AI Foundation on Arshad Siddiqui

AI Stages: ANI, AGI, ASI

Thu, 04 Jul 2024 10:55:52 +0100

AI Development Stages: ANI → AGI → ASI #

Artificial Intelligence is often described in three stages, based on capability and scope:

ANI: Task-specific intelligence (today’s AI)
AGI: Human-level general intelligence (future goal)
ASI: Beyond human intelligence (theoretical)

ANI — Artificial Narrow Intelligence #

also called Weak AI
designed to perform one specific task
Operates within a predefined environment
Cannot generalise beyond its training
Most AI systems today are ANI

examples

AI Stack

Mon, 01 Jan 0001 00:00:00 +0000

AI Stack #

The AI Stack describes the layers required to build an end-to-end AI system, from infrastructure at the bottom to user-facing applications at the top.

Different organisations represent the AI stack differently; this is a simplified conceptual view for learning.

Each layer depends on the one below it.

graph TB

 subgraph APP["Applications"]
 A[User Interfaces & Integrations]
 end

 subgraph ORCH["Orchestration"]
 O[Workflows • Agents • Control Logic]
 end

 subgraph DATA["Data"]
 D[Data Sources • Pipelines • Vector DBs]
 end

 subgraph MODEL["Models"]
 M[ML • DL • Foundation Models • LLMs]
 end

 subgraph INFRA["Infrastructure"]
 I[Cloud • On-prem • GPUs • Storage]
 end

 %% Styling
 style APP fill:#FFCCBC
 style ORCH fill:#90CAF9
 style DATA fill:#BBDEFB
 style MODEL fill:#C8E6C9
 style INFRA fill:#E1F5FE

 style A fill:#FFE0B2
 style O fill:#B3E5FC
 style D fill:#E3F2FD
 style M fill:#DCEDC8
 style I fill:#E1F5FE

1. Infrastructure #

The foundation that provides compute and storage.

AI Pipeline

Thu, 04 Jul 2024 10:55:52 +0100

AI Pipeline #

The AI pipeline is a continuous process where data is collected, prepared, used to train models, evaluated for performance, and continuously improved after deployment.

Collect Data #
Prepare data #
Train Model #
- Iterate until model is good enough
Deploy Model #
- Get data back
- Maintain & update model

timeline
 title AI Pipeline
 Collect Data : Data Ingestion
 : Data Understanding
 Prepare Data : Cleaning
 : Feature Engineering
 : Sampling
 Train Model : Model Training
 : Validation & Metrics
 Deploy Model : Deployment
 : Monitoring & Retraining

Home | AI Foundation

AI Learning Resources

Sat, 03 Jan 2026 12:00:00 +0100

AI Learning Resources #

A curated list of high-quality online courses to learn Artificial Intelligence, Machine Learning, and Deep Learning from reputable universities and organisations.

Recommended Books & References #

Deep Neural Networks (DNN) #

Deep Learning. MIT Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). (Vol. 1, No. 2).
Introduction to Deep Learning. MIT Press.
Eugene, C. (2019).
Deep Learning with Python. Simon & Schuster.
Chollet, F. (2021).