Artificial Intelligence, Machine Learning , Deep Learning, GenAI and more

What are they and how are they different?

Most of us have heard, if not all, at least some of these buzz words. These terms are often used interchangeably, but they do not mean the same thing. In this short blog, I’m giving a brief explanation of each one and how they fit together with the hope to clarify some of the ambiguity.

I created this diagram to visually represent the relationship between each of these terms

• Artificial Intelligence (AI) is a discipline, a branch of computer science, that deals with the creation and development of machines that think and act like humans. AI powered technologies have been around for a while and some everyday examples are Siri and Alexa and customer service chatbots that pop up on websites.
• Machine Learning (ML) is a subfield of AI. It is a program or system that trains a model from input data and then that trained model can make useful predictions from new or never before seen data.¹ So, ML gives the computer the ability to learn without explicitly programming. While in traditional programming, developers write explicit instructions for a computer to execute, in ML, algorithms learn patterns and relationships from data to make predictions or decisions. Unsupervised, Supervised and Reinforcement learning are the most common ML models. Google smart reply is an example of ML.
• Neural Networks (NNs) — also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning. The name and structure inspired by the human brain, mimicking the way that biological neurons signal to one another.² NN consist of interconnected artificial neurons organised in layers: an input layer, one or more hidden layers, and an output layer. NN are at the heart of deep learning algorithms.
• Deep Learning(DL) is a subset of NN. The word deep here refers to the depth of layers in a neural network. Any neural network with more than three hidden layers can be considered a deep learning algorithm. Having a higher number of hidden layers, DL models are well-suited for tackling complex real-world problems. Everyday examples of technologies using NN and DL are: image recognition or object detection in smartphone cameras — such as Facial Recognition and Autofocus- and online language translation services like Google Translate.
• Generative AI (GenAI) is subset of DL, a type of artificial intelligence technology that can generate different types of content - such as text, imagery, audio, video - based on what has learnt from existing content.
• Large Language Model (LLM) is a form of generative AI, which focuses on generating human-like text based on the patterns learned from vast amounts of textual data during the training process. Note the difference between LLM and ML. Large Language Model is a specific type of machine learning model specialised in natural language processing, while ML is a broader field that encompasses various algorithms and techniques used across diverse domains to enable computers to learn from data and make predictions or decisions. ChatGPT is possibly the most famous example of technologies using LLM right now.

I hope both tech and non tech people found this blog useful. If you have any questions, comments or just want to say hi please feel free to reach out to me here in Medium or LinkedIn.

References and additional resources

¹https://www.cloudskillsboost.google/course_sessions/3911204/video/384243

² https://www.ibm.com/topics/neural-networks

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