Artificial Intelligence (AI)
Artificial Intelligence (AI) is a branch of computer science that focuses on developing machines that can perform tasks that typically require human intelligence. AI encompasses various techniques and algorithms to enable machines to acquire, understand, and apply knowledge in order to mimic human cognitive functions. These functions include perception, learning, reasoning, problem-solving, understanding natural language, and decision-making. AI technology aims to create systems that can think and learn independently, adapt to new situations, and improve over time.
Chatbots
A chatbot is a computer program or software that is designed to interact with humans through text or voice-based conversations. Chatbots use artificial intelligence to understand and interpret user inputs and generate relevant responses.
ChatGPT
ChatGPT is an advanced language model developed by OpenAI. It is an extension of GPT (Generative Pre-trained Transformer), a state-of-the-art model for natural language processing. ChatGPT is specifically designed to engage in conversational interactions with users. ChatGPT leverages deep learning techniques and large-scale training on diverse datasets to generate responses in natural language. It has been trained on a vast amount of text data from the internet, enabling it to understand and generate coherent and contextually relevant responses.
Deep Learning
Deep learning is a subfield of machine learning that focuses on developing and training artificial neural networks to automatically learn and make intelligent decisions from data. It is inspired by the structure and function of the human brain’s neural networks.
Generative AI
Generative AI, also known as generative modeling, refers to a branch of artificial intelligence that focuses on training models to generate new data rather than making predictions or classifications. Generative AI models are designed to understand the underlying patterns and structure of a given dataset and generate new samples that resemble the original data.
GPT-3
GPT-3 is a large language model developed by OpenAI. The model is designed to be flexible and can be adapted to a wide range of tasks, such as answering questions, generating text, completing math problems, and even writing code. Its capabilities have been demonstrated in a variety of applications from chatbots to writing stories to solving math problems.
GPT-4
GPT-4 is the current research model developed by OpenAI and is considered to be the successor of GPT-3. It is much larger than GPT-3 and has more parameters, meaning it can process more data and do more advanced tasks. GPT-4 is designed to be more flexible, adaptive, and accurate compared to its predecessor. GPT-4 will have a multimodal language model, accepting prompts as both text and image, and producing text results.
Hallucination
A hallucination refers to a situation where an AI system generates a response or output that is not accurate or true to the reality of the situation. This can be due to limitations in the model or data used to train the model, or it could be that the AI system is not given enough context or information to generate an appropriate response.
Large Language Model (LLM)
A large language model (LLM) is a machine learning model that has been trained on a large pool of text. These models can be used to generate human-like text, answer questions, perform language translation, and other natural language processing tasks. LLM can be trained on billions of words, making it highly effective at processing and analyzing large amounts of language data.
Machine Learning
Machine learning is a field of artificial intelligence that enables computers to learn and improve from experience, without specifically programmed rules or instructions. The goal of machine learning is to enable machines to learn from large amounts of data, recognize patterns and relationships, and use that knowledge to make predictions or decisions on new data. Machine learning models can be trained on a wide variety of datasets, including images, text, audio, and sensor data. By using machine learning, computers can perform a wide range of tasks, including image recognition, natural language processing, and decision-making.
Multimodal Language Model
A multimodal language model, also known as a multimodal neural network, is a computational model that can understand information in multiple modalities such as text, audio, or video. The model is able to learn relationships and patterns across different modalities and use that information to improve its performance. The use of a multimodal model can help make machine learning algorithms more effective and robust by allowing them to leverage multiple types of information to make predictions or decisions.
Neural Network
Neural networks are a type of machine learning model inspired by the structure and function of the human brain. Neural networks can be trained on large amounts of data, allowing them to learn patterns and relationships that can be used to make predictions or decisions.
OpenAI
OpenAI is an artificial intelligence research lab that develops and publishes open-source AI technologies.
Prompt Engineering
Prompt engineering is a technique used in natural language processing (NLP) to improve the performance of a machine learning model, especially for text-generation tasks. The goal of prompt engineering is to create effective prompts or templates that guide the model in generating more finely tuned responses. The prompts are intended to provide the model with relevant information about the desired output, such as the style, topic, and tone of the text being generated. By crafting effective prompts, the model can generate more nuanced and coherent responses, which can then improve its performance.
