General intelligence rests on adapting to unfamiliar problems

A definition shared by researcher François Chollet frames intelligence as on-the-fly adaptation to novel circumstances rather than encyclopedic training across known tasks.

When automated systems excel at complicated tasks, onlookers often assume the software understands the world. A computer program can defeat grandmasters in chess, generate coherent essays, and translate across dozens of languages. Yet when faced with a trivial variation of a simple problem that sits outside its training data, the same system frequently fails in unexpected ways. This gap between broad performance on known material and sudden confusion on novel tasks highlights an unresolved question in computer science: what actually constitutes general intelligence?

The distinction between memorized capability and genuine problem-solving matters across the entire development of autonomous technology. If artificial intelligence is measured primarily by benchmark scores on familiar tasks, developers risk building systems that appear proficient while remaining fragile. In practical settings such as automated logistics, medical diagnostics, or critical infrastructure management, a machine that encounters an unexpected situation must resolve the ambiguity safely rather than failing silently or producing catastrophic errors.

The underlying mechanism of reasoning involves a clear operational sequence. An agent perceives a new scenario that does not match its historical data. To function effectively, the agent must identify the relevant rules, discard irrelevant noise, and construct a workable solution under unfamiliar constraints. Systems that merely retrieve patterns from training data cannot complete this chain because they rely on prior exposure rather than real-time synthesis.

What distinguishes genuine intelligence from stored competence?

Genuine intelligence is the capacity to make sense of a new situation immediately rather than relying on pre-existing competence. In a public commentary posted on August 30, 2026, artificial intelligence researcher François Chollet argued that adaptability to novel challenges defines whether an intelligent system is truly general.¹ Chollet wrote that what makes general intelligence general is that, no matter the problem, an agent should show intelligence.¹

Under this perspective, intelligence is distinct from a catalog of pre-trained capabilities. Chollet stated that the core property is not a-priori competence, but rather the ability to make sense of a new problem on the fly.¹ The primary goal of developing general intelligence is therefore adapting to arbitrary circumstances rather than preparing specific routines for every conceivable eventuality.¹

Why do current automated systems struggle with basic failures?

Current machine learning models struggle with novel tasks because high performance on standard benchmarks does not guarantee operational robustness. The discussion around Chollet's definition also included observations on system limitations from researcher Jerry Tworek. Tworek recounted telling a former technical team that the most basic failure modes matter more than peak capabilities, noting that he cared less about the smartest thing a model can do than about the most stupid thing that it cannot do.¹

Tworek emphasized that robustness remains a significant limitation to increased automation.¹ When software is deployed in real environments, unexpected edge cases inevitably arise. If a model lacks the capacity to adapt dynamically to unfamiliar inputs, its peak capabilities cannot compensate for its vulnerability to basic reasoning failures.

What does this framework mean for evaluating future artificial systems?

Evaluating future systems under an adaptation-centered framework requires testing machines against tasks they have never encountered during training. Standard evaluations often measure how well a model recalls or interpolates between known data points. Assessing general intelligence instead demands benchmarks that present novel puzzles with minimal prior context, measuring how efficiently an agent acquires new skills in real time.

This perspective cannot resolve all engineering hurdles on its own. Defining intelligence as on-the-fly adaptation sets an operational standard, but it does not specify the exact mathematical architectures or learning algorithms required to achieve it. The observation also relies on conceptual arguments and qualitative evaluations rather than a newly published empirical study with unified statistical baselines.

The broader implication for technology developers is that scaling existing model sizes may not automatically produce general intelligence. If models simply expand their lookup tables of known human knowledge, they will continue to struggle whenever an unpredicted event occurs. Future work in the field will test whether novel training paradigms can foster genuine abstraction and runtime reasoning.

This piece was prepared from public records; the authors have not been interviewed.

References

This article is based on 1 source, listed in the order they are cited.

1. 1 H https://x.com/fchollet announcement · 30 Aug 2026 François Chollet Defines General Intelligence as Adaptation to Novel Problems See the source