Generative AI vs General AI - Two Faces of Artificial Intelligence
Have you ever wondered why ChatGPT can write a convincing recipe but doesn't understand the true taste of food? Or why DALL-E creates stunning images but doesn't comprehend what they actually represent? The answer lies in the fundamental difference between what today's AI systems can do and what we aspire to achieve in artificial intelligence development.
Introduction
Artificial intelligence (AI) is revolutionizing nearly every aspect of our lives - from healthcare to business and entertainment. In today's technological landscape, two main streams emerge: generative AI (GenAI) and artificial general intelligence (AGI). While interconnected, these two faces of artificial intelligence represent different approaches to technology development and present us with distinct challenges and opportunities.
Comparison of Generative AI and General AI
| Feature | Generative AI | Artificial General Intelligence (AGI) |
|---|---|---|
| Goal | Creating new content based on existing data | Achieving human-level intelligence capable of understanding, learning, and adapting across various domains |
| Capabilities | Generating images, text, music, code | Language understanding, problem-solving, learning, creativity, adaptation |
| Applications | Marketing, design, medicine, entertainment, e-commerce | Robotics, education, medicine, scientific research |
| Development Status | Actively developed and implemented | Theoretical concept, not yet achieved |
| Learning Type | Unsupervised or semi-supervised learning | Various learning types, including reinforcement learning, supervised and unsupervised learning |
| Required Data | Large datasets for pattern learning | Potentially smaller but more diverse datasets enabling knowledge generalization |
| Innovation | Ability to generate original content based on existing data | Ability to create entirely new concepts and solutions beyond existing data |
Generative AI: The Creative Force of Modern Technology
Generative AI enables machines to create new content - text, images, music, and video. It's like teaching a computer creative skills that we previously considered exclusively human. At the heart of this technology lie two interesting approaches:
- Generative Adversarial Networks (GANs) function similarly to a master-apprentice relationship. Imagine a novice artist (generator) and an experienced art critic (discriminator). The artist creates images, and the critic evaluates whether they look realistic. With each attempt, the artist learns and refines their craft based on the critic's feedback. This continuous learning and improvement process leads to increasingly better results.
- Transformers can be compared to a very attentive reader who can understand context and meaning in text. When reading a book, we remember not just the last sentence but also previous threads and can connect them. Transformers work similarly - analyzing text, they can capture relationships between words and sentences, even if they appear in distant parts of the text.
Generative AI finds applications in many fields, e.g.:
- In marketing and design - personalizing advertising content and creating 3D product models
- In entertainment - generating music, scripts, and avatars for virtual worlds
- In e-commerce - enhancing personalization and creating product recommendations
Communication with Different Types of AI
How we communicate with AI varies significantly depending on whether we're working with generative systems or aiming for human-like interaction, as in AGI development.
In current generative AI systems, precise prompt formulation plays a crucial role. For instance, when generating images through DALL-E or Midjourney, a detailed prompt describing the desired result directly affects the quality of the generated image. Similarly, with language models, how we formulate our query determines the usefulness of the response.
In contrast, AGI development aims to create systems that understand natural, human ways of communication. AGI should be capable of interpreting ambiguous statements, understanding context, and adapting its response to the situation - similar to how humans communicate in everyday conversations. This represents a fundamental difference compared to current generative systems that require precise instructions.
Current and Future Applications of Both AI Types
Comparing current AI systems with the vision of AGI is best illustrated through specific applications in various sectors.
In medicine, current generative AI systems specialize in narrow but important tasks - like analyzing medical images or generating examination descriptions. Their effectiveness is high but limited to specific, specialized tasks. Meanwhile, AGI could offer comprehensive medical diagnosis, combining knowledge from various specializations, analyzing patient history and the latest scientific research - similar to an experienced doctor using their complete medical knowledge.
The difference is equally clear in the business sector. Current generative tools, like Gemini Deep Research, can analyze data and create reports within a specific scope. AGI, however, could independently identify new business opportunities, predict market trends, and propose innovative solutions, going beyond available data analysis. This represents a transition from a decision-support tool to a system capable of strategic thinking at the level of an experienced board of directors.
Ethical Challenges in Generative and General AI Development
Both generative AI and AGI present us with various ethical challenges directly related to their nature and capabilities.
With generative AI, the main challenge is its ability to create convincing but potentially false content. Generative models can create realistic deepfakes, convincing disinformation texts, or content deceptively similar to copyrighted works. Additionally, these systems often reflect biases present in training data - for example, when generating images of various professions, they may perpetuate gender or racial stereotypes.
In the context of AGI, ethical challenges become even more significant. A system with human cognitive abilities would need to be equipped with solid foundations for ethical reasoning. The question arises of whose moral values should be implemented in such a system and how to ensure it will make decisions aligned with humanity's well-being. This becomes particularly important in the context of medical planning or automation of decision-making processes in justice systems.
Privacy issues also differ depending on the type of AI. Generative AI requires enormous amounts of training data, raising questions about the right to use private content in the learning process. With AGI, the problem becomes even more complex - a system with human cognitive abilities could potentially analyze and process personal data in ways far more advanced than current systems.
Limitations of Current Systems and Challenges in AGI Development
Analysis of both AI types' limitations helps us better understand the differences between them and the challenges facing their development.
Generative AI, despite its impressive content creation capabilities, encounters clear limitations. These systems can generate convincing texts or realistic images, but they lack true understanding of what they create. A model can write technically correct program code but doesn't understand its actual operation. It can create an image of a cat but doesn't know what a cat really is or what role it plays in the ecosystem.
For AGI, the challenges are even more fundamental. The Moravec paradox indicates that the most difficult aspects to recreate are not advanced mathematical or logical operations, but basic human abilities - like intuitive understanding of the physical world or the ability to reason based on common sense. While current generative AI can create an image of a person lifting a cup, AGI would need to understand basic principles of physics, ergonomics, and the purpose of such an action.
These differences show two distinct levels of challenges: generative AI struggles with limitations within its narrow specializations, while AGI development requires breaking through fundamental barriers in understanding the world at the level of human intelligence.
Summary
Comparing generative AI and AGI reveals two different faces of artificial intelligence development. Generative AI, already present in our lives, shows how effectively we can automate content creation and support work in many fields. However, its limitations are clear - it operates effectively within defined specializations but lacks deeper understanding of context and meaning in the content it generates.
AGI, meanwhile, remains an ambitious goal, requiring not just technological progress but also a deeper understanding of human intelligence. While generative AI excels at specific tasks, the path to AGI requires solving fundamental problems related to world understanding, learning, and adaptation to new situations.
These two development directions are not mutually exclusive. Experience gained in developing generative systems can bring us closer to understanding how to create truly intelligent systems. At the same time, AGI research helps us better understand the limitations of current systems and indicates directions for their improvement.
Note!
This article is an example of practical use of generative AI in creating educational content. It was both originally written and translated from Polish by Claude 3.5 Sonnet, where the AI model supported the writing process while human guidance provided critical analysis and necessary corrections. Successive iterations of the text, resulting from human-AI dialogue, helped achieve appropriate merit and adapt the content to readers' needs. This is a practical demonstration of the capabilities and limitations of contemporary generative AI discussed in the article. If you notice any translation errors, please let me know in the comments.
