If you are new to AI, you will hear a lot of buzzwords and technical jargon that can feel overwhelming. In this article, I break down 9 essential terms that you need to know to increase your understanding of AI and equip you to participate in conversations with confidence. By understanding these key concepts, you'll be able to follow along with discussions about AI, make informed decisions about its use, and even contribute your own ideas to the conversation.
AI - Artificial Intelligence
AI is about creating machines that can simulate human intelligence, so they can carry out tasks without explicit programming. The idea is that these systems become more efficient and adaptive over time, offering solutions to complex problems and potentially improving various aspects of our lives, from healthcare to transportation.
AGI - Artificial General Intelligence
AGI represents the next frontier in AI, where machines can achieve a level of intelligence comparable to humans across a wide range of tasks. It's the holy grail of AI research, as it would unlock the potential for machines to truly think and learn like humans, opening up possibilities for rapid innovation and problem-solving.
ML - Machine Learning
Machine learning is the engine that drives AI's growth. Instead of programming a computer to do specific tasks, ML allows the computer to learn from data, extracting patterns and making predictions. This way, machines can get better at tasks over time, making them valuable tools for businesses and researchers alike.
NLP - Natural Language Processing
NLP is all about making it easier for humans to interact with machines using language. This technology helps computers understand the nuances of human language, enabling them to perform tasks like summarizing articles, translating text, or even engaging in conversation.
NLU - Natural Language Understanding
NLU is a crucial component of NLP, focusing on the comprehension aspect of language. By getting machines to understand the meaning behind words and phrases, we can create AI systems that are more effective in assisting or collaborating with humans in various applications.
LLMs - Large Language Models
LLMs are like the swiss-army knives of AI, capable of understanding and generating human-like text. By training these models on massive datasets, they learn to capture the essence of human language, allowing them to be used in a wide range of applications, from chatbots to content creation. Examples of LLMs include GPT-4, PaLM, and LLaMA.
GPT - Generative Pre-trained Transformer
GPT is a cutting-edge LLM developed by OpenAI that's pushing the boundaries of what AI can do with language. Its power comes from a transformer architecture that can predict and generate text based on input, making it useful for various tasks, including conversation and summarization.
On one hand, ChatGPT is a specific version of GPT specifically designed for conversational tasks. It's been fine-tuned to engage with humans in a more natural manner, making it a valuable tool for businesses, researchers, and developers who want to leverage AI in their communication and support systems. On the other hand, the term ChatGPT is also used to refer to the API and interface that enables users to interact with the underlying GPT model, like GPT-3.5 Turbo. In this context, you can think of ChatGPT as a conversational AI application. Here's an excellent article by Mary Newhauser on GPT-4 and ChatGPT.
RLHF - Reinforcement Learning from Human Feedback
RLHF is a learning technique that combines human intuition with machine learning. By incorporating human feedback into the learning process, AI agents can better align their actions with our values and preferences. RHLF is used to improve the performance of AI systems including in the training of models like GPT-4. Learn how OpenAI used RHLF to train models that are much better at understanding user intention.