Pengertian Istilah AI, Diuraikan untuk Manusia

Artificial intelligence is currently a popular topic in the tech industry, with many companies discussing their advancements in using or developing AI technology. However, the field of AI is filled with complex jargon that can make it difficult to understand the latest developments. To help clarify some of the common AI terms, we have compiled a list to explain their meanings and significance.

AI, short for artificial intelligence, is the discipline of computer science dedicated to creating computer systems that can think like humans. However, in current conversations, AI is often portrayed as a technology or entity, making its definition less clear. Companies like Google emphasize their investments in AI to improve products and offer intelligent tools like Gemini. AI models such as OpenAI’s GPT power many AI tools, while Meta CEO Mark Zuckerberg refers to individual chatbots as AI.

As companies promote AI as the next big innovation, the terminology and nomenclature surrounding AI can become confusing. To provide a better understanding, we have outlined key terms in AI, focusing on making computers smarter.

Machine learning involves training systems on data to make predictions and learn new information, playing a critical role in many AI technologies. Artificial general intelligence (AGI) aims to create AI as smart as or smarter than humans, potentially raising concerns about superintelligent machines. Generative AI can produce new text, images, and more, but may exhibit “hallucinations” or inaccuracies due to training data biases.

AI models, such as large language models (LLMs) and diffusion models, are trained on vast datasets to perform tasks like generating natural language text or creating images from text prompts. Foundation models serve as the basis for various applications without specific training, while frontier models represent upcoming advancements in AI technology.

Training AI models involves analyzing datasets to make predictions and recognize patterns, often requiring significant resources and computing power. Parameters within AI models determine how inputs are converted into outputs, influencing the complexity of the model. Additional terms like natural language processing (NLP) and inference relate to the machine’s ability to understand human language and generate responses.

Overall, understanding these AI terms can provide insight into the advancements and challenges within the field of artificial intelligence.