Introduction to Claude Artifacts

In the past year, our understanding of AI has increased as programs like ChatGPT, DALL-E, and Midjourney have become ubiquitous tools in our daily lives. These programs all fall under the same category of generative AI, a type of AI that excels at synthesizing new text and media content. But AI can do much more than generate content, and it’s worth exploring and familiarizing yourself with the technology’s full range of possibilities. 

Before we get into the different types of AI, it helps to define what AI is. People often use AI as an umbrella term to describe many different techniques, says Nitya Mandyam, Codecademy Senior Curriculum Developer. (Fun fact: Along with creating a ton of our data science courses and content, Nitya’s also pursuing a postgraduate degree in AI Ethics.)  

“AI is an interesting field because it’s kind of defined by the end goal, which is achieving human or superhuman intelligence, rather than what it actually is,” Nitya says. “Curiously, this lack of a precise universally accepted definition has probably helped the field grow and blossom.” Simply put, AI refers to intentionally constructed systems that actively interact with the world and are usually guided by large data models.

Early AI systems were inspired by mathematical logic and sought to mimic conscious thought processes through rules. (For example, in the late 90s, IBM built a chess-playing commputer called Deep Blue that was able to beat a grandmaster in chess.) Today, the many different types of AI that we encounter are much more sophisticated, in part because our understanding of human intelligence is more in-depth, Nitya explains. 

While its roots go back decades, modern AI really accelerated in the last few years. As AI advances and seeps into technology we use, the lines between different types of AI can be hard to distinguish. Ahead, Nitya breaks down the various categories of AI that you might come across and key differences that you should know about each.