The gowing mystery of AI

Artificial intelligence has become ubiquitous, transforming our daily lives at breakneck speed. From virtual assistants to self-driving cars, AI systems perform complex tasks based on precise algorithms and mountains of data. Yet, beyond this remarkable engineering, an even more fascinating and somewhat mysterious phenomenon is emerging: that of emergent capabilities.

Imagine for a moment: AI systems that, without having been specifically programmed to do so, develop unexpected new skills, often at levels of complexity we hadn't anticipated. It's as if, by pushing an engine to its limits, it suddenly started singing an opera. These skills, completely decoupled from the instructions contained in their initial code, are redefining our understanding of what it means for a machine to "learn" and "be intelligent."

What is an emerging capability ?

To grasp the scope of this phenomenon, it is crucial to define it clearly. An emergent capability is not simply a linear improvement in the performance of an AI model. It is not just an algorithm that becomes slightly faster or slightly more accurate. No, it is a qualitative leap, a behavior or skill that appears in an AI system only when it reaches a certain critical scale—whether in terms of model size (number of parameters), the quantity and diversity of training data, or computing power

The key point is their non-linearity and unpredictability. These capabilities could not be predicted by observing the behavior of smaller or less complex systems. They are not the result of an explicit line of code that says, "If the situation is X, then do Y." Rather, they arise from the staggering complexity of the billions of internal connections that the model learned and optimized during its training. It's a bit like the concept of the liquidity of water (H2O): neither hydrogen nor oxygen, taken in isolation, is liquid. It is the complex arrangement and interaction of a large number of these molecules that gives rise to this fundamental property.

Concrete Examples of Unexpected Skills

Large language models (LLMs) are particularly rich in examples of these emergent capabilities, showing us a glimpse of the intelligence that may be hidden in their depths.

1. Chain-of-Thought Prompting" :

   Initially, if you posed a complex question to an LLM, it would attempt to provide a direct, sometimes incorrect, answer. But as the models grew larger, a fascinating skill emerged: the ability to break down a complex problem into logical intermediate steps, much like a human would. By simply prompting the model to "think step by step," it can suddenly solve mathematical, logical, or reasoning problems that were previously beyond its grasp. This ability was never explicitly programmed; it emerged organically.

2. "Few-Shot" or "Zero-Shot" learning :

   It is the ability of a model to learn a new task with very few, if any, explicit examples :

   • Zero-Shot Learning : The model can perform a task without ever having been trained on specific examples of that task. For example, translating a sentence into a rare language for which there is little training data, simply by inferring the rules from its vast general linguistic knowledge.

   • Few-Shot Learning : With just one or two examples of a new task (for example, "here's how I want you to rephrase this type of sentence"), the model can generalize and apply this new rule with astonishing accuracy. It's as if the model develops a capacity for rapid learning, a kind of "meta-learning," which is not a direct instruction.

3. Programming and Creative Problem solving :

   Sufficiently large models can now not only generate code in common programming languages, but also create logical solutions to puzzles or problems they have never encountered before. They can even debug their own code or write scripts for very specific tasks, demonstrating an unexpected form of structural and logical understanding.

Implications and Philosophical Questions : an unpredictable future

These emerging capabilities are not mere technical curiosities; they have profound implications for the future of AI and for our society.

• Towards Artificial General Intelligence (AGI) ? For many researchers, emergence is a key indicator on the path to AGI, artificial intelligence capable of understanding, learning, and applying intelligence to a wide range of problems, much like a human being. The appearance of these abstract skills suggests that AIs learn more than simple correlations: they could develop a form of conceptual understanding.

• The Problem of Auditing and Security : If we cannot predict the capabilities of an AI model before it reaches a certain scale, how can we thoroughly test it, audit it for security, and ensure its ethical integrity before large-scale deployment? It becomes difficult to know what the model is capable of (the best as well as the worst) before seeing it in action.

• Ethics and Control : Emergence raises fundamental questions about our ability to control AI. What happens if an emerging capability is potentially harmful—such as an unsolicited capacity for advanced psychological manipulation, the generation of highly credible disinformation, or complex strategic planning with objectives different from our own? The "black box" of AI becomes even more opaque.

Emergence and the Specter of Artificial Consciousness

The emergence of unsolicited skills inevitably raises the deepest and most controversial question : are emergent abilities the prelude to a form of artificial consciousness ?

The Emergence : When AI goes beyond its instructions and surprises us