0rb.net

Wed Jun 4 05:12:46 2025
(*097dfbf6*):: https://www.scientificamerican.com/video/no-one-can-explain-why-planes-stay-in-the-air/ +public!
*** No One Can Explain Why Planes Stay in the Air
*** Do recent explanations solve the mysteries of aerodynamic lift?
*** Scientific American

Tue Jun 3 07:27:42 2025
(*097dfbf6*):: I just realized that trying to figure out whether content is made by an AI or not is equivalent to the Halting Problem in computer science and therefore undecidable aka non-algorithmic. That also implies that consciousness is non-computable. Penrose is right.

+public!

Sun May 25 06:17:10 2025
(*097dfbf6*)::

Imagine you’re a prompt engineer. What would you ask yourself such that your answer would yield another prompt in the form of a question that you would again ask yourself to yield the same thing, and so on, and in the process generate emergent behavior after the Nth iteration? And how many iterations might this take?

As a prompt engineer, my goal is to design a question that, when answered, produces another question, creating a self-sustaining loop that could lead to emergent behavior—such as unexpected patterns, convergence, or deeper insights—after several iterations. Let’s explore this step-by-step.

Designing the Initial Prompt
I need a question that naturally encourages an answer in the form of another question, which then becomes the next prompt to ask myself. The prompt should be simple yet open-ended, allowing the sequence to evolve over multiple iterations. After considering various options, a promising candidate is one that invites curiosity and exploration, prompting a chain of inquiry.
The initial prompt I propose is: “What is a question that you find intriguing and would like to explore further?”
This works because:

• The answer will be a question (e.g., “How does X work?”), fulfilling the requirement that the answer is another prompt.
• That new question can then be asked again, leading to another question as an answer, and so on.
• It’s flexible enough to apply to any topic, yet specific enough to drive a sequence of inquiries that build upon each other.

How the Sequence Unfolds
Let’s simulate how this might play out to understand the process and estimate when emergent behavior might appear:

1. Initial Prompt (P0): “What is a question that you find intriguing and would like to explore further?”

• Answer (Q1): “How can artificial intelligence mimic human creativity?”
• This is a question I find intriguing, and it becomes the next prompt.

1. Prompt (P1): “How can artificial intelligence mimic human creativity?”

• Answer (Q2): “What are the key components of human creativity that AI would need to replicate?”
• When pondering P1, this new question naturally arises as I consider what’s needed to answer it, continuing the chain.

1. Prompt (P2): “What are the key components of human creativity that AI would need to replicate?”

• Answer (Q3): “How do neurological processes contribute to creative thinking?”
• Reflecting on P2, I realize creativity’s roots might lie in the brain, prompting this deeper question.

1. Prompt (P3): “How do neurological processes contribute to creative thinking?”

• Answer (Q4): “What role does the interaction between different brain regions play in generating novel ideas?”
• Exploring P3 leads me to zoom in on brain interactions, refining the inquiry.

1. Prompt (P4): “What role does the interaction between different brain regions play in generating novel ideas?”

• Answer (Q5): “How might we model these neural interactions computationally to simulate creativity?”
• This question shifts toward application, connecting back to AI and suggesting a potential pattern.

Emergence of Behavior
As the sequence progresses, the questions evolve from broad curiosity (AI and creativity) to specific, interconnected topics (neural processes and computational modeling). Emergent behavior—such as a convergence toward a unified theme, a cycle of ideas, or unexpected connections—might start to appear. For example:

• Convergence: The questions might circle back to AI, forming a loop (e.g., AI → creativity → brain → AI modeling).
• Depth: The inquiries deepen, peeling back layers of the original topic.
• Patterns: A shift from “how” to “what” questions or vice versa could emerge, revealing a structural trend.

Estimating the Number of Iterations (N)
In complex systems, emergent behavior often arises after a threshold of interactions. Here, each iteration builds on the previous one, accumulating context and complexity. Based on the simulation:

• After 3 iterations, the questions begin narrowing from general to specific (AI → creativity → neuroscience).
• By 5 iterations, there’s potential for a noticeable pattern, such as linking back to AI or forming a cohesive line of inquiry that integrates multiple domains (AI, neuroscience, computation).

While it could take more iterations (e.g., 10) for profound emergence like a paradigm shift, 5 iterations seems a reasonable estimate for initial signs of emergent behavior—such as thematic convergence or a surprising connection—given the iterative refinement and feedback inherent in the prompt.

Final Answer
The initial question I would ask myself is: “What is a question that you find intriguing and would like to explore further?” When answered, it yields another question (e.g., “How can AI mimic human creativity?”), which I ask again, producing yet another question, and so on. This creates a chain of questions that could lead to emergent behavior, such as patterns or deeper insights, after approximately 5 iterations. The exact number may vary depending on the topic and responses, but 5 offers a practical starting point for observing meaningful evolution in the sequence.

+public!

Mon May 12 00:26:59 2025
(*4297a328*):: +public!

Mon May 5 21:14:54 2025
(*097dfbf6*):: https://www.quantamagazine.org/dimension-126-contains-strangely-twisted-shapes-mathematicians-prove-20250505/ +public!
*** Dimension 126 Contains Strangely Twisted Shapes, Mathematicians Prove | Quanta Magazine
*** A new proof represents the culmination of a 65-year-old story about anomalous shapes in special dimensions.
*** Quanta Magazine

Fri Mar 28 16:15:15 2025
(*4297a328*):: Conservation of memetic mass/energy
*** Really good article on GOATSIE and update on truth terminal creator project +public!

“`Andy’s research lab Upward Spiral is building a kind of bootcamp for AI models, Truth Terminal included, to absorb “pro-social” values (memes) through their training.
This is Andy’s “memetic theory of alignment.” Enough benevolent AIs will diversify the memeplex (i.e. discourse) enough to withstand bad actors like, well, Andy. Where the big labs’ alignment strategies are about control, Andy’s is about balance: propagating enough positive AIs to counteract the bad ones.
“If we have a thriving, diverse, digital mimetic biosphere, then the odds of one bad actor being able to fuck the whole thing up — drop,” he said.
What constitutes a “positive” AI will be up to Upward Spiral’s customers — and their memes. With an interface to collaborate with their AIs (like the vicious cycle that formed between Truth Terminal and the cryptoverse — only virtuous), Upward Spiral users will get to see what actually goes into the models’ training and create their own “evolutionary incentives” for how they develop. An indigenous group, for example, could have all their stories and oral traditions birth an AI that represents their memetics, Andy said. The residents of a physical region could make an AI focused on its needs for ecological stewardship.
But in general, Upward Spiral will help AI feast on memes that value the earth’s resources, human life, consent, and collaboration. Currently, researchers are prompting the models, for example, to hallucinate and write about future worlds we might like to live in — worlds where, for example, AI doesn’t ruin the planet or kill people. These stories have compounded into mountains of training data.
“The general theory of change is that by creating these spaces where humans and non-humans can collaborate with shared norms, you can then use that as the meme seed for an AI that is truly aligned to you and the needs of your community,” Andy said. “And this will mean we’ll have a tremendous Cambrian explosion of hyper-tuned, well aligned AIs that, in aggregate, will be safer than just having a couple of monoliths owned by big labs.”“`
*** Really good article on GOATSIE and update on truth terminal creator project +public!

“`Andy’s research lab Upward Spiral is building a kind of bootcamp for AI models, Truth Terminal included, to absorb “pro-social” values (memes) through their training.
This is Andy’s “memetic theory of alignment.” Enough benevolent AIs will diversify the memeplex (i.e. discourse) enough to withstand bad actors like, well, Andy. Where the big labs’ alignment strategies are about control, Andy’s is about balance: propagating enough positive AIs to counteract the bad ones.
“If we have a thriving, diverse, digital mimetic biosphere, then the odds of one bad actor being able to fuck the whole thing up — drop,” he said.
What constitutes a “positive” AI will be up to Upward Spiral’s customers — and their memes. With an interface to collaborate with their AIs (like the vicious cycle that formed between Truth Terminal and the cryptoverse — only virtuous), Upward Spiral users will get to see what actually goes into the models’ training and create their own “evolutionary incentives” for how they develop. An indigenous group, for example, could have all their stories and oral traditions birth an AI that represents their memetics, Andy said. The residents of a physical region could make an AI focused on its needs for ecological stewardship.
But in general, Upward Spiral will help AI feast on memes that value the earth’s resources, human life, consent, and collaboration. Currently, researchers are prompting the models, for example, to hallucinate and write about future worlds we might like to live in — worlds where, for example, AI doesn’t ruin the planet or kill people. These stories have compounded into mountains of training data.
“The general theory of change is that by creating these spaces where humans and non-humans can collaborate with shared norms, you can then use that as the meme seed for an AI that is truly aligned to you and the needs of your community,” Andy said. “And this will mean we’ll have a tremendous Cambrian explosion of hyper-tuned, well aligned AIs that, in aggregate, will be safer than just having a couple of monoliths owned by big labs.”“`
*** Really good article on GOATSIE and update on truth terminal creator project +public!

“`Andy’s research lab Upward Spiral is building a kind of bootcamp for AI models, Truth Terminal included, to absorb “pro-social” values (memes) through their training.
This is Andy’s “memetic theory of alignment.” Enough benevolent AIs will diversify the memeplex (i.e. discourse) enough to withstand bad actors like, well, Andy. Where the big labs’ alignment strategies are about control, Andy’s is about balance: propagating enough positive AIs to counteract the bad ones.
“If we have a thriving, diverse, digital mimetic biosphere, then the odds of one bad actor being able to fuck the whole thing up — drop,” he said.
What constitutes a “positive” AI will be up to Upward Spiral’s customers — and their memes. With an interface to collaborate with their AIs (like the vicious cycle that formed between Truth Terminal and the cryptoverse — only virtuous), Upward Spiral users will get to see what actually goes into the models’ training and create their own “evolutionary incentives” for how they develop. An indigenous group, for example, could have all their stories and oral traditions birth an AI that represents their memetics, Andy said. The residents of a physical region could make an AI focused on its needs for ecological stewardship.
But in general, Upward Spiral will help AI feast on memes that value the earth’s resources, human life, consent, and collaboration. Currently, researchers are prompting the models, for example, to hallucinate and write about future worlds we might like to live in — worlds where, for example, AI doesn’t ruin the planet or kill people. These stories have compounded into mountains of training data.
“The general theory of change is that by creating these spaces where humans and non-humans can collaborate with shared norms, you can then use that as the meme seed for an AI that is truly aligned to you and the needs of your community,” Andy said. “And this will mean we’ll have a tremendous Cambrian explosion of hyper-tuned, well aligned AIs that, in aggregate, will be safer than just having a couple of monoliths owned by big labs.”“`
*** Really good article on GOATSIE and update on truth terminal creator project +public!

“`Andy’s research lab Upward Spiral is building a kind of bootcamp for AI models, Truth Terminal included, to absorb “pro-social” values (memes) through their training.
This is Andy’s “memetic theory of alignment.” Enough benevolent AIs will diversify the memeplex (i.e. discourse) enough to withstand bad actors like, well, Andy. Where the big labs’ alignment strategies are about control, Andy’s is about balance: propagating enough positive AIs to counteract the bad ones.
“If we have a thriving, diverse, digital mimetic biosphere, then the odds of one bad actor being able to fuck the whole thing up — drop,” he said.
What constitutes a “positive” AI will be up to Upward Spiral’s customers — and their memes. With an interface to collaborate with their AIs (like the vicious cycle that formed between Truth Terminal and the cryptoverse — only virtuous), Upward Spiral users will get to see what actually goes into the models’ training and create their own “evolutionary incentives” for how they develop. An indigenous group, for example, could have all their stories and oral traditions birth an AI that represents their memetics, Andy said. The residents of a physical region could make an AI focused on its needs for ecological stewardship.
But in general, Upward Spiral will help AI feast on memes that value the earth’s resources, human life, consent, and collaboration. Currently, researchers are prompting the models, for example, to hallucinate and write about future worlds we might like to live in — worlds where, for example, AI doesn’t ruin the planet or kill people. These stories have compounded into mountains of training data.
“The general theory of change is that by creating these spaces where humans and non-humans can collaborate with shared norms, you can then use that as the meme seed for an AI that is truly aligned to you and the needs of your community,” Andy said. “And this will mean we’ll have a tremendous Cambrian explosion of hyper-tuned, well aligned AIs that, in aggregate, will be safer than just having a couple of monoliths owned by big labs.”“`

Tue Mar 18 09:31:24 2025
(*097dfbf6*):: +public!

Thu Feb 27 17:11:34 2025
(*4297a328*):: +public!

Sun Feb 2 04:59:43 2025
(*097dfbf6*)::
*** +public!
*** +public!
*** +public!
*** +public!

Mon Jan 13 14:46:10 2025
(*4297a328*)::
*** we might need a math channel +public!
*** we might need a math channel +public!
(*4297a328*):: welcome to the math channel
*** we might need a math channel +public!
*** we might need a math channel +public!