Artificial intelligence, language models, machine learning, algorithms, natural language processing.... to get the hang of a new field, you need to navigate a new vocabulary. I'll be honest; I sourced these definitions from a Q&A session with ChatGPT. This source became widely available in December 2022, and it's pretty easy to use. A disclaimer on its homepage cautions that it is prone to spewing incorrect information, though, so I have cross-checked anything I am putting in here. This page mainly serves as a decent collection of definitions and terms, so I have also edited for clarity and concision. I guess it turns out that we are still relevant...for now.
ME: So what is AI, anyway?
CHATGPT: AI (Artificial Intelligence) refers to the ability of a computer or machine to perform tasks that would normally require human intelligence, such as understanding language, recognizing images, making decisions, and learning. There are different types of AI, including narrow or weak AI, which is designed to perform a specific task, and general or strong AI, which is designed to perform any intellectual task that a human can. AI has the potential to revolutionize many fields and is already being used in a variety of applications, such as virtual assistants, self-driving cars, and financial trading systems.
ME: Are you a strong AI?
CHATGPT: As an AI language model, I am not able to perform tasks that would require human intelligence beyond what I have been trained on. I don't have the ability to sense the world around me or take actions in the real world, so I would not be considered a strong AI. I exist purely to generate human-like text based on the input I receive.
ME: What is a language model?
CHATGPT: A language model is a statistical model that is designed to predict the likelihood of a sequence of words. It does this by analyzing the patterns and relationships between words in large amounts of text data. Language models are used in a variety of natural language processing tasks, such as speech recognition, machine translation, and text generation.
ME: What is natural language processing?
CHATGPT: Natural language processing (NLP) is a field of artificial intelligence and computer science that focuses on the interaction between computers and humans using natural language. It involves using computers to process, analyze, and generate human language, including speech and text.
Some common tasks in natural language processing include language translation, text summarization, sentiment analysis, and question answering. These tasks require the ability to understand the meaning and context of words and sentences, as well as the ability to generate coherent and natural language text. NLP is used in a variety of applications, such as virtual assistants, chatbots, and language translation software.
ME: What is machine learning (ML)?
CHATGPT: Machine learning is a method of teaching computers to perform tasks without explicitly programming them. It is a subset of artificial intelligence that involves training algorithms on data so that they can make predictions or take actions based on that data.
In machine learning, an algorithm is fed a large amount of data and a desired output, and it learns to generate the output by finding patterns in the data. The algorithm is then tested on new data to see how accurately it can make predictions or perform actions based on what it has learned.
There are several different types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. These techniques are used in a variety of applications, such as email filtering, credit fraud detection, and self-driving cars.
ME: Are you using natural language processing (NLP) and machine learning (ML)?
CHATGPT: As a language model, I use natural language processing (NLP) techniques to understand and generate human-like text. I have been trained on a large dataset of text, and I use the patterns and relationships learned from that data to generate responses to user input.
I also use machine learning (ML) techniques as part of my training process. Specifically, I was trained using a type of machine learning called supervised learning, in which I was provided with a large dataset of text and the corresponding correct output for a given task (such as translation or text summarization). I used this dataset to learn to generate the correct output based on the patterns and relationships in the data.
During my training, I was not given any task-specific programming or explicit instructions on how to perform a particular task. Instead, I learned to perform these tasks based on the data I was provided and the machine learning algorithms that were used to train me.
The takeaway: While ChatGPT may not be self-aware, it speaks as though it is. And it's pretty good at explaining itself, its functions, and even its limitations.
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