Machine Learning

mL/AI - The movement to emulate natural intelligence.

Scope

  • The dream for many programmers, scientists, engineers and humans would be to create an entity that could scale past our natural intelligence. This is a task that would define the 21st century and push the upper limits on humanity, naturism and metaphysics into the next industrial intelligence revolution.

mL

  • mL (machine learning) is a subset of artificial intelligence and follows the theory of computational training, to understand core principles through computer science and statistics. These training methods can be broken down to supervised, unsupervised and reinforcement learning.

AI

  • Artificial intelligence is an umbrella phrase that encapsulates various fields within computer science, mathematics, philosophy and information with the goal to emulate natural intelligence display by humans and animals.

References

  • This is an ever growing and evolving list of all mL / ai services, concepts and ideas that can be referenced for your experiences within the field.

Text Models

GPT

  • GPT-Neo

    • Official Github Repo
      • We should note that the team, EleutherAI, are no longer maintaining the gpt-neo and their repo is currently in archive mode. However below is the gpt-neox, which is still being actively maintained as for Oct 2022.
    • The GPT-Neo may have been an extension of GPT2 but changes to the layering.
  • GPT-NeoX


Journal

  • This is a collective journal with tasks, opinions and notes.

  • They should not be taken as valid information and should be seen as mere unaudited thoughts of a wandering collection of souls.

  • 10/24/2022

    • -> October 24th, 2022 -> “Computational Learning” as well as “mL/AI” -> two important concepts.
    • We should say that ai is an umbrella phrase that includes various tools, concepts and data. If we could imagine data as crude oil then we can say models are refined oils, thus the functional aspect of refinement should be a pillar of machine training.
    • It be like taking data from our natural world, filled with its random and chaos, is collected or drilled, then processed into abstract collections of meaningful and layered information, finally forming our computational models.
    • There definitely is more to this but that should be a solid building path for where we can go.
    • The speed at which this field is growing is also remarkable, its still insane to see how my laptop can generate art from just processing my vocals as I talk.