The Paleo Cyborg Podcast

Wherein free will is explained along with

How it’s Lensing all the way down

The interesting part of free will is the Will

Reason is slave to the passions”

Stoplight metaphor

A different timescale

https://multisenserealism.com/the-competition/departing-from-the-consensus/determinism-tricks-of-the-trade/

Holos is present in every fragment

Versing the unity

Nothing is ever repeated

A triangle is eternal

The third side makes itself for free if you have two sides

Spirals instead of looping

The difference between a labyrinth and a maze

The maze is finite and temporal

The labyrinth is infinite possibility

The foundation is laid for explaining Free Will and meta-representational Consciousness without resorting to concepts such as Emergence

Map and territory

Chris Langan free will is creating space and time via manifolds and fractal recursion

You are the universe interacting with itself

Flatland

Chris Fields

Infinite regression recursively stabilizes

Bruce Lipton

Consciousness via external inputs

Nikola Tesla energy frequency and vibration

His brain is but a transmitter and a receiver

“Mind-expanding” drugs suppress the ego and survival mechanisms

With a five minute preamble introducing and explaining the map and the territory as well as simulations and representations

A brief reframe of “the map is not the territory”

The map and the territory enter the chat. Amplituhedrons simplify the equations instead of complexifying the math

Emergence

Map and territory

Chris Langan free will is creating space and time via manifolds and fractal recursion

You are the universe interacting with itself

Flatland

Chris Fields

Infinite regression recursively stabilizes

Bruce Lipton

Consciousness via external inputs

Nikola Tesla energy frequency and vibration

His brain is but a transmitter and a receiver

“Mind-expanding” drugs suppress the ego and survival mechanisms

“You can’t do impossible things”

Wizard of consciousness Craig Weinberg returns once again to explain multisenserealism.com and more Show notes:

David Chalmers Hard Problem latest blog post

https://multisenserealism.com/2023/06/20/youtube-is-there-really-a-hard-problem-of-consciousness/

Aesthetic participatory phenomenon

Descartes “ I think therefore I am “

Wavelengths and frequencies of light and the colour red

You should measure photons and matter against the ruler of redness and blueness, compare and contrast (optical illusions, for example)

Dream matters

“The brain filters reality”

Simulation theory is a metaphor

Map vs territory

Physic and evolution and natural selection

Donald Hoffman

Neo - Platonism

Newton and astrology and alchemy

Epicycles added to science to explain errors and correct them

The epitome of this is string theory

Caio Temer speaks on LLMs and Ai and AGi & the possibility for them becoming conscious.

Collaborative efforts involve creating language models using Facebook's FastText, demonstrated in Colab notebooks and GitHub repositories:

- [Colab 1](https://colab.research.google.com/drive/1wVQrpzyY-SkCZTRZCcP6xJGRdg1ik0jR#scrollTo=pIW6-VwMuRlz)

- [Colab 2](https://colab.research.google.com/drive/1oABIZr1xiIu7DKc7AbbZfeBJFLkZW6Ep#scrollTo=XfR-3PpPYoFU)

- [GitHub Repository](https://github.com/rtkclouds/fast-js-language-model)

Additionally, you can find more details in the provided [GitHub gists](https://gist.github.com/rtkclouds/50b81d10736793f07cdca354516e8757) and [here](https://gist.github.com/rtkclouds/a6ee9afd96461ca94b3e9c22f78bda3a).

For further insights, you can explore the [X.com link](https://x.com/canalCCore2/status/1735044356535472278?s=20).

Part 2

https://canalccore.medium.com/innovating-beyond-openais-q-a-novel-reinforcement-learning-approach-to-nlp-09d4b58da1ea

https://ai.plainenglish.io/leveraging-local-indexes-in-language-models-to-enhance-contextual-learning-3d2a8f98fde5

https://canalccore.medium.com/language-models-still-have-basic-structural-problems-9f88b04576c

Caio Temer explains LLMs and machine learning. found that generalized learning happens after 10k epochs with a considerable amount of data per epoch, are there studies in this regard?

ChatGPT

Yes, there are studies related to generalized learning after many times, with a large amount of data per season. A notable example is the "Deep Bootstrap Framework", presented at ICLR 2021. This framework makes a connection between generalization and online optimization. He compares a "real world", where a model is trained on a finite number of samples by multiple times, with an "ideal world", where each training step uses fresh samples from a virtually infinite data set. The research found that models that train quickly in infinite data are the same as those that generalize well in finite data. This study offers a new perspective on design choices in practice and provides a theoretical path to understand generalization.

Get started with ChatGPT

Caio Temer

show notes:

Part One summary

interesting topics for trading- -how I created the first bots -how models combine language models with autonomous robots -why conventional methods don't work about language models: -language models are simpler to obtain with other structures -problem in the current structure and how I solved it - development of intuitive neural networks using hash abstraction

Caio developed a way for neural networks, through cryptography techniques that use sum, to learn to abstract and understand deeply quickly

created a lib of a tiny language model that learns to predict all tokens at an absurd speed by using distance vector embeds instead of tokens

Transformers:

[](https://arxiv.org/abs/2205.13504)

for example, if the network is exploding, there's something wrong with a component, or the update rate is wrong If the rate is right, the linearities are wrong, just exchange some linear or more linear connection like relu for tanh or add a tanh. softmax in deep layers as a projector reduces network convergence time by forcing layer alignment training the transformer one layer at a time, adding a copy of the first layer and freezing the first accelerates training and improves retention

In sequence for sequence, a permuted linear dense solves 99% of problems in less time and better than the transformer. Giant embeds work like entire networks, only needing one activation at the end after reshaping

“1 tanh layer of 1 million units solves everything better than any other network with the same number of parameters, at least in the tasks I use, I always start with it, then see how to reduce”

Collabs:

making a language model using Facebook's FastText https://colab.research.google.com/drive/1wVQrpzyY-SkCZTRZCcP6xJGRdg1ik0jR#scrollTo=pIW6-VwMuRlz

https://colab.research.google.com/drive/1oABIZr1xiIu7DKc7AbbZfeBJFLkZW6Ep#scrollTo=XfR-3PpPYoFU

https://github.com/rtkclouds/fast-js-language-model

https://x.com/canalCCore2/status/1735044356535472278?s=20

GitHub gists:

[](https://gist.github.com/rtkclouds/50b81d10736793f07cdca354516e8757)

[](https://gist.github.com/rtkclouds/a6ee9afd96461ca94b3e9c22f78bda3a)

Episode 12, Part 2

Twitter / X account

https://x.com/fr_and_ai?s=21

YouTube channel

https://youtube.com/@FRandAI?si=iHpZtMYXkS0LtLP-

Patreon

[](https://www.patreon.com/frandai?utm_campaign=creatorshare_fan)

Aze Alter account;

https://x.com/azealter?s=21

Incomprehensible weirdness

Backwards causation

Ruliad : entangled limit of all possible computations

Branchial space

[](https://knowyourmeme.com/memes/shoggoth-with-smiley-face-artificial-intelligence)

Stephen La Berge lucid dreaming

Annihilation notes meme https://slate.com/culture/2018/02/annihilation-co-composer-ben-salisbury-explains-the-musical-cue-from-the-alien.html

https://slate.com/culture/2018/02/when-does-the-weird-musical-noise-finally-happen-in-annihilation.html

https://slate.com/culture/2018/02/im-obsessed-with-annihilations-five-note-melody-sound-effect.html

https://x.com/gracespelman/status/1082862350846312448?s=46

Binaural Gateway

Optimizing for attention not for attention span

Rewatchability

Engagement

Mid journey 6

Very unpleasant dreams

Rabbit character

ChatGPT

The Judgement

The Bureau

The Penal Colony

The Metamorphosis the VR experience

Goat simulator

The Hunger Artist

Unlimited Dream Company

Satisficing

Christopher Nolan

Focused

Alex Garland

Kenneth Stanley

Serendipity and novelty

Peter Birkhauser

Jungian

Tesla channeling

Ian McGilchrist left brain right brain split

[](https://youtu.be/lgnMyF-o0sQ?si=raUB-6NKEuJwDjUQ)

Duplass brothers

Pascal’s Wager

Ursula Le Guinn

Director and artist Forrest Rice joins the show to talk about the future of art, Ai, philosophy, Wittgenstein, the Ruliad, HP Lovecraft, Franz Kafka and much more. Show notes: Twitter account

https://x.com/fr_and_ai?s=21

YouTube channel

https://youtube.com/@FRandAI?si=iHpZtMYXkS0LtLP-

Patreon

[](https://www.patreon.com/frandai?utm_campaign=creatorshare_fan)

brief intro

Kafka’s short stories

The Colony

Indie film making

Wittgenstein

Loss function

Joscha Bach

Alan Turing machines

Halting problem

Logical positivism

Functionalism

Duty of Genius by Ray Monk

“I play the orchestra “ —Steve Jobs

Alfred Leon Vitali

Filmworker

Peacock Kubrick documentary

[](https://m.imdb.com/title/tt6851066/)

Bo Burnhan INSIDE Netflix

Aze Alter

Capital of Conformity

Loss function returns

Indrid Cold derinberger

Mothman Prophecies

Roko’s Basilisk

Gaia

Christmas gnome elf

CIA gateway

Incomprehensible weirdness

Backwards causation

Ruliad : entangled limit of all possible computations

Branchial space

[](https://knowyourmeme.com/memes/shoggoth-with-smiley-face-artificial-intelligence)

Stephen La Berge lucid dreaming

Annihilation notes meme

Binaural Gateway

Optimizing for attention not for attention span

Rewatchability

Engagement

Mid journey 6

Very unpleasant dreams

Rabbit character

ChatGPT