← 2 AI (models)


1 This is actually a useful list

because it roughly follows the historical evolution of AI models. For ZiptieAI, I’d treat most of these as: • interesting side roads while:

  • Core NN
  • CNN
  • Transformer
  • JEPA / World Models

are probably your main road.

RNNs / LSTMs (1990s-2010s)

Purpose: Process sequences one step at a time.

Examples:

  • speech
  • text
  • time series

Mechanism:

  • token 1 ↓ token 2 ↓ token 3.
  • each step passes memory to the next.
  • Problem: slow , hard to train, long-range context problems.
  • Mostly replaced by transformers.

Autoencoders

Purpose: Compress data and reconstruct it.

Mechanism: input ↓ compressed representation ↓ reconstruction

Examples:

  • anomaly detection
  • dimensionality reduction
  • feature extraction

Interesting historically because they introduced the idea of learning latent representations.

GANs (Generative Adversarial Networks)

Purpose: Generate realistic synthetic data.

Mechanism: Generator vs Discriminator

Examples:

  • fake faces
  • image generation
  • deepfakes

Very famous around 2015-2020. Mostly superseded by diffusion models.

Graph Neural Networks (GNNs)

Purpose: Learn from graph structures.

Examples:

  • social networks
  • fraud networks
  • chemical molecules
  • knowledge graphs

Input:

  • nodes
  • edges
  • relationships

These are still important. Drug discovery often uses GNN-like approaches.

Diffusion Models

Purpose: Generate images, audio, video.

Mechanism:

  • add noise
  • learn to remove noise

Examples:

  • Stable Diffusion
  • Midjourney-like systems
  • image generation

Current state-of-the-art for many image-generation tasks.

JEPA / World Models

Purpose: Learn how the world works.

Mechanism:

  • observe
  • predict hidden structure
  • learn representations

Examples:

  • robotics
  • autonomous systems
  • planning

This is the area associated with Yann LeCun and is one of the reasons you became interested in NCU and robotic AI.

My recommendation for ZiptieAI

Main path: Core NN ↓ CNN ↓ Transformer ↓ JEPA / World Models ↓ Agentic AI

Side roads (learn later if needed):

  • RNN/LSTM
  • Autoencoder
  • GAN
  • GNN
  • Diffusion

The reason is simple:

  • RNNs -> mostly historical
  • GANs -> mostly historical
  • Autoencoders -> useful but niche
  • GNNs -> important in some industries
  • Diffusion -> important for media generation
  • JEPA -> directly related to your robotic AI interests

So if your goal is:

  • AI models
  • → robotic AI
  • → agentic AI

then I think your current roadmap is already pointing toward the most relevant technologies.


2 i just want to study the old list

because when i see these talked about in the future i will understand how they are irrelevant to what i am doing. or how they led to the tech that i am using.

RNN/LSTM

  • Early sequence models.
  • Used before transformers.
  • Important historically.
  • Mostly replaced by transformers.

GAN

  • One of the first successful image generators.
  • Interesting historical step.
  • Largely replaced by diffusion models.

Autoencoder

  • Learns compressed representations.
  • Helped establish the idea of latent spaces.
  • Many later architectures borrowed these ideas.

GNN

  • Still relevant.
  • Used when the data is naturally a graph.
  • Not a competitor to transformers.
  • Different problem class.

Diffusion

  • Current dominant image generation approach.
  • Not particularly relevant to PAL, agents, or workflow automation.

JEPA / World Models

  • Potential future direction for robotics.
  • Directly relevant to my robotic AI interests.

historical landmarks

    ↓
MLP / Core NN
    ↓
CNN
    ↓
RNN/LSTM
    ↓
Transformer
    ↓
Agentic AI

CNN
    ↓
GAN
    ↓
Diffusion

Transformer
    ↓
JEPA / World Models
    ↓
Robotic AI


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