While teaching students in two different courses (AIML & “Data Science and Analysis”), there was a requirement to categorize historical AI & ML along with it’s interface with Data Science.
To start: AI is the superset, ML is a subset of AI, Neural Networks (Deep Learning) are specialized subsets of ML.
Below is a categorization of AIML across four waves and it’s interface with Data Science:
Wave 1:
Concepts: Traditional topics like state space search, heuristics, knowledge representation, expert systems, fuzzy logic, problem solving languages and such.
UseCases: Think a small basic robot moving through your home and taking decisions on avoiding obstacles.
Wave 2:
Concepts: Standard algorithms built on top of Regression, Statistics, Algebra, Probability, Calculus and such – Classification, Decision Trees, Association Mining, Clustering, Ensemble methods, Random Forest, SVM and so on. NLP, Computer vision, scanning solutions, advanced search and such areas also evolved here in parallel or with the help of these algorithms.
UseCases: Spam detection, Decision making, Co-related variables related predictions, Prescriptive Analytics and so on.
Wave 3:
Concepts: Replicating human / animal brain. Neural Networks. Storing and managing very large amount of data (structured & un-structured)
UseCases: BigData, Self driving cars, Image recognition, Complex reasoning, Medical diagnosis, Chat bots, Personal assistants, potentially unlimited usecases interfacing with all usecases across AIML & Data Science.
Wave 4:
Concepts & UseCases: Explanability, Interpretability – Understanding the complexity of artificial intelligence & machine learning models. UI & Low code driven AIML (Neural Networks), one shot learning, hardware optimized AIML. Deep Learning. BERT and newer context driven algorithms also are in this area, Natural Language Generation is another area here.
Where does Data Science interface with AIML:
- Non structured data analysis
- Natural language generation
- Sentiment analysis
- Use of standard algorithms to analyse structured data
- Building insights & making predictions / prescriptions and so on
Email me: Neil@TechAndTrain.com
