Discover the World of AI

AI Dictionary

Neural networks are the computer programs on which deep learning is built. Very loosely based on the brain, neural networks consist of many artificial neurons wired together through complex mathematical equations called algorithms. When a new piece of data (a picture of a cat, for example) is uploaded to a system, it cascades through those algorithms until different data (such as the word “cat”) comes out the other end. An untrained neural network will spit out rubbish. A trained neural network, known as a model, is one that has learned to reproduce patterns seen in its training data to produce correct answers (most of the time).

Machine learning uses statistics to find patterns in massive amounts of data. And data, here, can mean a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm. Machine learning powers some of the AI that you’re most likely to have used. Recommendation algorithms like the ones on Netflix, YouTube, and Spotify use machine learning, as do search engines, social media feeds, and voice assistants like Siri and Alexa.

Deep learning is machine learning on steroids. It uses massive data sets, large neural networks, and a lot of computing power to give machines an enhanced ability to find—and amplify—even the smallest patterns. And some of these systems can spot patterns without even being told what to look for.

Generative AI is a popular and powerful kind of AI that’s emerged in the last few years. Image-generating AI developed by Google, OpenAI, and others can now create stunning artworks based on just a few prompts. Type in a short description of pretty much anything, and you get a picture of it in seconds. ChatGPT does the same thing, but for text. There are engines to generate music, videos, and more. These tools have the power to revolutionize the economy and transform entire industries. For example, scientists are training models on specialized data about chemical compounds to try and discover new drugs. (Spoiler alert: no one really knows where the rise of generative AI will lead us.)

Large language models are a kind of generative AI. They’re built on deep-learning algorithms that are trained on enormous amounts of text. That means they can figure out which words are most likely to appear together in a sentence or paragraph and generate passages that sound like what a person might write. ChatGPT is a chatbot based on a large language model (GPT-3 and GPT-4).

Multimodal AI refers to generative AI that processes multiple types of data, like audio, video, text, and images, to return results in any of these formats. Right now, we have a few multimodal models, like Gemini and GPT-4.

Keep in mind—AI isn’t perfect and will get things wrong. It doesn’t actually "know" anything (as far as we know), and there are serious flaws with AI-powered search engines. ChatGPT even reminds you to double check any important information it generates with another source.

Alignment is the work being done to ensure that AI systems do what users want them to do and nothing else.