What Exactly Is AGI? Expectations and Reality of a "Universal AI"
AGI (Artificial General Intelligence) is, roughly speaking, a general-purpose AI capable of handling a wide range of intellectual tasks at a human level, not specialized to a single task. Not only text generation, but also planning and execution, re-learning according to circumstances, and adapting to unknown problems—such is the image often described.
But the current mainstream models (LLMs: large language models) are basically trained to guess the next likely word from training data, even if they seem smart. Therefore, long-term consistency, real-world causality, accuracy, and the ability to verify for themselves and raise confidence remain weaknesses.
So how are each company trying to bridge those gaps? From here, as a map to the path toward AGI, we outline the thinking and current standings of major players.
The Five Main Routes to Getting Closer to AGI
Although wording varies by company, the efforts can be broadly categorized into five routes.
- Scaling route: Increase model/data/computational resources to push capabilities upward
- Reasoning/inference enhancement route: Elevating the 'thinking ability' with Chain-of-Thought, search, and self-verification
- Agent route: Moving toward 'getting the job done' through tool use, planning, and execution
- Multimodal route: Expanding understanding of the world with images, audio, video, robotics, etc.
- Safety and governance route: Strengthening control and operations as much as capability, toward societal deployment
In reality, it’s not about winning with a single route, but about bundling and advancing multiple routes.
OpenAI: Advancing through Scaling, Reasoning, and Product Integration
OpenAI, in addition to scaling to raise the capabilities of LLMs, has recently shifted focus toward reasoning and agent-like usage. In products, it centers on ChatGPT, integrating it into offerings usable by individuals to enterprises.
Approach highlights
- Strengthening reasoning: Provide a mode to carefully solve hard problems, increasing accuracy
- Tool integration: Bundle search, code execution, file analysis, etc., to complete the work
- Safety: Strong emphasis on model evaluation, staged releases, and policy operation
Current status (roughly)
The generality as an LLM is very high, but long-term plan stability and real-world verification (confirming "is this really the case?" by themselves) are still challenges. In other words, it's close to an excellent partner, but still needs another leap to be a colleague you can autonomously delegate to.
Google DeepMind: Depth of Research, Multimodality, and Scientific Applications
DeepMind has a strong research orientation and remains a consistent presence in AGI discussions. Recently, Gemini serves as the axis, advancing model design assuming multimodal inputs (not just text but images, audio, video, etc.).
Approach highlights
- Multimodal: Handle multiple information modalities from the start, broadening the foundation of understanding
- Reasoning and tools: Connect to search and various services to supplement accuracy and execution capability
