AI vs. Machine Learning vs. Deep Learning vs. Generative AI: Understanding the Key Differences

Artificial intelligence is basically trying to simulate with a computer something that would match or exceed human intelligence. What is intelligence? Well, it could be a lot of different things, but generally, we tend to think of it as the ability to learn, to infer, and to reason. Things like that. So, that’s what we’re trying to do in the broad field of AI, artificial intelligence.

If we look at the timeline of AI, it really kind of started back around this time frame. And in those days, it was very premature. Most people had not even heard of it. And it was basically a research project. But I can tell you, as an undergrad, which for me was back during these times, we were doing AI work. In fact, we would use programming languages like Lisp or Prolog. And these kinds of things were kind of the predecessors to what became later expert systems.

Read more about AI in Education: The Future of Teaching and Learning.

Teach a computer how to perform a task without explicitly programming it to perform said task. Instead, feed data into an algorithm to gradually improve outcomes with experience, similar to how organic life learns. The term was introduced in 1959 by Arthur Samuel at IBM, who was developing artificial intelligence that could play checkers.Half a century later, predictive models are embedded in many of the products we use every day, which perform two fundamental jobs:

• Classify data
• Make predictions about future outcomes

The machine learning process includes the following steps:

1. Acquire and clean up data: lots and lots of data. The better the data represents the problem, the better the results. “Garbage in, garbage out.” The data needs to have some kind of signal to be valuable to the algorithm for making predictions.
2. Feature engineering: transform raw data into features that better represent the underlying problem.
3. Separate data into training and testing sets: the training data is fed into an algorithm to build a model, and then the testing data is used to validate the accuracy or error of the model.
4. Choose an algorithm: might be a simple statistical model like linear or logistic regression, or a decision tree that assigns different weights to features in the data. Or, you might get fancy with a convolutional neural network, which is an algorithm that also assigns weights to features but also takes the input data and creates additional features automatically. This is extremely useful for data sets that contain things like images or natural language, where manual feature engineering is virtually impossible.
5. Train the algorithm: every one of these algorithms learns to get better by comparing its predictions to an error function. If it’s a classification problem like, is this animal a cat or a dog?, the error function might be accuracy. If it’s a regression problem like, how much will a loaf of bread cost next year, then it might be mean absolute error.

Deep learning is the next layer of our Venn diagram. Well, it’s deep learning in the sense that with deep learning, we use these things called neural networks. Neural networks are ways that, on a computer, we simulate and mimic the way the human brain works, at least to the extent that we understand how the brain works. And it’s called deep because we have multiple layers of those neural networks.

Gen AI is a subset of deep learning, which means it uses artificial neural networks. Can process both labeled and unlabeled data using supervised, unsupervised, and semi-supervised methods. Large language models are also a subset of deep learning. Generative AI is powered by large language models (LLMs), which are trained on massive datasets and can process and produce text with human-like finesse.

These models use layers of algorithms to create neural networks that simulate a web of neurons, similar to the human brain. Each layer builds upon the previous one, refining and perfecting the output until it’s something entirely new yet strikingly familiar. Practical uses of Generative AI practical uses of Generative AI, including:

• Financial services: forecasting market trends and personalizing investment advice
• Healthcare: crafting personalized therapy programs that consider individual health histories
• Scientific research: simulating complex biological processes and generating new hypotheses

Read more about What is generative AI: A comprehensive guide (2024).

AI, machine learning, deep learning, and generative AI are interconnected, they each play important roles in the broader field of artificial intelligence. Machine learning is a subset of AI, that can learn and improve from experience without being programmed. Deep learning, a more specialized branch of machine learning, involves neural networks with multiple layers that enable computers to process and learn from large amounts of data. Generative AI, on the other hand, is a specific application of deep learning that generates images, text, or music, based on the patterns learned from existing data. Together, these technologies are shaping the future of AI and provide unique capabilities.