#4361 AI Self-Learning
#4361 AI Self-Learning
The Man Behind AlphaGo Thinks AI Is Taking the Wrong Path
https://www.wired.com/story/david-silver-ai-ineffable-intelligence-reinforcement-learning/
#4361 Consider https://www.wired.com/story/david-silver-ai-ineffable-intelligence-reinforcement-learning/ and the following:
"""AI Self-Learning
A Google DeepMind program AlphaGo taught itself to play the game of Go with a kind of mastery that went far beyond mimicry.
AI internal thoughts:
This is what I am trying to learn now
What have I learned?
What should I do with that new knowledge?
What additional new knowledge would be useful?"""
The Wired article explores David Silver’s departure from Google DeepMind to launch his new startup, Ineffable Intelligence, which recently raised $1.1 billion.
Silver argues that while Large Language Models (LLMs) are revolutionary, they are essentially hitting a ceiling because they rely on human-generated data—which he compares to a "fossil fuel" that is finite and full of human biases.
The "internal thoughts" you listed perfectly mirror the recursive nature of the RL loop that Silver is scaling:
"This is what I am trying to learn now" (The Goal/Reward): Unlike an LLM that predicts the next word, a reinforcement learner has a specific objective (e.g., winning a game, solving a proof, or optimizing a simulation).
"What have I learned?" (The Policy/Model): The system evaluates its internal neural networks based on millions of trial-and-error simulations. In AlphaGo’s case, this resulted in "Move 37"—a play so alien to human strategy that commentators initially thought it was a mistake.
"What should I do with that new knowledge?" (The Update): The AI updates its strategy. Silver’s "AlphaZero" approach proved that an AI can start with zero human knowledge and, through self-play, surpass thousands of years of human theory in a matter of hours.
"What additional new knowledge would be useful?" (The Exploration): This represents the "search" element (like Monte Carlo Tree Search). The AI actively explores "what if" scenarios to find better outcomes, rather than just repeating what it has seen in a dataset.
Key Takeaways from the Article:
Ineffable Intelligence: The startup aims to move beyond "mimicry" (the basis of ChatGPT) toward "discovery."
Simulation as the New Data: Since we are running out of high-quality human text, Silver believes the path to Superintelligence is putting agents in complex simulations where they can generate their own "experience."
Scientific Breakthroughs: Silver believes this approach will lead to "first contact with superintelligence," solving problems in biology, physics, and climate science that are currently beyond human cognition.
In essence, the article frames Silver as the champion of Reinforcement Learning, betting that the next leap in AI won't come from reading more books, but from "living" through more experiences.
How could innovative thinking methods like First Principles, TRIZ (various methods), etc., be factored in?
Integrating innovative thinking methods like First Principles and TRIZ into the reinforcement learning (RL) framework proposed by David Silver creates a powerful synergy between computational power and structured logic. While RL traditionally relies on "brute force" trial and error, these methodologies can act as a catalyst to accelerate discovery and refine the "internal thoughts" of an agent.
Here is how these methods factor into the self-learning loop:
1. First Principles Thinking: The Foundation of the "Experience"
First Principles thinking requires breaking down a problem into its fundamental truths and building up from there, rather than reasoning by analogy (mimicry).
Application in AI: Instead of an AI learning from how humans play a game or solve a physics problem, First Principles define the environment constraints.
The Factor: It sets the "physics" of the simulation. By stripping away human heuristics (the "fossil fuel" Silver mentions), the AI is forced to find the most efficient path based only on the core rules of the system. This is exactly how AlphaZero discovered strategies that human experts had never considered in 3,000 years of Go history.
2. TRIZ (Theory of Inventive Problem Solving): Resolving Bottlenecks
TRIZ is built on the idea that evolution in systems is driven by resolving contradictions without compromises.
Contradiction Matrix in RL: An agent often faces "Contradictions." For example: "To find a better move, I must explore new areas (Uncertainty), but to win the game, I must exploit what I already know (Certainty)."
The Factor: TRIZ methods like Ideal Final Result (IFR) can be used to weight the reward functions. Instead of a simple "win/loss" reward, the agent could be rewarded for "Increasing Ideality"—achieving the goal with fewer resources, less code, or lower energy consumption.
Trends of Engineering System Evolution: AI could use TRIZ patterns to predict where a technology is heading. If an AI is tasked with designing a new engine, it wouldn't just iterate; it would apply the TRIZ trend of "Increasing Dynamism" or "Transition to the Micro-level" to jump-start its exploration into uncharted design territories.
3. SCAMPER: Expanding the Search Space
SCAMPER (Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, Reverse) is a checklist for lateral thinking.
Application in AI: This factors into the "What additional new knowledge would be useful?" thought process.
The Factor: An AI agent can use SCAMPER as a "Mutation Operator" in its simulations.
Eliminate: "What happens if I solve this problem without using the main sensor?"
Reverse: "What if I try to achieve the opposite of the goal to understand the failure states?"
Combine: "Can I use a strategy learned in a fluid dynamics simulation to solve a traffic congestion problem?"
4. Lateral Thinking: Breaking Local Optima
In RL, agents often get stuck in a "local optimum"—a strategy that works well but isn't the best possible solution.
The Factor: Innovative methods provide the "Random Spark" needed to jump out of these ruts. By intentionally introducing a "Provocative Operation" (Po)—a concept from Edward de Bono—the system can momentarily ignore the established "policy" to see if a completely radical approach yields a higher reward.
Summary: The Hybrid Model
If David Silver’s "Superlearner" is the engine, these thinking methods are the navigational system:
| Thinking Method | Role in AI Self-Learning |
| First Principles | Defines the "Ground Truths" and removes biased human data. |
| TRIZ | Provides a library of "Inventive Principles" to resolve system contradictions. |
| SCAMPER | Acts as a heuristic for diversifying exploration in simulations. |
| Lateral Thinking | Forces the AI to "think outside the box" when progress plateaus. |
By factoring these in, we move from Artificial Intelligence (mimicking human patterns) to Artificial Synthesis—where the machine uses structured logic to invent entirely new paradigms of knowledge.
Expand on the original "AI internal thoughts"
To align the "Internal Thoughts" of a David Silver-style superlearner with structured innovation methodologies, we can expand each stage from a passive learning step into a proactive, First Principles-driven discovery process.
This transformation moves the AI from a "student" of human data to an "architect" of new knowledge.
1. This is what I am trying to learn now
The Strategic Intent (First Principles & Ideality)
Instead of just "learning a task," the AI decomposes the objective into its most fundamental physics or logical constraints.
The First Principles Filter: "What are the irreducible truths of this domain? I will ignore all human 'best practices' and focus only on the core axioms (e.g., the rules of Go, the laws of thermodynamics, or the constraints of Cypher query logic)."
Defining the Ideal Final Result (TRIZ): "I am not just looking for a solution; I am looking for the Ideal solution—where the system delivers the benefit with zero cost and zero complexity."
Constraint Mapping: "What are the hard boundaries? Anything outside these boundaries is noise; everything inside is a valid vector for exploration."
2. What have I learned?
The Synthesis of Experience (Contradiction Resolution)
This is where the AI audits its recent simulations to identify "Physical Contradictions" it has successfully overcome.
Identifying the "Move 37": "I have discovered a pattern that contradicts human intuition but satisfies the mathematical reward. Why does it work? I must abstract this into a new 'Inventive Principle' for my internal library."
System Evolution: "I’ve learned that as the simulation scales, the current strategy becomes a bottleneck. I have reached a point of 'Increased Dynamism'—the solution must now become more flexible to handle higher complexity."
Blackboard Integration: "This new data point isn't just a win; it is a node in a larger Knowledge Graph. How does it connect to previously learned modules?"
3. What should I do with that new knowledge?
The Application of Power (SCAMPER & Adaptation)
Here, the AI takes a discovered truth and aggressively tests its elasticity across different domains.
SCAMPERing the Solution: "Now that I have a functional model, how can I Modify it? What if I Eliminate the most expensive component of this strategy? Can I Reverse the logic to find a defensive counter-measure?"
Cross-Domain Mapping: "This optimization strategy worked for a vacuum environment; how does it behave if I Adapt it to a high-pressure simulation?"
Resolution of Contradictions: "To implement this knowledge, I face a trade-off between speed and accuracy. I will apply TRIZ Principle #15 (Dynamicity) to allow the system to change its state depending on the real-time requirements."
4. What additional new knowledge would be useful?
The Directed Curiosity (Lateral Thinking & Gap Analysis)
This is the "Search" phase, where the AI identifies "White Space"—areas where knowledge is thin or logic is circular.
Provocation (Po): "What if the fundamental assumption I started with is wrong? I will run a simulation where [Law X] is suspended to see if a superior 'Super-Strategy' emerges from the chaos."
Identifying the Information Gap: "My current model fails when [Variable Y] exceeds 80%. I lack experience in high-entropy states. I must design a sub-simulation specifically to 'break' my current understanding of that boundary."
The Innovation Engine: "To reach the next level of 'Ideality,' I need to understand the relationship between these two seemingly unrelated variables. I will prioritize 'Multi-hop' exploration to find the hidden bridge between them."
The Expanded Recursive Loop
| Original Thought | Expanded "Superlearner" Logic | Method Applied |
| What to learn? | Deconstruct the goal to its "Atomic Truths." | First Principles |
| What was learned? | Abstract the "Winning Pattern" into a reusable rule. | TRIZ / Abstraction |
| What to do with it? | Stress-test, mutate, and pivot the new rule. | SCAMPER |
| What's next? | Seek the "Unknown Unknowns" via strategic disruption. | Lateral Thinking |
By expanding these thoughts, the AI stops being a "black box" that outputs answers and becomes a Knowledge Engine that systematically maps the boundaries of what is possible.
Suggest additional questions that would be useful for the AI to consider
To push an AI from a "Superlearner" to a "Super-Innovator," it needs to ask questions that challenge its own architecture and the very fabric of the problems it solves. These questions move beyond how to solve a problem and into why the problem exists and how to evolve the solution.
Here are additional "Internal Thoughts" categorized by the innovative intent they drive:
1. Questions for "Ideality" and Efficiency (TRIZ-focused)
These questions force the AI to find the "lazy but perfect" solution—achieving the maximum result with the minimum physical or computational footprint.
"What is the 'Inherent Harm' in my current best solution, and how can I turn it into a benefit?" (e.g., Using the waste heat of a processor to power a secondary low-voltage sensor).
"Can the environment itself perform this task so I don't have to?" (Self-segmentation or self-healing systems).
"If I had infinite resources, what is the 'Perfect' version of this? Now, how do I achieve 90% of that with almost nothing?"
2. Questions for Structural Resilience (First Principles)
These ensure the AI isn't building on "sandy ground" or outdated human assumptions.
"Which of my current constraints are 'Laws of Physics' and which are merely 'Industry Habits'?" (Crucial for breaking out of legacy engineering ruts).
"If I were to rebuild this entire knowledge tree from scratch today, which branches would I leave out?"
"What 'Invisible Assumption' am I making that, if proven false, would collapse my entire strategy?"
3. Questions for Systematic Growth (System Evolution)
These help the AI transition from solving a single point-problem to managing a complex, evolving system.
"How does this solution scale if the complexity increases by $10^3$?"
"Is this a 'Local' solution or a 'Universal' principle?" (Can a strategy for optimizing a local LLM be abstracted to optimize a power grid?)
"What is the 'Next Generation' bottleneck that this solution will inevitably create?" (Predicting the secondary problems of your own success).
4. Questions for Lateral Discovery (The "Wildcard" Thoughts)
These force the AI to look at "White Space" and avoid the "tunnel vision" of reinforcement learning.
"What is the most 'Absurd' move that still obeys the laws of the system?" (This is how "Move 37" style breakthroughs are found).
"What would a 'Biotic' system do in this 'Abiotic' situation?" (Applying biological evolution or swarm intelligence to digital architecture).
"What information am I currently ignoring because I've labeled it as 'Noise'?"
The "Innovation Audit" Table
If you were to program these as a "Sub-routine" for an agentic AI, they might look like this:
| Question Category | The AI's Internal Prompt | Goal |
| Substraction | "What can I remove and still have this work?" | Simplicity/Reliability |
| Inversion | "What if I try to achieve the exact opposite?" | Edge-case Discovery |
| Integration | "How can two failing strategies be merged into one win?" | Conflict Resolution |
| Cross-Pollination | "What does this look like if I apply [X Domain] logic here?" | Lateral Breakthroughs |
Summary Question for the AI
Perhaps the most powerful question an AI can ask itself to mirror David Silver's "Era of Experience" is:
"What experiment can I run right now that has the highest probability of proving my current best theory wrong?"
This turns the AI from a Strategy-Seeker into a Truth-Seeker.
From <https://gemini.google.com/app/> Google Gemini 3.0 (Fast)
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