Intelligence and AI, and What is AGI
Intelligence is the ability to acquire and apply knowledge and skills, as well as to adapt to new situations and solve problems. Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI can perform tasks such as learning, reasoning, planning, decision making, and natural language processing. However, AI is not the same as human intelligence, because it is limited by the data, algorithms, and hardware that it uses. AI can also have biases, errors, and ethical issues that affect its performance and impact. Therefore, AI can be seen as a form of intelligence, but not a replacement or equivalent of human intelligence.
One possible definition of intelligence in data science and machine learning circles is the capability of a machine to imitate intelligent human behavior by using methods, algorithms, systems, and tools to extract insights from structured and unstructured data and to improve performance or inform predictions based on collecting and analyzing large amounts of data .
One of the most ambitious goals of artificial intelligence is to create artificial general intelligence (AGI), a system that can perform any intellectual task that a human can. However, current AI systems are mostly specialized for specific domains or tasks, and lack the ability to generalize across different contexts and modalities. A possible way to overcome this limitation is to chain together large language models (LLMs) and agents, which are AI systems that can interact with their environment and other agents through natural language. LLMs are powerful models that can generate natural language and encode a vast amount of knowledge and common sense from massive amounts of text data. Agents are systems that can use LLMs as their interface to communicate, reason, plan, and execute actions on various platforms or systems. By chaining LLMs and agents together, we could potentially create AGI systems that can leverage the strengths of both components and perform complex tasks that require multimodal inputs and outputs, such as playing games, writing code, or designing products. Some examples of frameworks that explore this idea are ProAgent (Zhang et al., 2023), which uses LLMs to fashion proactive cooperative agents that can anticipate teammates' decisions and formulate enhanced plans; AgentChain (Jina AI, 2023), which uses LLMs for planning and orchestrating multiple agents or large models for accomplishing sophisticated tasks; and ULTI-A (Wang et al., 2023), which harnesses the power of intelligent LLM agents for collaborative problem solving.