"The Turing Option" and the 2026 AI

the-turing-option-vs-2026-ai.md

"The Turing Option"	Modern AI/LLM Equivalent
Agents (mindless basic units)	Individual sub-agents. Tools, skills. Mixture of Experts (MoE).
Agent Societies (agents coordinating together)	Multiple agents working together, Counsil of LLMs
A-brain (primary processing)	Worker agents, based on more simple LLMs
B-brain (monitoring supervisor)	Planning agents. Gating Mechanisms in NN.
C-Brain (A higher-level monitor that watches the B-Brain to ensure it doesn't become stuck in its monitoring duties.)	???
Struggle for power between A/B-brains	Adversarial Training / GANs
Superego Template	Directly trained / Hard-coded filters / Anthropic Constitutional AI)
Papert’s Principle	Detects hallucinations and triggers Google Search as a third-party arbiter.
K-lines (Knowledge-lines)	Skills.
K-line reactivating partial mental states	Reading relevant docs and using relevant tools from skills.
Nemes (Connection Agents)	Token Embeddings / Semantic Vectors
Nemes automatically activating related concepts	Embedding Similarity / Semantic Relatedness -- related concepts may influence results due to attendtion design
Semantic Networks	Knowledge graphs, not very popular
B-CRAM (Content-Addressed Memory)	Vector Databases / Retrieval Systems
Memory conversion: short→long term	-
Memory backup and restoration	-
LAMA (Language for Logic and Metaphor)	- ( domain-specific languages for the feature of the preious "summer" of AI )
Self-referential processing (LISP basis)	Recursive Language Models [https://arxiv.org/abs/2512.24601]
Parallel processing support	Agents running in background in parallel
CYC (Encyclopedia Database)	LLM pretraining.
Rule-Based Agent Systems	Not emphasized now (but they were popular as part of expert systems back then), but may be implemented as tool if needed
Fuzzy Logic and Uncertainty	We have some sort of uncertainty inside LLMs but not in the academic sense of 1990s
Inhibition Systems	Attention Masking, Gating Mechanisms, agentic guardrails
Rewarding successful, inhibiting failed subunits	Only at LLM level, not at agents level now
Knowledge-based manager learning strategies	You can force to "Claude" to add something to memory, but it doesn't guaranty that it will work
Conflict detection and resolution	I didn't see good examples
Three-eye error-checking system	You can use "Gemini" to check "Claudes" code and vice versa
Self-Reinforcement Learning	GAN ?
Experience-based strategy selection	Memory use?
Internal Computation Tracing	Mechanistic Interpretability / Attention Visualization / Anthropic and Google analytic tools
Conflict Detection	???
Logic + Metaphor Language	As I know few are doing separate logic reasoning. Why?..
System Learning from Experience	???
Autonomous Self-Repair	???