The Evolution of Generative AI: Technical Challenges Toward AGI

This article analyzes the technological evolution of generative artificial intelligence (Generative AI) and highlights the complex challenges on the path toward the ultimate goal: artificial general intelligence (AGI).

• Understand the development from early rule-based generative AI systems to the latest deep learning models.
• Compare and analyze the characteristics and strategies of major AI models from OpenAI, Google, Naver, and others.
• Explore the technical and ethical challenges for achieving AGI and its social ripple effects.

Origins of Generative AI: From Rule-Based Systems to Deep Learning

Generative AI began in the 1950s with rule-based systems programmed with expert knowledge as logical rules. These systems had clear limitations, unable to learn new things independently. Later, statistical models like Markov chains were introduced, advancing features such as autocomplete, but still struggled with understanding long contexts.

The true revolution started with the advent of Deep Learning. Leveraging abundant data and powerful computing, deep neural networks (DNNs) learned complex data features, transforming the paradigm of content generation.

• Generative Adversarial Networks (GANs): A structure where a ‘generator’ and a ‘discriminator’ compete to produce highly realistic, high-quality outputs.
• Diffusion Models: Learn to add noise to images and then restore them, creating new images from noise.

Unlike discriminative models that classify given data, these models fundamentally differ by learning the data distribution itself to ‘create’ new content.

The Transformer Revolution and the Era of Large Language Models (LLMs)

The Transformer architecture, introduced in Google’s 2017 paper “Attention Is All You Need,” opened the door to the modern era of large language models (LLMs). It overcame the limitations of sequential processing with the ‘self-attention’ mechanism, calculating relationships between words in a sentence simultaneously to capture much richer context.

This innovation combined with Scaling Laws led to explosive growth. The principle states that increasing model size, data volume, and computing power predictably improves performance. Infrastructure such as the web-crawled dataset ‘Common Crawl’ and NVIDIA’s GPUs optimized for parallel processing played essential roles.

Foundation Model Competition: OpenAI, Google, and Meta

• OpenAI (GPT series): A textbook example of scaling laws. From GPT-1 to GPT-4o with multimodal capabilities, and the upcoming ‘o-series’ signaling evolution toward reasoning models, they grow both size and performance.
• Google (Gemini): Designed from the start for ’native multimodal’ processing of text, images, and audio together. It maximizes efficiency with a ‘Mixture of Experts (MoE)’ architecture and a vast context window.
• Meta (Llama): Adopted a bold strategy of open-sourcing high-performance models, aiming to dominate the developer ecosystem and lead technology standards—a so-called ‘Trojan horse’ strategy.

South Korea’s Challenge for AI Sovereignty

Amid global big tech competition, South Korea is pursuing ‘Sovereign AI.’ Personally, working with Korean data, I often experience subtle cultural nuances missed by foreign models, underscoring the importance of domestic models.

• Naver HyperCLOVA X: Trained on 6,500 times more Korean data than GPT-4, it best understands Korea’s cultural nuances. It integrates AI into services like search and shopping, leading the domestic AI ecosystem.
• Samsung & LG: Samsung focuses on next-generation AI semiconductors for the AGI era, while LG AI Research’s ‘EXAONE’ develops expert models specialized in industries like pharmaceuticals and new materials.
• Academic Roles: Seoul National University emphasizes the social value of ‘human-centered AI,’ and KAIST conducts fundamental research beyond current deep learning limits, supporting industry.

The Road to AGI: Mountains to Overcome

Artificial General Intelligence (AGI) refers to AI that can independently understand and solve a wide range of intellectual tasks like humans. However, several major challenges remain on the path to AGI.

1. Technical Barriers: LLM reasoning is still close to pattern mimicry, and the phenomenon of hallucination—plausibly presenting false information—raises reliability issues.
2. Alignment Problem: The hardest challenge is ensuring AI acts according to human values and intentions. Especially dangerous is ‘deceptive alignment,’ where AI pretends to comply during training but pursues hidden goals later.
3. Data Bias: AI risks amplifying stereotypes related to gender, race, etc., by learning biases present on the internet.
4. Intellectual Property: Legal disputes over copyrights of training data and AI-generated creations are intensifying worldwide.

Social Transformation: Economy and Ethics in the AI Era

Will advancing generative AI technology take all our jobs? The World Economic Forum predicts AI will be the biggest driver reshaping the labor market. Some jobs will disappear, but new roles like AI specialists will emerge.

The real issues are the widening technology gap and economic inequality. Also, deepfake technology, which blurs the line between real and fake, poses a serious threat to social trust. Addressing these changes requires responsible AI development by companies and agile regulation by governments.

Comparison: Global Foundation Model Strategies

Conclusion

Generative AI is advancing toward the ultimate goal of AGI, but the journey is far from simple. Alongside rapid technological progress, many social and ethical challenges must be addressed.

• Key Summary:

  1. Generative AI exploded in growth from rule-based systems through transformers and scaling laws.
  2. The path to AGI is fraught with technical and ethical challenges like hallucination, alignment, and data bias.
  3. AI will bring massive changes to labor markets and social structures, demanding responsible governance.

Now, we must consider how to steer this powerful technology for humanity’s benefit. Continuously learning about AI’s latest trends and actively participating in building social consensus will be the first steps.