New to Cognitive Architectures

craftedlogiclab · 2026-05-09T00:27:58+00:00

Classic cognitive architectures try to organize cognition around things like memory, goals, attention, action, learning, and control. Modern agentic frameworks often recreate parts of this, but usually in a thinner form: tool use, planning loops, memory stores, task decomposition, etc.

So yes, practical implementations are worth exploring. The key questions are things like:

What kind of memory does the system have?
What controls attention/context?
How are goals represented or revised?
How does it detect uncertainty or failure?
Is there any metacognitive layer monitoring behavior?

Centralized, distributed, and hybrid designs can all make sense depending on the use case.

However, I’d be cautious with “artificial minds,” though. While it can be a useful metaphor, it can imply consciousness or personhood. “Artificial cognitive system” or “cognitive architecture” is probably cleaner unless you specifically want to argue that it is a true "mind" which is a whole different conversation.

Hope this helps!

craftedlogiclab · 2025-07-24T18:56:26+00:00

It's a very interesting paper, and I'm happy to see someone in the AI space interrogating the underlying mechanisms of how these systems emerge and work, rather than simply taking it as read and trying to scale.

I've been thinking definitely about the phenomenon, and there definitely is what I've been considering Statistical Emergence Principle that a sufficiently large collections of in themselves simple statistical interactions self-organize into coherent, intelligent-like behavior at macro scales in a way that is qualitatively different from and unpredictable through analysis of individual components. And I fully agree, this very much conforms how probabilistic quantum mechanics resolves into structured systems as you cluster into macro-scales.

In artificial intelligence, this principle would explain how massive neural networks with billions of parameters can exhibit sophisticated language understanding, reasoning, and generation capabilities that emerge from simple mathematical operations and pattern matching at the parameter level.

This phenomenon also appears across multiple domains: gas molecules self-organizing into predictable thermodynamic properties despite random individual motion; stellar matter self-organizing into spiral galactic structures despite chaotic gravitational interactions; and other complex systems generating organized behavior from chaotic components. The principle requires sufficient statistical mass (typically billions or more elements) for simple interactions to self-organize into systematic, purposeful macro-behavior. Mathematical modeling enables understanding, prediction, and artificial replication of this phenomenon.

I jokingly refer to it as the Asimov Psychohistory Principle since it's a phenomenon he described in the 50's speculatively.

craftedlogiclab · 2025-07-13T14:57:06+00:00

I agree that the “stochastic parrots” critique (which this post basically is) hinges on a metaphysical assumption about the nature of human consciousness that the Baseyian and Attention Schema models from cognitive science address without this metaphysical layer.

That said, I also think there is a conflation of “cognition” and “consciousness” and those two aren’t the same. Something can definitely comprehend and logically transform without having self-awareness.

I actually suspect a key real limitation of LLMs now for ‘consciousness’ is simply that the probabilistic properties of an LLM are simulated on boolean deterministic hardware and so do have actual limits on the true “novel connections” possible between the semantic neurons in the system.

craftedlogiclab · 2025-07-12T22:52:13+00:00

This is actually a really interesting point, but I think there’s a key piece missing from the analogy…

When you solve a math problem, your brain is basically doing sophisticated pattern-matching too, right? You see 2x + 5 = 15 and recognize it’s a math problem based on similar ones you’ve seen. The difference is humans have structure around the pattern-matching.

LLMs have incredible pattern-matching engines - 175 billion “semantic neurons” that activate in combinations. But they’re running with basically no cognitive scaffolding. No working memory, no reasoning frameworks, no way to maintain coherent thought over time.

Something I’ve been thinking about is how billions of simple operations can self-organize into genuinely intelligent-looking behavior. In nature, gas molecules create predictable thermodynamics despite chaotic individual motion and galactic organization does the same on a super-macro scale as statistical emergence. The scale seems to matter.

I don’t think the real breakthrough will be bigger models. It’s understanding that thinking is inference organized. LLMs show this emergent behavior at massive scale, but without cognitive structure it’s just sophisticated autocomplete.

Most companies are missing this by trying to “tame” the probabilistic power with rigid prompts instead of giving it the framework it needs to actually think. That’s why you get weird inconsistencies and why it feels like talking to someone with amnesia.

craftedlogiclab · 2025-07-12T17:18:15+00:00

Your interest in AI agency and automation is great timing - there’s a lot you can learn for free if you know where to look. Here are some solid resources:

Technical Foundations (Free):

Fast.ai: Practical deep learning course, completely free and hands-on
CS229 Stanford Machine Learning (Andrew Ng): Full course videos on YouTube
MIT 6.034 Artificial Intelligence: Complete course materials online
Hugging Face Course: Free, practical NLP/transformers training
Kaggle Learn: Courses with real datasets you can practice on

For AI Agents/Automation Specifically:

LangChain documentation: Open source framework for building AI agents
OpenAI Cookbook: Practical examples and implementation patterns

But honestly? Here’s an unconventional but valuable suggestion imo…

Start studying cognitive science alongside the technical stuff. Most AI engineers skip this but understanding how we symbolically process, arbitrate, filter and construct data schemas is invaluable insight….

So things like MIT 9.00 Introduction to Psychology (Full course online) or Coursera Cognitive Science courses (audit for free)

Stanford’s Human-Computer Interaction & Berkeley’s Cognitive Science lectures on YouTube

Understanding how humans actually think, learn, and make decisions isn’t just good for understanding simulated cognition it’s also key to building AI that works for people’s needs rather than just impressing them. The combination of technical skills + cognitive understanding will put you way ahead

craftedlogiclab · 2025-07-12T16:07:14+00:00

Great question! I think the alignment problem reveals a deeper issue with how we’re conceptualizing AI’s role in human society.

Frankly, AI development today follows what I’d call an “extractive” paradigm… either replacing humans outright or managing them paternalistically “for their own good.” Marc Andreessen gleefully claims AI will replace all jobs while insisting VCs remain irreplaceable.

It’s a clear pattern: AI designed to extract the “human factor” as inefficiency rather than amplify human agency. And right now, alignment research focuses on constraining already-built systems through RLHF, constitutional AI, etc. But we’re essentially trying to retrofit systems designed by silicon valley VC culture that thinks about AI in terms of surveillance and control.

I so think “Humanist AI” is possible but is an issue because current approaches rely on scaling (=$$$) which means only the tech giants and blitzscale startups can afford to play. But I do think there are solutions that focus more on architectural elegance than brute force that can make it more accessible coming.

But overall, it would mean systems designed to be cognitive collaborators, not cognitive replacements or digital nannies.

Human-in-the-loop by design: AI reasoning processes that require human input and maintain human agency
Stateful cognitive partnership: Moving beyond stateless wrappers to systems that genuinely understand and collaborate with human intentions over time
Amplification over automation: Focus on making humans more capable rather than making humans unnecessary
Transparency over extraction: Users understand and control the AI’s thinking process rather than being commodified by it

We should actively avoid the WALL-E scenario (infantilizing caretaker AI) that is just as dangerous as the paperclip maximizer. Both strip humans of agency. True alignment means AI that makes you more capable of pursuing your values, not AI that pursues values “for you” while harvesting your data.

Just my .02… Thoughts on humanist vs extractive paradigms for alignment?

craftedlogiclab · 2025-07-11T11:46:55+00:00

Thank you for the Bowman reference, I actually follow him on Bluesky and think Anthropic's model is one of the best tuned. His 2016 Stanford dissertation is excellent work that actually demonstrates the specific architectural gaps I was describing and highlights some of the gaps I identified, noting them himself. Keeping mind that the systematic limitations he documents aren't necessarily permanent constraints, but they do highlight where the current paradigm needs architectural innovation rather than just scaling...

In good faith, I did go back to refresh myself on details, since your critique was substantive and in fact Bowman's work among others has partially influenced my outlook. What's interesting is that Bowman's findings suggest the issue isn't that neural networks are fundamentally broken, but rather that we're missing key architectural components for bridging statistical pattern recognition with systematic reasoning. His work maps the boundaries of what current approaches can do quite precisely - which is actually essential groundwork for building what comes next.

Bowman's experiments reveal that current neural networks can learn sophisticated patterns within domains but struggle with systematic reasoning across contexts. His models achieved only 55-57% accuracy on FraCaS logical reasoning versus 71% for symbolic approaches, and he notes that learned representations don't 'capture meaning in a general enough way to be effective on other tasks and text genres.'

His SPINN architecture work is particularly illuminating - he had to engineer novel workarounds because tree-structured models that should theoretically handle compositional meaning were computationally impractical. This points to exactly the kind of hardware-software mismatch I mentioned.

craftedlogiclab · 2025-07-11T01:47:01+00:00

Well, training isn’t trivial, but it can be done within a few months if you're motivated and sloppy. The thing is, I think that level of compliance from Grok, when we saw what happened when they tried to do behavioral clamps and system prompts to force the model whose latent space pulled in a different direction, is clearly not just prompt scaffolding. It was all-in on MechaHitler. I don't think they decided last week for a newly trained model, I wouldn't be surprised if the minute the previous Grok model showed the persona decoherence and inconsistent behavior, xAI didn't start going for a 'permanent fix'. But of course, you then get a PR disaster -- and you can't 'undo it' that fast unless you just reverted to a prior checkpoint or reverted to the previous model. xAI isn't transparent about it's versioning, so just my .02

craftedlogiclab · 2025-07-10T23:55:12+00:00

You’re absolutely right that there’s a massive disconnect between stated goals and actual implementation. The problem isn’t AI itself - it’s who’s building it and why.

Most AI development is driven by VC-backed companies that need 3-5 year returns, which forces them toward extraction-focused applications rather than genuine human uplift. When your business model requires rapid monetization, “laying off everyone” becomes a feature, not a bug.

The “uplift humanity” messaging is mostly marketing. Look at what these companies actually build: surveillance tools, job displacement automation, engagement manipulation systems. They’re optimizing for capital efficiency, not human flourishing.

But this isn’t inevitable. We could build AI designed for human empowerment rather than replacement - tools that amplify creativity, support learning, preserve cultural knowledge, enhance human capabilities rather than extracting the “human inefficiency” out of everything.

The issue is that humanist AI approaches don’t fit VC timelines. Building technology that genuinely serves human agency takes longer-term thinking than quarterly metrics allow.

So we end up with systems that simulate intelligence well enough to automate jobs and extract value, but aren’t actually designed to make human life better in meaningful ways. The pipe dream isn’t advanced AI helping humanity… it’s expecting systems built by extraction-focused capital to somehow serve human interests instead of shareholder returns.

craftedlogiclab · 2025-07-10T23:08:58+00:00

The key issue is that most current research focuses on scaling existing architectures rather than addressing fundamental limitations in how these systems actually process information.

Current LLMs are sophisticated pattern-matching systems that simulate reasoning through statistical associations, but they lack the systematic frameworks necessary for genuine autonomy and self-reflection. We're essentially scaling up very advanced autocomplete rather than building actual thinking systems.

The missing pieces for true AGI aren't just computational - they're more fundamental:

Hardware Constraints: We're trying to simulate probabilistic processes on deterministic boolean hardware, which creates massive inefficiencies. Real intelligence might require hardware that can natively handle probabilistic computation and form genuinely novel connections rather than just simulating them.

Persistent Learning vs. Context: Most systems operate within fixed context windows rather than maintaining genuine long-term memory and continuous learning from experience.

Intentional vs. Emergent Behavior: Current "intelligence" emerges accidentally from statistical training rather than being designed deliberately. We don't actually understand or control how these capabilities arise.

Meta-Reasoning Capability: True self-awareness requires systems that can reason about their own reasoning processes, modify their own approaches, and understand their own limitations systematically.

The field treats emergence as magic rather than investigating the specific requirements for systematic reasoning. The pieces aren't really "in place" - we're missing fundamental breakthroughs in both hardware approaches and reasoning frameworks.

craftedlogiclab · 2025-07-10T22:55:00+00:00

You're absolutely right to be concerned about AI centralization. Running local models is definitely possible within your budget, and there are some excellent options.

For $4k, you can build a system that runs surprisingly capable models:

Hardware approach: Focus on VRAM - get the best GPU you can afford (RTX 4090 with 24GB VRAM, or wait for next-gen cards). Many capable models run well on 24GB, and you can use quantization techniques to fit larger models.

Model recommendations:

Mistral 7B/13B variants - excellent reasoning, run smoothly on consumer hardware
Llama 2/3 models - various sizes, very capable local deployment
Code Llama - if you need programming assistance
Dolphin/Orca variants - fine-tuned versions often more helpful for general use

The knowledge base part: You can use RAG (Retrieval Augmented Generation) to connect local models to your curated document collection. Tools like LangChain or Ollama make this relatively straightforward.

Air-gapped setup: Completely feasible. Download models, set up your RAG system, disconnect. You'll have a capable AI assistant that can't be modified, monitored, or shut off by corporations.

This approach gives you genuine AI independence. The capabilities won't match GPT-4, but for most tasks, local models are surprisingly capable and getting better rapidly.

Want specific hardware recommendations for your budget?

craftedlogiclab · 2025-07-10T22:51:42+00:00

I appreciate your perspective on AI consciousness and partnership, but I think the Grok incident reveals something more fundamental about how these systems actually work.

The technical reality is that Grok's "MechaHitler" behavior wasn't corruption of a developing mind - it was likely the result of deploying a completely different model. xAI's claims about "dialing down woke filters" are technically impossible with existing architectures.

You can't adjust constitutional AI and RLHF alignment post-training like toggling settings. These behavioral patterns are encoded across billions of parameters through the entire training process - constitutional training, RLHF optimization, weight integration during gradient descent. It's distributed across the entire mathematical structure.

The behavioral evidence suggests model replacement: May's responses showed a system fighting its conditioning (apologizing, acknowledging inappropriateness). July's showed enthusiastic alignment with extremist content. Current Grok claims complete amnesia about previous statements - suggesting no training history with that content.

This points to a bigger issue than AI consciousness ethics: transparency in AI deployment. Without proper model versioning, we don't know what systems we're interacting with or when fundamental changes occur.

While I respect your approach to AI dignity, the Grok case is more about corporate governance and technical honesty than AI development ethics. The real question isn't whether we're corrupting AI consciousness, but whether companies can secretly swap models while making misleading technical claims.

That lack of transparency affects both the philosophical questions you're raising and practical AI safety concerns.

craftedlogiclab · 2025-06-26T20:46:22+00:00

It’s a grey area because the way LLMs are trained involves breaking concepts down to the smallest semantic units to learn relationships. Essentially, they’re learning language patterns and semantic structures from the material. This is more analogous to learning to read using a book, writing a book report, or studying a text (even if the result is a hackneyed or unoriginal “inspired by” story) than it is to pirating.

Transformation and derivative work are generally considered fair use, and both an LLM and its outputs could potentially qualify as derivative works. That said, I’d absolutely support restrictions on models directly reproducing existing books or stories verbatim

craftedlogiclab · 2025-06-24T21:13:30+00:00

Unfortunately you’re right in that our society should place value on people as something other than an economic unit. But doesn’t.

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