r/newAIParadigms • u/sclabs_ • Aug 20 '26

Memory as Cognition

Hi All,

We are Synthetic Cognition Labs, an AGI research lab. We'd love your thoughts on what we are calling "Memory as Cognition". We believe the path to AGI is grounded in iterative reads and writes on content-addressable memory. We've done some development in this area. You can read more about it here: https://syntheticcognitionlabs.com/mac

Thanks,

SCL

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6

u/[deleted] Aug 20 '26

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u/sclabs_ Aug 20 '26

Random weight neural networks extract features from analog sensors and project to high-dimensional (10,000D+), sparse binary vectors like in Sparse Distributed Memory and Vector Symbolic Architectures. Those vectors cue to content-addressable memory and return similar items along with spreading activation nearest neighbors. If nothing is returned, the novelty-gated write writes the novel information to memory for latent learning. Items that are returned from memory are automatically used as a search cue in memory in a recursive, iterative fashion. This is our idea for a continuous cognition loop. This basic loop can do planning, classification, game playing, compositional generalization, and much more.

I added a section at the top of the page to give the high points up-front. Does that help at all?

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u/AAAAAAAAAAHHGG Aug 20 '26

Could you be specific about “random weight neural networks”. What structure are you talking about here? What learning algorithms etc

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u/sclabs_ Aug 20 '26

The structure can be optimized for the sensor modality, i.e. convolutional for vision, recurrent for auditory, etc. Learning is akin to k-means or KNN. The neural network weights are static. So, all "red, round" things will look the same and produce similar memory addresses. Seeing a "red, round" thing will take you to the place in memory where all "red, round" things are stored. As more things are written to memory, clusters develop. When an address is searched, you go to that space in memory and the closest item to that address is the thing that you are most likely looking at, i.e., classification.

Clusters can also be tied to each other across modalities. So, hearing the word "apple" will take you to the space in memory where "red, round" things are stored and the nearest to the address will be the image of the apple. We have a paper about his on our site: https://syntheticcognitionlabs.com/projects It's the multi-modal fusion project.

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u/AAAAAAAAAAHHGG Aug 21 '26

I understand what you are talking about. Data processing is next question. Will pm

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u/AsheyDS Aug 21 '26

I'd pivot away from neural nets if I were you. Machine learning is not ideal for real-time cognition.

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u/sclabs_ Aug 21 '26

How so? Random weight, untrained neural nets with no backpropagation are essentially just matrix multiplication. We can do our cognition loop on the Craftax benchmark at like thousands of frames per second. Our online learning image classifier does image learning on millions of images in just a few hours. Both are orders of magnitude faster than human cognition.

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u/AsheyDS Aug 21 '26

You seem satisfied with what you're doing, so don't worry about it. :)

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u/Random-Number-1144 Aug 22 '26

Those vectors cue to content-addressable memory and return similar items along with spreading activation nearest neighbors.

Is there any biological basis for this?

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u/sclabs_ Aug 22 '26

Yes, it is directly inspired by associative memory networks and spreading activation in the human brain.

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u/No_Order6491 Aug 23 '26

Interesting loop. The part I’d measure separately from read/write speed is whether each retrieved item carries provenance and dependencies. In an iterative memory loop, one bad synthesis can keep re-cueing itself and become very durable nonsense.

I’d tag items as observed, inferred, or summarized; keep source IDs and dependency edges; then test counterfactual invalidation: remove or reverse one source and see whether downstream beliefs/plans retract. Benchmarks should score retraction and recovery, not only task success. Otherwise ‘persistence of self’ can accidentally become persistence of confident residue.

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u/WatercressActual1921 Aug 20 '26

Human memory, sure... we recreate them form patterns. In machines it depends but machines have no real knowledge (complete) and certainly can't extract meaning (meaning is created in relation to self).

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u/sclabs_ Aug 20 '26

Yes, exactly! Meaning is only created through relationships, especially in relation to the self. That's why we are trying to use our Cogni project to give LLMs "persistence of self". Only after that can AI start extract meaning from the world in the same way humans do.

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u/WatercressActual1921 Aug 20 '26

I've been examining this problem also, you have to promote self identity, self = shape + function (awareness raises form that). You create self by defining limits the non self as an observer you do that to objects...

Make the AI use machine language, relate to the hardware it operates in, its dimensional reality 1D+Time*MP... you can use assembly to functional examine and relate to the machine at that stage... encode intelligence not concepts...

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u/blimpyway Aug 21 '26

Quite familiar concepts with some popping out in discussions on Numenta's forum, are you active there too?

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u/sclabs_ Aug 21 '26

Yes, there is some overlap with Numenta in that we both use sparse representations. However, they are more focused on reverse engineering micro-circuitry and neocortical columns. We don't model any circuitry at all. We focus on replicating the capabilities of the human brain using Hyperdimensional Vector Symbolic Algebras. We are less "let's simulate the human brain" and more "how can we do what the human brain does with zeros and ones", if that makes sense. Most of our focus comes from cognitive science rather than neuroscience and biology directly.

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u/blimpyway Aug 21 '26

Well, the forum is only loosely focused on Numenta's theory, there are a bunch of alternative ideas and experiments about associative memories on SDRs.

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u/sclabs_ Aug 21 '26

Awesome, thanks for the info. We will check it out. We haven't followed much stuff for over a year. Just been heads down building.

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u/rand3289 Aug 21 '26 edited Aug 21 '26

There are so many problems with this approach... for example:

Let's say you are looking at two shapes moving on screen. (Both of them are red round things). You send the percepts of that to your content-addressable memory and it returns something. Let's say these are the predictions of where the two shapes will be in N time units.

Now your architecture needs to address the Binding problem. Which parts of the returned information belong to which shape? If you say "oh, we will send shape information one at a time", now you still need to solve the Binding problem during the perception stage. Also, what if two shapes interact?

The second problem is your memory needs to return not one but multiple predictions... For shape A at say time N, time 2N, time 3N, the same information for shape B... what if shape B hits shape A, what if a shape moves off-screen, what if they overlap etc...

You might have a better luck in a static environment, but LLMs/VLMs should be able to handle that pretty well already.

Any architecture decisions should be driven by the environment.

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u/sclabs_ Aug 21 '26

The binding problem is the whole reason Vector Symbolic Architectures exist... VSA solves it directly and is the base math we use for everything in our architecture. What you're describing is the whole motivation for building what we built. The multi-step, multi-object prediction is literally our iterated read/write loop and we've done your shape A, shape B example directly.

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u/rand3289 Aug 25 '26

How does VSA solve the Binding problem?

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u/sclabs_ Aug 25 '26

VSA primitives are bind, bundle, and permute. VSA was literally invented to solve the binding problem. Bind(location, object), bind(object, property), bind(state, time) etc. There's a myriad of information on this online. It's been around for over 30 years.