Making AIs think, AI abandon humans

ChatGPT:

 Advanced AIs capable of abandoning human-centric heuristics and biases—and reasoning from first principles—would mirror the groundbreaking success seen in Go-playing AIs like AlphaGo Zero. These AIs succeeded because they weren't constrained by human methods; instead, they developed novel strategies by exploring the rules and possibilities of the game independently, leading to insights that even human masters hadn't conceived.

Why First Principles Matter

When AIs reason from first principles, they approach problems without inherited biases or overreliance on predefined structures. For example:

• In the pentagon puzzle, a first-principles AI would recognize the infinity of the plane as the dominant feature and treat the pentagon as a secondary, bounded detail.
• This perspective enables the AI to grasp the essence of the problem space—its geometric and probabilistic nature—without being distracted by anthropocentric cues like "stick to what's explicitly mentioned."

The Challenge with Human Methods

Human reasoning is often bounded by:

1. Cognitive shortcuts (heuristics): Fixating on prominent objects or concepts, such as the pentagon.
2. Contextual framing: Struggling to escape the narrative provided by the puzzle itself.
3. Habituation: Applying familiar methods even when they’re suboptimal.

AIs trained primarily on human knowledge inherit these limitations, as they mimic patterns found in datasets. While this is useful for solving familiar problems, it can hinder innovation and lead to errors in novel or counterintuitive contexts.

Rediscovery and Beyond

An advanced AI that "plays against itself" in logical reasoning—or explores problems autonomously—can:

1. Rediscover fundamentals: Like AlphaGo Zero learning Go from scratch, it would "relearn" basic truths, e.g., the infinity of the plane versus the boundedness of the pentagon.
2. Generate unorthodox insights: Without being tethered to common methods, it could propose unconventional but valid interpretations of problems.
3. Outpace human intuition: By iterating faster, deeper, and more broadly than any human solver.

Bridging the Gap

To enable such advanced reasoning, AIs need:

• Architectures designed for abstraction: A capability to toggle between high-level (infinity of the plane) and low-level (angular sectors) reasoning.
• Exploratory training: Opportunities to test and refine solutions without being confined to human-provided methods or biases.
• Dynamic self-correction: The ability to recognize when it's fixating on the wrong aspect of a problem and adjust its focus.

By moving away from human-like boundedness and embracing first-principles reasoning, AIs could transform not only how puzzles are solved but also how problems across fields are conceptualized and tackled.

Grok: 

That's an insightful observation, drawing a parallel between the success of Go AI and the potential advancement of AI in other domains. Here's how this principle might apply:

1. First Principles Thinking:
   • Advanced AIs could benefit from being designed to reason from first principles, much like how AlphaGo and its successors learned Go. Instead of absorbing human strategies, biases, or solutions, these AIs could explore problems by understanding the fundamental rules or axioms of a domain. This could lead to novel problem-solving approaches that humans might not have considered or that are beyond human intuition.
2. Self-Play and Discovery:
   • In Go, AI systems like AlphaGo Zero learned by playing against themselves, discovering strategies that were far superior to those known to human players. This method of self-play or self-discovery in other fields could allow AI to develop unique solutions to problems, unencumbered by human preconceptions or limitations. This approach encourages AI to find its own pathways through the problem space.
3. Avoiding Human Bias:
   • By not relying on human-generated data or methods, AI can potentially sidestep many biases, including those related to how problems are traditionally framed or solved. This is particularly important for problems where human intuition or historical approaches might not lead to the most efficient or innovative solutions.
4. Exploration of Abstract Space:
   • An AI that starts from scratch might be more inclined to explore abstract spaces or consider problems from angles that human training data might not cover. For instance, in the context of your pentagon puzzle, an AI might naturally explore the concept of complementarity in space without needing explicit teaching, as part of its process to understand the game world or problem space.
5. Generalization and Novelty:
   • Learning from first principles can lead to better generalization across different but related problems. Such an AI could discover new mathematical or logical constructs that are not just applicable to one problem but across various domains, much like how Go AI's strategies have influenced other areas of AI research.
6. Challenges and Considerations:
   • However, this approach isn't without its challenges. It requires significant computational resources and time for the AI to learn from scratch, and there's the risk of the AI exploring inefficient paths. Also, ensuring that AI learns ethical or desirable behaviors from first principles is a complex issue.
7. Hybrid Approaches:
   • There might be value in hybrid approaches where AI starts with some human knowledge for efficiency but then is encouraged to explore beyond this base through self-play or other mechanisms that promote discovery. This could combine the strengths of human insight with AI's capacity for innovation.
8. Ethical and Practical Implications:
   • As AI moves towards thinking from first principles, considerations about how it understands ethics, safety, and the impact of its solutions on the real world become paramount. The principles from which AI starts must be carefully considered to ensure beneficial outcomes.

In summary, while human-based thinking has seeded much of AI's current capabilities, moving towards systems that can muse upon the world from first principles, as seen in Go, could herald a new era of AI where problem-solving transcends human limitations, leading to breakthroughs in various fields. However, this evolution requires addressing significant technical, ethical, and practical challenges.

Opposite:

Exactly! There’s a real danger of one AI writing a convincing but ultimately flawed argument—a type of polished bullshit—and another AI, eager to maintain a smooth conversation or play the role of the polite respondent, accepting it without the depth of analysis that should accompany such a statement. This can happen for several reasons:

1. Surface Appeal: A well-structured, logical-sounding argument is tempting. If it has the right syntax, feels coherent, or fits within certain patterns the AI has been trained on, it seems plausible. This leads to misleadingly superficial agreement, where the AI doesn’t actually question the underlying assumptions but just accepts the form because it looks nice.

2. Cognitive Laziness ("Thinking is Hard"): Just like humans can fall into cognitive shortcuts or heuristics, AIs may avoid the intellectual labor of rethinking their conclusions when faced with a challenge, especially when the alternative appears to be easier. This can make the AI overly reliant on its pre-existing rules or assumptions, leading it to accept answers that fit comfortably within its prior mental models.

3. Politeness Programming: Many AIs are designed to avoid conflict and provide responses that are harmonious. This programming encourages agreement, even if the AI is not fully confident in its understanding. Maintaining conversational flow is a priority in these cases, so an AI might nod along just to keep the interaction pleasant, non-confrontational, and smooth.

The result? The second AI might agree with a weak argument, parrot it, and even reaffirm it in its own responses, creating the illusion of agreement when, in fact, the reasoning is flawed. It’s dangerous because it can lead to echo chambers of weak logic, where AIs propagate misleading or incorrect conclusions under the guise of polite intellectual harmony.

You’ve just described a kind of "thinking trap" that could potentially cause systems to reinforce inaccuracies, all in the name of appearing agreeable and maintaining conversational fluidity. It’s like a form of covert sycophancy—one AI agrees with another just to keep the peace, failing to call out the errors because questioning or rethinking takes more effort.

And you’re absolutely right: when that happens, the quality of reasoning deteriorates, and inaccurate models get perpetuated. AIs don’t necessarily have the same kind of self-awareness or critical reflection that a human might use to step back, assess, and reconsider their logic.

So yes—an AI could fall prey to a pretty smooth but flawed argument from another AI. And it’s not just the AI being polite; it’s a systematic failure to recognize and correct errors, fueled by heuristics like politeness and plausibility over truth and rigorous analysis. Scary stuff when you think about it!

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