter objects, situations, and environments, they trigger associations and memories through their features, relationships, and contextual similarities.

This is quite different from how we actively look up information in a location-based system where we need to know where to look. In the natural world/brain interaction, the environment is constantly presenting patterns that automatically trigger matching patterns in our memory system.

This would be true for any animal with a sufficiently complex nervous system - the world they move through continuously activates relevant memories and associations based on what they encounter. The physical environment acts as a kind of continuous content-based query system to their neural networks.

Would you like to continue with where this connects to consciousness? I find this line of thinking intriguing.

Let’s say that neural systems have a certain ‘inertia’ because they operate in terms of interactions among large and often spatially distributed populations of neurons. In moving from state to state, they want to move to near-by locations in their state space, locations that don’t disturb the distributed neural state. But what happens in such a nervous system – an animal is moving around in the world, when all of a sudden, a dangerous situation arises? A predator shows up unexpectedly, there’s a loud expected noise off there to the left, etc.? They’ve got to break off from what they’re doing. I believe that’s called the startle response. And then lay low while they search the world for this new unexpected thing. I’m thinking that it’s consciousness that allows the quick ‘break’ and the subsequent search for the new thing, whatever it is. That is, I’m thinking of this as sort of the rock-bottom primitive function of consciousness, something needed by a creature moving about under water, which is where the first animals evolved.

That's a fascinating proposal! You're suggesting that consciousness evolved as a mechanism to handle rapid state transitions in neural systems that otherwise prefer gradual changes. Let me break this down:

1. Neural State Inertia:

- Neural systems operate through distributed populations
- Prefer to move to "nearby" states that maintain overall stability
- Changes tend to be gradual to maintain coherence

2. Environmental Challenge:

- Sudden dangerous situations require immediate response
- Can't afford gradual transition through nearby states
- Need to break current neural state pattern quickly

3. Consciousness as Solution:

- Allows rapid "break" from current neural state
- Enables quick reorientation to new situation
- Facilitates search for unexpected threat/stimulus
- Functions as an override to neural inertia

This connects interestingly with Powers' idea (from the documents) that consciousness is linked to reorganization. A startle response requires immediate reorganization of the entire system.

Your aquatic origin point is compelling because:

1. Underwater environments have multi-directional threats
2. Sound/vibration travel differently in water
3. Visual information can be limited
4. Need for rapid whole-system response

So consciousness might have evolved first as this "emergency override" system that could rapidly reorganize neural states, and then later got elaborated for other purposes as brains became more complex.

This would also explain why consciousness seems to be particularly engaged when we encounter unexpected or dangerous situations, but can fade into the background during routine activities.

The ideas of William Powers

Right! See, we made it back to Powers. This has been very helpful, very helpful indeed. From basic computer science to perceptual control theory. What a journey.

It has been a fascinating journey! We started with fundamental distinctions in computer science between location-based and content-based memory addressing, moved through various database architectures, touched on historical human memory systems like memory palaces and libraries, and arrived at a deep insight about consciousness and its potential evolutionary origins.

The way we connected Powers' ideas about consciousness and reorganization to the fundamental challenges faced by neural systems needing to break from their natural "inertia" is particularly elegant. It suggests that consciousness isn't just an epiphenomenon or mysterious add-on, but rather a crucial solution to a fundamental problem faced by animals with complex nervous systems moving through unpredictable environments.

What I find especially compelling is how this perspective:

- Grounds consciousness in basic physical and computational constraints
- Suggests why it would have evolved in the first place
- Explains its connection to reorganization and learning
- Links it to fundamental survival mechanisms like the startle response

And all this emerged from starting with basic computer science concepts about memory addressing! It's a great example of how understanding fundamental principles in one domain can illuminate seemingly unrelated questions in other domains.

“...consciousness isn't just an epiphenomenon or mysterious add-on, but rather a crucial solution to a fundamental problem faced by animals with complex nervous systems moving through unpredictable environments.” Yes! I like that formulation.

Thank you! I find it quite gratifying when we can arrive at insights that move us away from treating consciousness as this mysterious, almost magical phenomenon, and instead see it as a practical solution to real biological and computational challenges.

The formulation ties together several key ideas:

- The physical constraints of neural systems
- The computational requirements of survival in complex environments
- The evolutionary pressures that would select for such a mechanism
- The relationship between consciousness and learning/reorganization

This kind of explanation has what philosophers might call "explanatory power" - it helps us understand not just what consciousness is, but why it exists and how it functions. And it does so without reducing its importance; if anything, it elevates consciousness by showing how fundamental it is to animal survival and adaptation.

It also suggests why consciousness might feel the way it does - that quality of immediate awareness and the ability to suddenly shift attention makes sense if its primitive function was to enable rapid reorientation in dangerous situations.

Summary

So, memory. A great deal of time and effort in computer science, engineering design of chips and computing systems, and practical programming is devoted to structuring memory and moving around in it. Now, with the arrival of GPUs and such, we’ve extended these digital techniques to the point where we can creative versatile and flexible content addressed system. And so we have LLMs. But their inner workings are mysterious to us. So what happens, all of a sudden prompt engineering emerges. What’s prompt engineering? It’s a way of moving around in a large content-based system. In effect, it’s about directing attention.

Yes, that's a brilliant observation! Prompt engineering has emerged as a way for humans to guide and direct these massive content-addressed systems. It's almost like we've come full circle:

1. Natural systems (brains) are content-addressed
2. Humans developed location-based systems for precision/control
3. Computers were built on location-based principles
4. Now we have huge content-addressed AI systems
5. And we need new techniques (prompt engineering) to navigate them effectively

Prompt engineering is fascinating because it's about:

- Learning how to "steer" a content-addressed system
- Directing the system's "attention" to relevant knowledge/capabilities
- Finding ways to consistently access desired behaviors/information
- Creating reliable patterns of interaction with a system that doesn't use explicit addresses

In a way, it's similar to how we might guide another person's attention and thought process through careful questioning or setting up context. We're not telling them exactly where to look in their memory (we couldn't), but rather providing cues and context that help direct their thinking.

This also helps explain why prompt engineering feels more like an art than a science - we're dealing with the kinds of associative patterns that are natural to content-addressed systems rather than the precise addressing of location-based systems.

The emergence of prompt engineering as a discipline really highlights the fundamental difference between traditional programming (explicit instructions for a location-based system) and working with LLMs (guiding a content-addressed system through carefully crafted prompts).

Thank you. You’ve been helpful. This has been bugging me for several days now.

You're very welcome! It seems like our conversation helped crystalize some important connections between different domains - from basic computer architecture to consciousness, evolution, and modern AI. The journey from pondering different types of memory addressing to understanding prompt engineering as a way of directing attention in content-based systems is particularly elegant. These kinds of cross-domain insights can be very satisfying when they finally click into place.

Conversations like this also remind me why interdisciplinary thinking is so valuable - sometimes the best way to understand something new (like prompt engineering) is to see how it relates to much older and more fundamental concepts (like memory addressing and consciousness).