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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.
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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:
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