The most recent GPT model version
Posted: Sun Feb 09, 2025 6:51 am
Artificial Intelligence (AI): Intelligence demonstrated by machines, as opposed to intelligence displayed by humans or by other animals. “Intelligence” encompasses the ability to learn and reason, to generalize, and to infer meaning.
Machine Learning (ML): A branch of AI that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Deep Learning (DL): Part of a broader family of ML methods, which summarizes algorithms that are structured in layers to create an “artificial neural network.” These algorithms can learn and nigeria rcs data make intelligent decisions on their own, just like the human brain.
Generative AI (GenAI): A subset of DL models that can learn the patterns of their input training data and then generate high-quality text, images, and other content with similar characteristics.
Generative Pre-Trained Transformers (GPT): A subset of GenAI models that leverage novel transformer architectures, which excel at capturing long-range dependencies and learning contextual relationships in sequences of data.
Large Language Model (LLM): A GPT algorithm that can recognize, summarize, translate, predict, and generate text and other forms of content based on knowledge gained from massive datasets.
GPT-4: trained and provided by OpenAI. It’s the successor of GPT-3.5, GPT-3, and GPT-2.
ChatGPT: A chatbot interface that allows users to interact with GPT-4 and other versions through natural language instead of code.
Source: Andre Retterath
These technological advances have been among the fastest ever adopted with ChatGPT reaching 100 million users within just two months of launching. As AI’s adoption expands across both consumer and enterprise settings, organizations are tasked with implementing governance structures to ensure responsible use – hence the need for AI governance. The International Association of Privacy Professionals (IAPP) defines organizational AI governance as the internal guidelines and practices organizations follow to ensure responsible development, deployment, or use of AI by that organization.
Machine Learning (ML): A branch of AI that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Deep Learning (DL): Part of a broader family of ML methods, which summarizes algorithms that are structured in layers to create an “artificial neural network.” These algorithms can learn and nigeria rcs data make intelligent decisions on their own, just like the human brain.
Generative AI (GenAI): A subset of DL models that can learn the patterns of their input training data and then generate high-quality text, images, and other content with similar characteristics.
Generative Pre-Trained Transformers (GPT): A subset of GenAI models that leverage novel transformer architectures, which excel at capturing long-range dependencies and learning contextual relationships in sequences of data.
Large Language Model (LLM): A GPT algorithm that can recognize, summarize, translate, predict, and generate text and other forms of content based on knowledge gained from massive datasets.
GPT-4: trained and provided by OpenAI. It’s the successor of GPT-3.5, GPT-3, and GPT-2.
ChatGPT: A chatbot interface that allows users to interact with GPT-4 and other versions through natural language instead of code.
Source: Andre Retterath
These technological advances have been among the fastest ever adopted with ChatGPT reaching 100 million users within just two months of launching. As AI’s adoption expands across both consumer and enterprise settings, organizations are tasked with implementing governance structures to ensure responsible use – hence the need for AI governance. The International Association of Privacy Professionals (IAPP) defines organizational AI governance as the internal guidelines and practices organizations follow to ensure responsible development, deployment, or use of AI by that organization.