In recent years, rapid advancements in artificial intelligence have prompted researchers to revisit fundamental questions about the nature and structure of intelligence itself. Despite extraordinary achievements, modern AI systems remain surprisingly brittle, excelling within narrow domains yet faltering dramatically when faced with genuinely novel situations. This reality underscores a crucial distinction: while specialized skill and intelligence are often conflated, they are fundamentally different constructs. Intelligence, in essence, refers to the capacity for efficient adaptation, abstraction, and flexible problem-solving across diverse and unforeseen circumstances, rather than the mere accumulation of static skills or memorized knowledge.

General intelligence (GI), therefore, involves more than just mastering predefined tasks—it encompasses the broader cognitive competencies that allow an agent, human or artificial, to consistently adapt and thrive in dynamic environments. Central to this adaptability is the ability to form abstractions: the cognitive capability to distill complex, high-dimensional information into simplified, reusable mental models. Abstraction not only facilitates efficient generalization but also underpins a system's ability to rapidly acquire and recombine skills in creative ways. Without this critical foundation, intelligence remains rigid and limited, bound by the constraints of prior experiences and unable to break free from learned patterns.

Understanding the capacities required for general intelligence thus necessitates an exploration of core cognitive faculties. Researchers, notably François Chollet, have argued convincingly that genuine intelligence is best characterized by skill-acquisition efficiency—how quickly and effectively an entity can learn to solve new problems given minimal prior exposure or data. This conception shifts the goalpost for AI from performing well on extensively trained tasks to developing an intrinsic capability to grasp entirely new problems swiftly, employing minimal built-in cognitive priors such as basic notions of causality, numerosity, object permanence, and agentness.

In tandem with these foundational cognitive capabilities, general intelligence also demands substantial capacities for reasoning and hierarchical problem-solving. Complex real-world scenarios typically involve layers of interconnected sub-tasks and abstractions, demanding systematic hierarchical decomposition and compositional reasoning. Humans intuitively solve problems by dividing them into manageable pieces, leveraging prior knowledge to identify suitable abstractions, and flexibly recombining those abstractions in novel ways. Replicating this hierarchical and compositional reasoning in artificial systems remains a profound yet critical challenge on the path toward achieving GI.

Furthermore, general intelligence necessitates exceptional flexibility in managing uncertainty, ambiguity, and novelty—hallmarks of authentic real-world environments. True intelligence cannot rely on extensive training sets or exhaustive exposure to every potential scenario; it must robustly handle distributional shifts, incomplete information, and dynamic task requirements. Systems that fail under slight perturbations or minor variations, as contemporary deep learning models frequently do, illustrate clearly that current approaches lack the crucial resilience and adaptability required for genuinely generalizable intelligence.

Efficiency in resource utilization—computational, data-based, and representational—also emerges as a vital characteristic of genuinely intelligent systems. Human cognition demonstrates remarkable information efficiency, capable of mastering tasks quickly from sparse examples, often performing feats of abstraction and reasoning with minimal cognitive load. Replicating such efficiency in AI systems would mean moving beyond brute-force scaling of computational resources and toward approaches that emphasize compact, optimized representations, efficient memory usage, and economical learning strategies. Achieving intelligence thus involves not merely performing effectively but doing so within resource constraints comparable to human cognition.

Lastly, autonomy and the ability to self-modify define perhaps the most profound capacities underlying general intelligence. True GI systems must possess the capability for self-reflection, introspection, and continuous, incremental improvement. They must autonomously identify gaps in their own knowledge, detect and correct errors independently, and dynamically adapt their internal representations and reasoning methods without external reprogramming. Such autonomous self-improvement would mark a transformative shift in AI research, enabling artificial systems to progressively approach, and perhaps even surpass, the generality and flexibility of human intelligence. Exploring these critical dimensions not only clarifies the boundaries of our current achievements but also charts a meaningful path toward genuine artificial general intelligence.

Summary

Group 1: Core Cognitive Capacities

Core cognitive capacities are foundational mental abilities necessary for intelligent reasoning, abstraction, and adaptation, independent of extensive experience.

• Skill Acquisition Efficiency: Intelligence as the speed and effectiveness of acquiring new skills from limited experience.

• Generalization Ability: Handling novel situations beyond prior experiences.

• Abstraction Formation: Distilling complex data into simpler reusable patterns without explicit guidance.

• Adaptability: Dynamically adjusting behaviors without external retraining.

• Core Knowledge Utilization (Innate Priors): Leveraging innate minimal priors (e.g., causality, numerosity) to reason effectively.

• Hierarchical Reasoning: Decomposing problems systematically across multiple abstraction levels.

• Meta-Learning (Learning to Learn): Improving the skill acquisition process through experience.

• Compositionality: Combining simpler learned concepts into novel complex solutions.

• Causal Reasoning: Reasoning explicitly about cause-effect relationships.

• Commonsense Understanding: Intuitive understanding of everyday physical and social dynamics.

Group 2: Generalization & Flexibility

Generalization & flexibility represent the capacity to handle new scenarios effectively, especially under uncertainty or novel circumstances.

• Open-Ended Generalization: Robustly handling entirely new tasks without prior guidance.

• Robustness to Distribution Shifts: Maintaining performance under significant changes from training conditions.

• Cross-Domain Transfer: Applying learned knowledge effectively across fundamentally different domains.

• Zero-Shot and Few-Shot Learning: Quickly performing new tasks with minimal examples.

• Handling Uncertainty and Noise: Reasoning effectively despite incomplete or ambiguous information.

• Continuous Adaptation: Incrementally updating knowledge without losing previously learned skills.

• Flexible Goal Management: Dynamically adjusting and prioritizing goals based on changing conditions.

• Novelty Detection: Explicitly recognizing and responding appropriately to novel inputs.

Group 3: Information Efficiency & Resource Optimization

This group emphasizes the intelligent system's efficient use of computational, energy, data, memory, and representational resources.

• Computational Efficiency: Achieving effective reasoning with minimal computational resources.

• Energy Efficiency: Minimizing physical energy consumption in task completion and reasoning.

• Data Efficiency: Learning effectively from minimal data examples.

• Memory Optimization: Efficiently storing and managing learned information to minimize redundancy.

• Representation Learning: Discovering meaningful internal abstractions autonomously from raw data.

• Risk-Aware Decision Making: Balancing exploration and exploitation under uncertainty, optimizing risk and reward.

Group 4: Autonomy & Self-Modification

Autonomy & self-modification covers a system’s capacity for independent management, enhancement, and optimization without external intervention.

• Autonomous Skill Generation: Independently identifying and acquiring new skills.

• Self-Reflection and Introspection: Evaluating and reasoning about internal processes and performance autonomously.

• Self-Update & Adaptation: Dynamically modifying internal structures to enhance learning efficiency autonomously.

• Goal-Directed Behavior: Autonomously setting and dynamically pursuing explicit objectives.

• Error Detection and Correction: Recognizing and rectifying errors or misconceptions autonomously.

• Creativity and Innovation: Generating novel, contextually appropriate solutions independently.

The Capacities in Detail

Group 1: Core Cognitive Capacities

Group Definition:
Core cognitive capacities represent foundational mental capabilities required to process, understand, reason about, and interact intelligently with various environments. They form the fundamental "building blocks" upon which intelligent behaviors and generalization rest, encompassing the autonomous ability to form abstractions, adapt efficiently, and reason using innate or minimal cognitive priors.

1. Skill Acquisition Efficiency

Definition:
Skill acquisition efficiency is the ability of a system to quickly and effectively acquire new skills. It is defined explicitly by Chollet as the central metric of intelligence: the more efficiently a system can acquire new skills, especially given limited experience and priors, the higher its intelligence.

Logic:
Intelligence fundamentally isn’t about possessing fixed or static skills (e.g., memorized chess openings). Instead, it’s about the speed and ease of acquiring new skills. The core argument Chollet emphasizes is that intelligence is a "skill-acquisition process," analogous to a factory that produces skills rather than the skills themselves.

Measurement:

• Efficiency of few-shot or zero-shot learning tasks.

• Performance on tasks like ARC, explicitly designed to measure rapid acquisition of novel concepts from minimal examples.

• Learning curves: how quickly performance improves over minimal experiences.

Current AGI Performance:
Currently moderate-to-low. Models like GPT-4 perform adequately on tasks they’ve indirectly encountered in training but struggle severely with genuinely novel tasks, requiring significant data or task-specific fine-tuning.

2. Generalization Ability

Definition:
Generalization is the capability of a system to extend its knowledge beyond its prior experiences to handle situations or problems it has never encountered.

Logic:
Generalization is crucial to intelligence as it captures the essence of dealing with novelty and uncertainty. Chollet strongly argues that true intelligence lies in the capacity to generalize from limited data, using abstraction to handle previously unseen scenarios.

Measurement:

• ARC benchmark, specifically designed to measure generalization to completely novel tasks.

• Tests involving significant distribution shifts or out-of-distribution generalization challenges.

Current AGI Performance:
Generally weak. Chollet repeatedly points out (notably in the ARC papers and talks) that current AGI fails dramatically at ARC-style generalization, indicating it is far from human-level general intelligence.

3. Abstraction Formation

Definition:
Abstraction formation is the autonomous ability to distill complex, high-dimensional data into simpler, reusable concepts or patterns independently, without explicit guidance from external agents.

Logic:
Chollet describes abstraction as the "engine" behind generalization. True abstraction means independently discovering representations of complex scenarios that facilitate efficient reasoning and skill transfer to new problems. It is fundamentally tied to intelligence because it transforms knowledge into flexible conceptual structures.

Measurement:

• ARC challenges that explicitly require identifying abstract rules from minimal examples.

• Bongard problems, Raven’s Progressive Matrices, or analogous tests measuring abstract pattern recognition and reasoning.

Current AGI Performance:
Poor. Current AGI, especially deep learning systems (including GPT-4), generally rely on learned statistical patterns rather than genuine independent abstraction formation. They struggle notably with ARC and other abstract reasoning benchmarks.

4. Adaptability

Definition:
Adaptability is the ability of a system to adjust its behavior, knowledge representations, or problem-solving strategies dynamically, without retraining or re-engineering by an external human developer.

Logic:
Chollet emphasizes adaptability as a hallmark of intelligent systems because real-world environments constantly evolve. Intelligence demands real-time adjustments to maintain functionality without losing performance due to changed circumstances.

Measurement:

• Performance under distribution shifts and dynamic, continually evolving tasks.

• Continuous learning tasks (online learning), evaluating if AGIs can incrementally adapt without forgetting or catastrophic degradation.

Current AGI Performance:
Limited. AGIs today largely require additional data, retraining, or explicit re-programming to effectively adapt to genuinely new or altered conditions, clearly outlined as a limitation in Chollet’s analysis of current AI failures.

5. Core Knowledge Utilization (Innate Priors)

Definition:
Core knowledge utilization refers to effectively leveraging minimal, innate cognitive priors (such as objectness, causality, numerosity, geometry, and agentness) to facilitate intelligent reasoning and learning.

Logic:
Inspired explicitly by Elizabeth Spelke’s work on human cognitive priors (core knowledge systems), Chollet emphasizes that intelligence should be measured relative to minimal, explicitly defined priors. Human intelligence relies on basic built-in knowledge structures to bootstrap learning, providing efficiency and stability for cognitive development.

Measurement:

• Tasks explicitly constructed around core knowledge priors (object permanence, basic causality tests, intuitive numerosity).

• ARC tasks explicitly testing understanding of elementary geometry, topology, and numerical concepts without external training data.

Current AGI Performance:
Moderate to superficial. Current AGIs can implicitly leverage certain priors (e.g., object detection), but lack explicit reasoning about core knowledge. Chollet highlights ARC as demonstrating severe weaknesses in explicitly understanding and manipulating these minimal cognitive priors.

6. Hierarchical Reasoning

Definition:
Hierarchical reasoning refers to the ability to reason about problems through multiple interconnected layers or levels of abstraction, systematically decomposing complex problems into simpler sub-problems.

Logic:
Chollet emphasizes hierarchical reasoning as essential for intelligent behavior because complex real-world problems inherently involve multiple abstraction layers. Effective intelligence integrates these layers smoothly to simplify and tackle complex scenarios efficiently.

Measurement:

• Multi-step reasoning benchmarks (e.g., complex ARC tasks or math problems requiring explicit decomposition into simpler sub-steps).

• Tasks explicitly designed to test hierarchical decomposition (e.g., complex Raven’s matrices, analogy-making tasks).

Current AGI Performance:
Limited-to-moderate. GPT-4 and related systems can perform step-by-step reasoning through carefully structured prompting (chain-of-thought), yet autonomous hierarchical decomposition and systematic multi-level reasoning remain significant weaknesses, as repeatedly noted in Chollet’s critiques and ARC analyses.

7. Meta-Learning (Learning to Learn)

Definition:
Meta-learning, or "learning to learn," is the ability of a system to improve its overall skill-acquisition efficiency based on experiences from previously encountered learning tasks, making future learning processes faster or more efficient.

Logic:
Chollet explicitly frames intelligence as the process of acquiring skills. Therefore, meta-learning—the improvement of that very process—represents a fundamental form of intelligence. By continually refining its learning process, a truly intelligent system not only learns new tasks quickly but also learns how to become even better at learning over time.

Measurement:

• Few-shot learning performance, specifically tasks designed to evaluate improvements across sequential tasks (e.g., continual learning benchmarks).

• Efficiency improvements across repeated exposures to structured learning tasks.

Current AGI Performance:
Limited. Chollet argues current deep-learning approaches exhibit minimal genuine meta-learning capability, instead typically relying on memorizing or retrieving learned patterns. Systems today largely do not autonomously improve learning efficiency in meaningful ways across novel, unseen scenarios.

8. Compositionality

Definition:
Compositionality refers to the capability of a system to construct complex ideas, representations, or solutions by combining simpler, previously learned components or concepts in a novel manner.

Logic:
Chollet highlights compositionality as crucial because intelligent solutions are often inherently compositional: complex reasoning problems require breaking down and recombining simpler, previously acquired knowledge. A truly intelligent system autonomously leverages compositionality, flexibly recombining concepts across diverse tasks and contexts.

Measurement:

• ARC-type tasks explicitly designed around compositional reasoning (e.g., complex input-output puzzles requiring multiple abstractions).

• Tests like Raven’s Progressive Matrices or analogy tasks where new solutions require recombining simpler components.

Current AGI Performance:
Weak. Chollet emphasizes repeatedly that current AI largely lacks deep compositionality. Instead, it tends to solve problems through memorization of complex statistical patterns, not genuine recombination of learned simpler concepts.

9. Causal Reasoning

Definition:
Causal reasoning is the ability to infer, reason about, and manipulate cause-and-effect relationships within and across diverse contexts, going beyond correlation or superficial statistical association.

Logic:
Chollet explicitly identifies causal reasoning as fundamental for intelligence because intelligent agents must navigate environments structured by cause-effect relationships. Efficient skill acquisition and transfer rely heavily on recognizing and leveraging causal relationships rather than mere patterns or correlations.

Measurement:

• Tasks explicitly designed to test causal inference (e.g., Pearl’s causality benchmarks or tasks inspired by developmental psychology experiments).

• Scenarios that explicitly require manipulating variables to achieve desired outcomes, demonstrating explicit causal understanding.

Current AGI Performance:
Limited. Chollet frequently notes current AGI’s superficial grasp of causality, often mistaking correlation or statistical patterns for true causal relationships. Explicit causal reasoning benchmarks typically reveal substantial weaknesses.

10. Commonsense Understanding

Definition:
Commonsense understanding refers to the intuitive, baseline grasp of how the physical and social world operates, encompassing everyday expectations about objects, agents, physics, interactions, and basic reasoning about the natural world.

Logic:
Chollet clearly emphasizes the importance of commonsense as a critical baseline for general intelligence, as intelligence in real-world scenarios relies on fundamental assumptions about object permanence, basic physics, agent behaviors, and social contexts. Without commonsense, systems cannot reliably handle even simple real-world situations.

Measurement:

• Commonsense reasoning benchmarks (e.g., Winograd Schema Challenge, ARC tasks focused on intuitive physics and everyday reasoning).

• Evaluations explicitly testing agentness, basic object interactions, and simple intuitive physical predictions.

Current AGI Performance:
Limited-to-moderate but superficial. Chollet consistently criticizes current AI as displaying only superficial or "simulated" commonsense, largely acquired through memorizing training patterns rather than genuine understanding. When explicitly tested (e.g., ARC intuitive physics tasks), performance remains significantly below human baselines.

Group 2: Generalization & Flexibility

Group Definition:
Generalization & Flexibility represents the capacity of an intelligent system to handle tasks and scenarios that significantly differ from its previous experiences, maintaining robust performance under novel conditions, uncertainties, and distributional shifts. Chollet strongly argues that true intelligence is defined by this generalized capability, rather than performance on narrowly specified or previously encountered tasks.

11. Open-Ended Generalization

Definition:
Open-ended generalization describes the capacity of a system to robustly handle entirely new, unforeseen tasks without explicit prior training or guidance.

Logic:
Chollet explicitly positions open-ended generalization as the central hallmark of intelligence. Intelligence isn't about memorizing skills but rather forming abstractions that let a system reason through entirely new scenarios autonomously. Chollet’s ARC benchmark explicitly tests for this form of generalization, highlighting its importance as the essence of intelligence.

Measurement:

• ARC-AGI benchmark tasks specifically designed to test generalization to completely novel situations.

• Tasks never encountered during training, assessing zero-shot or few-shot performance.

Current AGI Performance:
Poor. Current systems still significantly struggle on Chollet’s ARC tasks and similar open-ended benchmarks. Chollet’s recent ARC competition documents explicitly state state-of-the-art scores (around 55%) remain significantly below human levels (97–99%), highlighting deep limitations in open-ended generalization.

12. Robustness to Distribution Shifts

Definition:
Robustness to distribution shifts measures an intelligent system's resilience in maintaining stable, effective performance when encountering inputs or environments substantially different from its original training distribution.

Logic:
Chollet stresses repeatedly that intelligence must handle realistic environments, where changes in data distribution frequently occur. Robustness is thus a critical property distinguishing genuinely intelligent systems from brittle models trained only on narrowly defined distributions.

Measurement:

• Out-of-distribution (OOD) generalization tests explicitly designed to test performance degradation when input distribution significantly shifts.

• Tasks with adversarial perturbations or scenarios evolving significantly from training conditions.

Current AGI Performance:
Limited. Chollet explicitly critiques current deep-learning systems as brittle, failing rapidly under even minor distribution shifts. ARC and related benchmarks strongly reveal that current models struggle significantly with robustness.

13. Cross-Domain Transfer

Definition:
Cross-domain transfer describes the ability of a system to apply concepts, knowledge, or skills learned in one domain effectively to entirely different, unrelated domains.

Logic:
Intelligence fundamentally involves extracting abstract, generalizable concepts from specific experiences. Cross-domain transfer is a clear test of whether learning involves genuine abstraction or mere memorization of domain-specific patterns.

Measurement:

• Benchmarks explicitly designed for cross-domain generalization, applying concepts learned in one domain to fundamentally different tasks (e.g., visual reasoning tasks transferring to symbolic tasks).

• Few-shot tasks explicitly requiring application of knowledge from unrelated domains.

Current AGI Performance:
Weak. Chollet repeatedly emphasizes the minimal genuine cross-domain capability of current models. AGIs predominantly rely on superficial, domain-specific knowledge acquired during training, with limited genuine cross-domain applicability.

14. Zero-Shot and Few-Shot Learning

Definition:
Zero-shot and few-shot learning measure the system's ability to understand and perform well on tasks given very minimal or even no explicit examples.

Logic:
True intelligence, according to Chollet, means efficiently leveraging abstraction and minimal priors to solve new tasks quickly and effectively without extensive training examples. Zero-shot/few-shot performance is thus a critical indicator of true intelligence.

Measurement:

• ARC-type tasks with explicitly limited training examples, measuring rapid skill acquisition.

• Few-shot reasoning tasks such as analogies, Raven’s matrices, and tasks requiring inference from minimal examples.

Current AGI Performance:
Moderate but superficial. While some AGIs (like GPT-4) superficially handle few-shot tasks if closely resembling previously seen examples, genuinely novel ARC-style few-shot problems expose significant deficiencies.

15. Handling Uncertainty and Noise

Definition:
The capability of a system to maintain effective reasoning, decision-making, and performance when faced with incomplete, noisy, or ambiguous information.

Logic:
Chollet emphasizes uncertainty and ambiguity as inherent characteristics of real-world environments. Thus, true intelligence must be robustly able to reason under uncertainty rather than relying exclusively on clean, certain data.

Measurement:

• Tasks explicitly constructed to introduce ambiguity or incomplete information (e.g., noisy ARC tasks, ambiguous reasoning benchmarks).

• Probabilistic reasoning tasks explicitly designed around uncertainty handling.

Current AGI Performance:
Limited-to-moderate. Chollet consistently criticizes current models as superficially handling uncertainty, often relying on statistical shortcuts rather than genuine reasoning about ambiguity or incomplete data. Performance in genuinely uncertain scenarios remains weak.

16. Continuous Adaptation

Definition:
Continuous adaptation is the capability of a system to incrementally update and modify its knowledge or skills continuously and autonomously, without losing previously learned information or requiring full retraining.

Logic:
Chollet stresses adaptability as essential, since environments constantly evolve. An intelligent system must continuously adapt in real-time, efficiently integrating new knowledge without catastrophic forgetting.

Measurement:

• Continual learning benchmarks explicitly testing performance retention over multiple sequential tasks.

• Dynamic tasks explicitly designed to introduce incremental, evolving changes requiring real-time adaptation.

Current AGI Performance:
Poor-to-moderate. Current AGI systems typically suffer from catastrophic forgetting or limited incremental adaptability, requiring extensive retraining or explicit intervention from developers.

17. Flexible Goal Management

Definition:
Flexible goal management involves autonomously setting, adjusting, prioritizing, and pursuing multiple goals based on changing conditions or contexts.

Logic:
Chollet explicitly argues intelligence requires dynamically shifting and managing goals in complex environments, continually adapting priorities based on evolving circumstances.

Measurement:

• Tasks explicitly designed to test goal adjustments and reprioritization in changing environments.

• Reinforcement learning benchmarks explicitly testing multi-objective optimization and dynamic goal changes.

Current AGI Performance:
Weak. Chollet notes explicitly that current AI systems usually follow static or pre-specified goals, rarely autonomously shifting or reprioritizing goals effectively without significant developer guidance.

18. Novelty Detection

Definition:
Novelty detection is the capacity to autonomously recognize and explicitly identify novel, unfamiliar patterns or scenarios not previously encountered.