Artificial intelligence has captivated our imagination by producing responses that seem to exhibit genuine reasoning. Yet, as many experts have noted, what we often see is not true thought but an elaborate simulation of it. In this blog, we dive deep into a humorous yet enlightening example—the “Kiwi Problem”—to explore how AI reasoning works, the challenges it faces, and what the future might hold. At VE3, we’re passionate about cutting through the complexity of these systems to help organizations harness AI in transformative ways.
The Kiwi Problem: A Humorous Insight into AI Reasoning
Imagine this scenario:
Oliver picks 44 kiwis on Friday, 58 on Saturday, and on Sunday, he picks double the number he picked on Friday—but five of these are smaller.
At first glance, you might be tempted to sum up the numbers (44 + 58 + 88) and conclude the total is 190. However, one AI chatbot, when faced with this problem, subtracted five from the total, arriving at 185. Why? The chatbot’s reasoning was skewed by the seemingly extraneous detail about the “smaller” kiwis.
This playful anecdote highlights a key aspect of many large language models (LLMs): rather than engaging in step-by-step logical reasoning like humans, they often rely on probabilistic pattern matching. The AI had seen similar phrasing in its training data, where additional qualifiers sometimes affected the outcome. Thus, it defaulted to a pattern that didn’t quite fit the arithmetic task at hand.
Under the Hood: How AI Reasoning Really Works
Probabilistic Pattern Matching
At the core of many LLMs is a mechanism that predicts the next word—or token—based on probabilities derived from vast amounts of training data. When a detail such as “five of those are smaller” appears, the model searches its memory of examples and applies what it has learned, even if that detail is irrelevant to the arithmetic operation. This behaviour is a hallmark of probabilistic pattern matching:
1. Strengths
In many cases, this approach enables AI to produce coherent, contextually appropriate responses.
2. Weaknesses
However, it can lead to errors when the context calls for pure logic rather than pattern recognition.
Token Bias and Sensitivity
Small changes in input—the so-called token bias—can have outsized effects on the output. A single token, or even a subtle detail, can shift the AI’s interpretation, demonstrating both the power and fragility of these systems.
AI as Advanced Autocompletion: Simulated Thought vs. Genuine Reasoning
The Autocompletion Analogy
Consider your smartphone’s autocomplete feature. It predicts the next word based on what you’ve typed so far. LLMs do something similar—but on a much grander scale. They can generate entire paragraphs, paragraphs that sometimes appear to be the result of thoughtful deliberation. In reality, these models are executing a supercharged version of autocomplete:
1. Simulated Reasoning
The AI produces outputs that mimic a logical flow, yet it is, at its heart, following statistical cues from its training data.
2. Real Reasoning
True human reasoning involves conscious awareness, goal-driven problem-solving, and the ability to discern which details are meaningful. AI, as of now, does not possess this level of subjective understanding.
The Philosophical Debate
This leads us to a deeper question: Can AI really think? While LLMs can simulate the appearance of thought, they lack the consciousness and self-awareness that characterize human reasoning. As one popular chatbot summarized, genuine thinking involves “conscious, goal-driven, subjective understanding and adaptability,” qualities that remain beyond the reach of today’s AI systems.
Advancements in AI Reasoning: Training Time and Inference Time Compute
Training Time Compute
Traditional AI models are trained on massive datasets where they learn to associate input patterns with outputs. During this phase, the models build a vast repository of examples, learning to predict tokens based on the probability distributions of past occurrences. However, this learning is inherently statistical rather than truly logical.
Inference Time Compute and Chain-of-Thought Prompting
Recent innovations have introduced the concept of inference time computing, where AI models are designed to “think” a little longer before responding. Techniques such as chain-of-thought prompting encourage the model to articulate intermediate reasoning steps. This extra processing time can help the model arrive at more logically sound answers, as it simulates a process akin to human deliberation:
Step-by-Step Reasoning
The model breaks down complex tasks into smaller, more manageable parts.
Reduced Token Bias
By taking the time to process the problem, the AI becomes less prone to being misled by irrelevant details.
The Future of AI Reasoning and Its Real-World Impact
As we continue to push the boundaries of artificial intelligence, the nuanced challenges of AI reasoning remind us that these systems are still in their developmental stages. The Kiwi problem may be a light-hearted illustration, but it encapsulates real issues, such as sensitivity to extraneous details and the inherent limitations of probabilistic pattern matching.
At VE3, we believe that understanding these challenges is crucial for building and deploying AI solutions that deliver real value. Our approach is informed by the latest research and innovation in AI, ensuring that our solutions are not only state-of-the-art but also tailored to the unique needs of each organization.
How VE3 Empowers Organizations with AI Solutions
The exploration of AI reasoning—from the quirky missteps in the Kiwi problem to the sophisticated techniques of chain-of-thought prompting—reveals both the promise and the pitfalls of current AI technology. While AI may not truly “think” in the human sense, ongoing advancements are making these systems more reliable and context-aware.
At VE3, we leverage our deep expertise in AI to help organizations navigate these complexities. We work closely with our clients to design, develop, and deploy AI solutions.
Whether you’re looking to implement smart chatbots, automate business processes, or harness predictive analytics, VE3 is your trusted partner in transforming challenges into opportunities with AI. Get in touch with us today to discover how we can help your organization unlock the full potential of artificial intelligence. Visit us today! for more information.