The New Language of the AI World: Key Terms You Need to Know in 2024

Artificial intelligence (AI) technologies are not only transforming the world at a rapid pace but also shaping an entirely new terminology. Today, abbreviations like LLM, RAG, or RLHF are frequently heard at product presentations, tech meetings, or investment panels. Even for industry professionals, these concepts can sometimes be confusing. According to TechCrunch, understanding these terms correctly is key to successful communication in the modern tech environment. About this, Techcrunch.com reports .

One of the most discussed yet most abstract concepts in the field of artificial intelligence is AGI (Artificial General Intelligence). OpenAI CEO Sam Altman describes it as "the equivalent of an average human you could hire as a coworker." Google DeepMind takes a more cognitive approach, defining AGI as a system that matches humans in most intellectually valuable tasks. Simply put, it is a technology that can think like a human and perform almost all economically valuable work.

AI Agents and Compute Power

The concept of "AI agents," which are more complex than simple chatbots, is gaining popularity today. These are not just programs that answer questions, but tools capable of executing chains of tasks on behalf of the user. For example, they can book flights for you, fill out expense reports, or write and debug code. Such agents connect with other services via APIs (application programming interfaces) and manage complex processes without human intervention.

For these complex systems to function, a resource called "Compute" is required. This term refers to the GPUs (graphics processing units), CPUs, and other hardware needed to train and run AI models. Today, the market position of companies like NVIDIA is growing rapidly due to the global demand for compute power.

Logical Reasoning and Deep Learning

The "Chain-of-thought" approach plays an important role in the development of AI models. In this process, AI doesn't immediately answer a complex question but breaks it down into small logical steps. For example, when solving a math problem, just as a human uses pen and paper, AI analyzes intermediate steps. This improves answer accuracy and reduces errors in logic and programming, even though the process requires somewhat more time.

It's also important not to forget the concept of "Deep Learning," which is the foundation of artificial intelligence. Below are its key characteristics:

• It uses multi-layer artificial neural networks similar to neural pathways in the human brain.
• It can independently identify important features in data without human assistance.
• It improves its results through learning from errors and repetition.
• It requires millions of data points and massive compute resources to work effectively.

In Uzbekistan's tech ecosystem, these terms are also being increasingly used. While local developers and startups are building their products through APIs provided by OpenAI or Google, this glossary serves as an important guide not only for professionals but also for the general public interested in technology.