We’ve all seen it: AI writing that’s so good it’s uncanny. It can summarize complex documents, draft emails with perfect grammar, and even generate creative stories. In many ways, large language models like GPT-4 seem to possess an almost human-like grasp of language and knowledge. But beneath the surface, are they truly reasoning in the way we do?
For decades, the Turing Test has been our go-to benchmark, challenging machines to mimic human conversation so well that we can’t tell the difference. Yet, as AI becomes increasingly sophisticated, perhaps it’s time for a new kind of test – one that probes not just the *output* of intelligence, but the *process* of reasoning itself. What if there was a simple, almost childlike puzzle that could reveal a fundamental gap between genuine human intelligence and even the most advanced AI?
The Hidden Challenge of “Global Reasoning”
Modern AI, particularly large language models (LLMs), excels at tasks that involve pattern recognition, information retrieval, and even local inference. They can quickly identify connections between adjacent concepts or complete sequences based on vast amounts of training data. But what happens when the answer isn’t immediately obvious, when it requires stitching together disparate pieces of information that aren’t directly linked?
This is where the concept of “global reasoning” comes into play. Imagine trying to solve a complex Sudoku puzzle or planning a multi-stop road trip. You don’t just look at one square or one leg of the journey in isolation. Instead, you need to hold multiple possibilities in your mind, connect seemingly unrelated facts, and build a comprehensive mental model of the entire problem space. This ability to integrate information across a broad “locality” is a hallmark of human problem-solving.
For AI, especially the Transformer-based architectures prevalent today, this global reasoning can be surprisingly difficult. Their very design, while incredibly powerful for processing sequences, sometimes struggles with tasks where the critical pieces of information are far apart or require many transitive steps to connect. It’s like trying to understand an entire book by only reading one sentence at a time, without any memory of the previous pages.
The “Height Comparison” Test: A Simple Yet Profound Hurdle
Researchers at Apple and EPFL recently put this theory to the test with a deceptively simple challenge: the “Height Comparison” task. Picture this: you’re given a series of pairwise relationships between people’s heights in a random order. For instance, “Omar is taller than Sara,” “Vlad is taller than David,” “Farah is taller than Omar,” “Sara is taller than Vlad.” Then, you’re asked a question like, “Is Omar taller than Vlad?”
To us, the answer is immediately clear once we piece together the chain: Farah > Omar > Sara > Vlad > David. So, yes, Omar is indeed taller than Vlad. It’s a basic exercise in transitive reasoning, something most elementary school children grasp without much effort.
But for an AI, especially one without explicit instructions to chain its thoughts, this task becomes a surprisingly complex gauntlet. To answer correctly, the model can’t just look for a direct “Omar > Vlad” statement. It needs to construct a complete ordered list of heights by combining multiple, often non-sequential, relations. The further apart the two people in question are in the overall height order (what the researchers call ‘n’), the more relations the AI needs to combine – and the harder the task becomes.
When AI Stumbles: GPT-3.5 vs. GPT-4
The results were telling. ChatGPT (GPT-3.5), even for the simplest case where only a few steps of reasoning were required (n=1, as in our Omar/Vlad example), largely failed. This highlights a critical limitation: despite its vast knowledge and language fluency, GPT-3.5 struggled to perform multi-step, global reasoning on unfamiliar, randomly ordered data.
Interestingly, GPT-4 showed a marked improvement. It performed much better, often getting the correct answer. This suggests progress in AI’s ability to handle more complex reasoning chains. But here’s the kicker: the researchers observed that when GPT-4 answered correctly, it frequently “ordered people based on their height,” effectively creating an internal (or sometimes externalized) scratchpad of the relationships. It was mimicking a human problem-solving strategy.
To further probe this, the researchers tried prompting GPT-4 with a simple instruction: “Answer only with a yes or no.” Without the ability to use “chain-of-thought” reasoning, GPT-4’s performance plummeted, failing for cases beyond n=1. This single experiment speaks volumes about the nature of AI reasoning.
The Power of the “Scratchpad” – Mimicking Human Thought
What this “Height Comparison” test really reveals is the critical role of an “educated scratchpad” in enabling AI to perform global reasoning. When we humans tackle a complex problem, we often don’t jump straight to the answer. We might jot down notes, draw diagrams, or mentally break down the problem into smaller, manageable steps. We create a “scratchpad” – a temporary workspace – where we can manipulate information and build a comprehensive solution.
Similarly, for AI, enabling a “chain-of-thought” or an explicit scratchpad allows the model to externalize its intermediate reasoning steps. Instead of just trying to output a direct answer, it can compute sub-problems, organize information, and gradually build towards a solution. This isn’t just about outputting more text; it’s about giving the model the space and structure to perform multi-step computations and overcome the “locality barrier” inherent in its architecture.
Without this ability to either internally or externally simulate a scratchpad, AI models often struggle with tasks that require inductive reasoning or piecing together a global picture from fragmented local facts. It underscores a fundamental difference: human intelligence fluidly integrates and reasons across vast cognitive spaces, while current AI often needs explicit scaffolding to achieve similar results in complex, multi-step tasks.
What This Means for Real Intelligence and the Future of AI
This isn’t to say AI isn’t intelligent. Its capabilities are astounding and continue to grow at a rapid pace. But this basic reasoning test highlights a crucial distinction. “Real intelligence,” as we understand it in humans, involves an innate and flexible capacity for global, transitive, and inductive reasoning – the ability to effortlessly build complex mental models from disparate facts, even when those facts aren’t directly linked.
Current AI, while powerful, often achieves similar outcomes by simulating these human reasoning strategies (like chain-of-thought) or by brute-forcing solutions through immense computational power and data. The “Height Comparison” task serves as a potent reminder that while AI can mimic reasoning, the underlying mechanism and inherent flexibility can still differ significantly from human cognition.
As AI continues to evolve, understanding these fundamental differences will be key. Research into “educated” and “inductive scratchpads” represents a fascinating frontier, pushing AI closer to emulating the seamless, global reasoning that we often take for granted. It’s not about finding a definitive winner in the intelligence race, but rather about understanding the unique strengths and persistent challenges in building truly intelligent systems that can reason with the same depth and flexibility as a human mind.
