Artificial Intelligence

Artificial Intelligence, or AI, was coined by John McCarthy in 1956. It's been a hotly contested term ever since. mainly due to the implications of the word "intelligence." We can divide AI into two categories.

What we have right now:

Narrow/Weak AI: a program or machined trained on one thing, and it (hopefully) does that one thing well.

What we have in movies:

Strong AI: A more general, overall intelligence. Think Terminator.

Black Box AI: Referring to things we don't understand or see - many machine learning algorithms are black boxes because we don't fully understand how it is making its decisions

GPT: Generative Pre-Trained Transformer - a type of language model

LLM (Large Language Model): Machine learning models that recognizes and generates text, because it has been trained on massive sets of textual data

Machine Learning: Think of machine learning as a field of study within AI; in machine learning, an algorithm may identify rules and patterns in the data with a human specifying them

Natural Language Processing: Another field of study within AI; it uses an understanding of structure, grammar, and the meaning of words to help a computer "understand." Some examples include speech to text, Grammarly, text summarizers, etc. 

Neural Networks: A method in artificial intelligence that teaches computers to process data in a similar way to the human brain

Deep Neural Networks: Many layered neural networks - the input goes through many layers before you get the output in this type of model

Supervised Learning: In contrast to machine learning, this is a model or an algorithm that has human help, such as already labeled data

Training Data: The data inputted into algorithms and models in order to create something like machine learning models or ChatGPT