Gen AI for Students

Artificial intelligence (AI) and generative artificial intelligence (Gen AI) share something in common. Both kinds of technology depend on machine-learning models, which use a large amount of data or information to train the models. The type of output that these models generate from all the data is where the difference lies: AI refers to learning models that generate predictions, and Gen AI refers to learning models that create new data (Zewe, 2023).

Figure 1

Unraveling AI Complexity: A Comparative View of AI, Machine Learning, Deep Learning, and Generative AI

Note. From Zhuhadar, 2023. CC BY-SA 4.0 DEED

If you are new to the world of artificial intelligence, take a look at these introductory resources to begin your learning journey!

What is AI?

This video from Common Sense Education provides a basic explanation of artificial intelligence, generative artificial intelligence, and things we need to consider as we use and develop this technology.

Common Sense Education. (2023, September 22). What is AI? [Online video]. YouTube.

How AI Learns

In this Google Applied Digital Skills resource, watch the video for an example of how AI learns with Google's Quick, Draw!

Google for Developers. (2016, November 5). AI experiments: Quick, draw! [Online video]. YouTube.

Try It with Doodles!

Quick, Draw! Built by Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim, and Nick Fox-Gieg, with friends at Google Creative Lab and Data Arts Team.

Try Quick, Draw!

Try It and Clean the Oceans!

Explore more with machine learning by training this AI to recognize fish from garbage with Code.org

Try AI for Oceans!

Textual Description for Figure 1: Unravelling AI Complexity - A Comparative View of AI, Machine Learning, Deep Learning, and Generative AI

This figure is a Venn diagram illustrating the relationships between artificial intelligence, machine learning, deep learning, and generative artificial intelligence. The figure shows artificial intelligence as the central circle with machine learning inside it, deep learning inside machine learning, and generative artificial intelligence within deep learning. The text in each topic box reads:

Artificial Intelligence: AI involves techniques that equip computers to emulate human behaviour, enabling them to learn, make decisions, recognize patterns, and solve complex problems in a manner akin to human intelligence.

Machine Learning: ML is a subset of AI that uses advanced algorithms to detect patterns in large data sets, allowing machines to learn and adapt. ML algorithms use supervised or unsupervised learning methods.

Deep Learning: DL is a subset of ML which uses neural networks for in-depth data processing and analytical tasks. DL leverages multiple layers of artificial neural networks to extract high-level features from raw input data, simulating how human brains perceive and understand the world.

Generative AI: Generative AI is a subset of DL models that generates content like text, images, or code based on provided input. Trained on vast data sets, these models detect patterns and create outputs without explicit instruction, using supervised and unsupervised learning.
Figure title: Unraveling AI Complexity - A Comparative View of AI, Machine Learning, Deep Learning, and Generative AI.