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Defining Key AI Terms


Definitions for key AI terms
Definitions for key AI terms
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Artificial IntelligenceConversation Automation
Published 06/21/23
5 minutes read

By its very nature, the world of AI is ever-changing. If you’re researching AI-powered solutions for your business, you’ll need to understand these key terms.

Generative AI is a subset of artificial intelligence that uses machine learning techniques to generate data that resembles real data. It’s often employed to create new, synthetic information that the AI has not been trained on before, while still maintaining a realistic quality. This might include images, text, speech, or music.

Generative AI often utilizes architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to learn and generate new content. When using a model like GPT (Generative Pre-trained Transformer), refers to the application of machine learning techniques to create new, high-quality, human-like text. GPT, which is a type of large language model, can generate paragraphs of text that feel as if they were written by a human.

A Large Language Model (LLM) is a type of artificial intelligence model trained on a broad range of internet text. These models, like GPT-4, have the ability to generate human-like text when provided with a prompt. They analyze the input given to them and produce a relevant response or continuation.

LLMs are capable of tasks like translation, question-answering, summarization, and more. However, they do not understand text in the same way humans do because they do not have a real-world understanding or experiences, they simply predict what comes next in a sequence based on patterns they have learned during training.

Transformers are a type of machine learning model architecture used primarily in the field of natural language processing (NLP). They were introduced in a paper titled “Attention is All You Need” by Vaswani et al., in 2017. The transformer model introduced the concept of the “attention mechanism”, which weighs the influence of different words when creating a representation of the sentence.

In traditional sequential models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory), the input data is processed in a sequential order which can lead to difficulties when dealing with long-range dependencies within the text. On the other hand, transformers overcome this issue by processing the entire sequence of data at once, thus allowing for better handling of such dependencies.

A key feature of transformers is the “self-attention” mechanism that enables them to focus on different parts of the input sequence when producing an output, capturing the context of words in a sentence regardless of their position. This mechanism has proved to be highly effective for a variety of NLP tasks, such as translation, summarization, and sentiment analysis.

Generative Pre-trained Transformer (GPT) is a type of artificial intelligence model for natural language processing tasks. GPT is part of the transformer model family and utilizes their self-attention mechanism.

The “pre-trained” component of GPT refers to the model’s initial training phase, where it is trained on a large corpus of text data to understand the statistical properties of the language. This includes predicting the probability of a word given all the previous words in a sentence. The pre-training allows the model to generate coherent, contextually relevant sentences.

The “generative” aspect refers to the model’s ability to generate new text based on the input it’s given. After the pretraining phase, GPT can be fine-tuned on specific tasks, such as translation, summarization, question-answering, and more. However, its most distinct feature is arguably its ability to generate creative, human-like text.

ChatGPT is a specific application of the Generative Pre-trained Transformer (GPT) model developed by OpenAI. It’s designed to generate human-like text responses in a conversational manner. This makes it useful for a range of applications such as drafting emails, writing code, creating written content, tutoring, translating languages, simulating characters for video games, and even as a chatbot for customer service.

ChatGPT is pre-trained on a large corpus of Internet text, but it doesn’t know specifics about which documents were in its training set or have the ability to access any personal data unless explicitly provided in the conversation. It generates responses to prompts by predicting what text should come next given the input, based on patterns it learned during its training.

It’s important to note that while ChatGPT can generate impressively coherent and contextually relevant responses, it doesn’t truly understand the text or have beliefs, desires, or opinions.

Other relevant terms & definitions in the realm of AI:

    • Machine Learning (ML): A subset of AI that gives computers the ability to learn patterns from data and make decisions or predictions without being explicitly programmed to perform the task. It’s the technology that underlies many modern AI systems.
    • Deep Learning: A subfield of machine learning that focuses on algorithms based on artificial neural networks, particularly deep neural networks. It’s the key technology behind many advanced AI applications, including image and speech recognition.
    • Neural Networks: A set of algorithms modeled loosely after the human brain, designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input.
    • Supervised Learning: A type of machine learning where the model learns from a labeled dataset, where the right answers are provided. After sufficient training, the model can start predicting outcomes for unseen data.
    • Unsupervised Learning: A type of machine learning where the model learns from an unlabeled dataset. The model has to identify patterns in the data and make sense of it on its own.
    • Reinforcement Learning: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a reward signal.
    • Natural Language Processing (NLP): A subfield of AI that focuses on the interaction between computers and humans through natural language. The goal is to enable computers to understand, interpret, and generate human language in a valuable way.
    • Natural Language Understanding (NLU): A subset of NLP that focuses on machine reading comprehension. It involves the use of AI to understand and interpret human language in text or voice format.
    • Natural Language Generation (NLG): Another subset of NLP, NLG is the use of AI to generate text that reads as if a human wrote it. This technology is used in applications like automated reporting and content creation.
    • Sentiment Analysis: Also known as opinion mining, sentiment analysis uses NLP and text analysis to identify and extract subjective information from source materials. This is often used to understand opinions, emotions, and attitudes expressed in a text.
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