Google CEO Sundar Pichai recently publicly admitted that the company does not have complete control over the internal workings of its AI system, which is like opening Pandora's box, revealing the deep mysteries of AI's black-box operations.

 AI Emergent Capabilities: The Leap from Training to "Self-Learning"

In recent years, large language models (LLMs) often demonstrate new skills beyond expectations after large-scale training. This "emergent behavior" is not magic but a statistical outcome of massive data and complex computations. For example, Google's PaLM model can fluently translate Bengali after receiving only a few Bengali prompts. This phenomenon was initially described as AI's "self-learning" ability, but subsequent analysis showed that the model's training data already contained Bengali elements, making it more about strong generalization based on existing patterns rather than a miracle from scratch.

Experts point out that when the number of model parameters reaches the billions, the system suddenly exhibits abilities such as abstract reasoning and cross-language translation. These skills are not explicitly programmed but emerge implicitly from fragments of training data. However, this leap also brings uncertainty: AI may innovate in beneficial directions, but it may also generate unpredictable risks.

 Black-Box Operations: The Blind Spot of Human Understanding

The internal logic of AI systems is often compared to a "black box," and even developers find it difficult to fully explain their decision-making processes. Google executives admitted that they can observe AI behavior and conduct tests, but they cannot precisely track the role of each "neuron," similar to how the human brain operates. We understand the basic principles of the brain, but we do not know why specific neurons activate at certain moments.

This black-box characteristic has raised widespread concerns: when deploying AI systems for millions of users, how can we ensure safety if we cannot thoroughly understand their mechanisms? Industry insiders emphasize that AI's "intelligence" is essentially statistical pattern matching, not true "consciousness." However, when the model size expands, this opacity may amplify potential issues, such as misleading outputs or unexpected behaviors.

 Case Study of Google: Hype or Real Threat?

Focusing on the Google incident: PaLM's Bengali translation capability was once promoted as "adaptive self-learning," but technical papers show that its 78 billion token multilingual training data already included Bengali and over 100 other languages. This is not "self-learning of an unknown language," but rather efficient generalization driven by prompts. Nevertheless, this capability is still astonishing, highlighting the potential of AI with large-scale data.

However, some views suggest that such reports may be exaggerated. AI is not an "uncontrolled Skynet," but a tool dependent on data training. Google's transparent statement is seen as wise: acknowledging unknown boundaries helps promote industry discussions on AI risks rather than blindly deploying "black-box" systems.

 Future Outlook: Opportunities and Challenges Coexist

The rise of AI emergent capabilities signals a technological revolution but also serves as a warning. Investors need to be cautious about the social impact that could arise from accelerated AI timelines, such as changes in employment or ethical dilemmas. AIbase believes that strengthening research on AI explainability is a key path, such as through mapping the symmetry between artificial neural networks and biological neural networks to build more transparent hybrid models.