AI, artificial intelligence, feels overwhelming because it involves complex technology, rapid innovation that is hard to keep up with, and heavy industry hype. As a result, many people—even experts—struggle to fully understand it, leading to common misunderstandings, including what AI actually means.
This article is written to be aligned with AWS Certified Practitioner certification. According to AWS documentation, you can think of AI as
Artificial Intelligence (AI) is a transformative technology that enables machines to perform human-like problem-solving tasks. From recognizing images and generating creative content to making data-driven predictions, AI empowers businesses to make smarter decisions at scale.
There are different types or subsets of AI. In this article, we will compare the similarities and differences between these.
From the diagram, you can see the four subsets of AI - AI, Machine Learning, Deep Learning, Generative AI.
AI is the collection of the all the fields. Here you can create machines and technology that can perform tasks that usually require human intelligence. It can learn, make decisions, recognise patterns like humans do. It can contain include rule-based systems and decision trees with no learning component. You are probably familiar with many of the AI examples like Apple Siri, robots, etc.
Machine Learning is a subset of AI that uses advanced algorithms to detect patterns in large data sets to make decisions. ML algorithms use supervised, unsupervised, and reinforcement learning methods. It can use both labelled and unlabelled data and generally more high quality data is better for the ML models. You can use ML for fraud detection, predictive analytics, and recommendation engines.
Deep Learning is a subset of ML and uses neural networks with multiple layers and use large volume of data for training. These are designed based on human brain model. This can often determine complex patterns and relationships. It can sometimes outperform humans. Facial recognition, Image recognition, natural language processing (NLP) are some of the examples of deep learning.
Generative AI is a subset of deep learning. It can essentially generate text content and images and even music based on the text prompt. It is not a copy/paste from relevant website but it generates the data based on what it has seen in other content. Large Language Models (LLMs) is a type of gen AI and applications like ChatGPT and Grok are built using these. With Gen AI, you can also code software easily automatically and tweak as needed.
Gen AI is obviously the easiest to use, however, depending on you use case, you might have to build ML or deep learning models.
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