Machine Learning, it is all many people think of when they hear Artificial Intelligence (AI). But, Machine Learning (ML) is a subset of AI.
Often you'll hear about Deep Learning (DL), so what's that? DL a subset of ML. Both ML and DL require data to do their training. No data, no intelligence.
The more data you have, the more intelligent robots you can create. By robots, it can be both traditional hardware robots or software robots.
AI has already entered our lives: recommender systems (youtube, netflix), face recognition (cameras, google, facebook), spam filtering (emails) and much more. It's no longer simple rule based AI, data based systems make all this possible.
Is ML and DL an end-all-be-all for AI?
No, ML and DL are not the only way to apply AI. There are many more solutions:
- Genetic Algorithms (GA)
- Bayesian Inferencing (BI)
- Semantic Reasoning using rules or ontologies
How to get into AI/ML?
There are many blogs and courses out there.
- https://gumroad.com/l/MnRYU
- https://pythonprogramminglanguage.com/python-machine-learning/
- https://developers.google.com/machine-learning/guides/
- https://towardsdatascience.com/the-cold-start-problem-how-to-build-your-machine-learning-portfolio-6718b4ae83e9
- https://pythonbasics.org/why-python-for-machine-learning/
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