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Apple Finds That AI Large Language Models Can’t Reason

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Artificial intelligence (AI) has captivated public imagination with its seemingly limitless potential, often exemplified by tools like Apple AI and other large language models (LLMs). These systems, powered by billions of data points, can summarize complex texts, generate creative content, and even solve basic mathematical problems. However, this awe is frequently accompanied by growing concerns: Will AI surpass human intelligence? Could it take over critical decision-making in unpredictable ways?

Recent research sheds light on these concerns, revealing key limitations in the reasoning capabilities of large language models. Conducted by Apple researchers, the study examines whether models like GPT-4 and others genuinely understand problems or merely mimic patterns from their training data. The findings not only answer some pressing AI concerns but also provide a roadmap for how businesses can responsibly integrate tools like Apple AI into their operations without overestimating their capabilities.

Shot of a man working on a laptop with large language models

What the Study Tells Us About AI Reasoning

The research conducted by Apple focused on GSM8K, a benchmark widely used to evaluate how well AI models handle grade-school mathematics problems. While these benchmarks previously indicated significant progress, a deeper investigation revealed a sobering reality: large language models often falter when faced with even minor alterations in the questions. To address this, the researchers developed GSM-Symbolic, a new evaluation framework that generates diverse question sets using symbolic templates.

What makes GSM-Symbolic unique is its ability to expose inconsistencies in AI reasoning. For instance, when only the numerical values in a question were altered, performance dropped significantly across all tested models. Even more striking, adding irrelevant clauses, details that should not impact the logic of the solution, caused some models to fail by up to 65%. This suggests that current large language models rely heavily on pattern recognition rather than genuine logical processing.

Consider this scenario: If a model is asked, “How many apples are in the basket if there are 5 oranges and 7 apples?” it might perform perfectly. But if you slightly reframe the question or add extraneous information, such as mentioning a type of basket irrelevant to the answer, the model often struggles. Such fragility underscores that while AI systems like Apple AI are excellent at certain tasks, their limitations in reasoning make them unsuitable for high-stakes decision-making.

Why AI Doesn’t “Think” Like Humans

The heart of the issue lies in how AI operates. Unlike humans, who approach problems with understanding and logic, systems like Apple AI rely on probabilistic pattern-matching. They predict the next most likely piece of information based on massive amounts of training data. While this makes them powerful tools for tasks like translation or summarization, it also makes them prone to errors when context shifts.

For example, imagine an AI system tasked with helping a business analyze customer data. It could efficiently generate reports and even provide insights into trends. But if asked to weigh conflicting variables, such as balancing customer satisfaction against budget constraints, it would struggle. This limitation arises because the system lacks a fundamental grasp of causality and cannot reason through competing priorities the way a human analyst would.

This study also highlights how large language models are sensitive to seemingly trivial changes. Altering the order of input data or introducing irrelevant information can derail their outputs. In one experiment, researchers added a sentence about a fictional character to a math problem. Even though the detail had no bearing on the solution, the AI’s performance plummeted, revealing its reliance on surface-level patterns rather than deep comprehension.

How to Use AI Wisely in Business

For businesses, these findings are both a warning and an opportunity. Tools like Apple AI have immense potential, but their limitations must guide their application. In practice, this means using large language models for well-defined tasks where their pattern recognition capabilities shine while steering clear of roles requiring complex reasoning or ethical decision-making.

One example is customer support. Companies can deploy AI chatbots to handle routine concerns and inquiries, such as resetting passwords or tracking shipments. These tasks are predictable, making them ideal for large language models. However, when a customer raises a nuanced issue, say, disputing a bill that involves ambiguous terms, the conversation should transition to a human representative. The AI can support the process by summarizing the interaction, but humans must handle the critical reasoning.

Similarly, in creative industries, AI can serve as a collaborative tool. Marketing teams can use Apple AI to draft ideas for campaigns or generate variations of ad copy. But the final approval and strategic alignment should rest with human professionals who understand the brand’s core values and long-term goals.

Businesswoman speaking mobile phone closeup. Office manager working on AI concern

Why AI Won’t Take Over

One of the most comforting takeaways from this research is the reassurance it offers about AI’s current limitations. Despite their power, tools like Apple AI are far from the self-thinking, self-evolving entities often depicted in dystopian narratives. They operate within strict boundaries defined by their training data and are prone to errors when faced with scenarios they haven’t explicitly encountered before.

This limitation should put some common fears to rest. The idea of AI systems independently making strategic decisions or acting without concern of human oversight is not supported by their current design or capabilities. Instead, these systems remain highly specialized tools, capable of augmenting human effort but not replacing it.

For instance, while AI excels at identifying patterns in financial markets or predicting trends, it cannot evaluate the ethical implications of investment decisions. A human still needs to determine whether prioritizing profits aligns with broader organizational values or societal impact.

Building Better AI Models

The limitations of current AI models also point the way forward for researchers and developers. Frameworks like GSM-Symbolic offer a blueprint for building more robust evaluation systems that go beyond simple benchmarks. By testing models under diverse conditions, developers can identify specific weaknesses and address them before deployment.

These insights emphasize the importance of interdisciplinary collaboration. Psychologists, ethicists, and domain experts must work alongside AI researchers to create systems that are not only powerful but also aligned with human values. For businesses, this means investing in training programs that help employees use AI responsibly and integrating AI systems with broader decision-making frameworks.

Apple’s involvement in this research signals a commitment to transparency and improvement. As businesses increasingly adopt AI tools, such transparency will be crucial in addressing AI concerns, fostering trust, and ensuring that AI serves as an ally, not a threat.

A Balanced Approach to AI

AI systems like Apple AI have redefined what machines can do, offering capabilities that seemed like science fiction only a decade ago. However, as this research shows, they remain tools, powerful yet limited. Their ability to mimic reasoning does not equate to true understanding, and their fragility in complex scenarios should temper both our expectations and fears.

By recognizing these limitations, businesses and individuals can make informed decisions about how to use AI effectively. Whether it’s augmenting workflows, sparking creativity, or analyzing data at scale, AI’s role is to support human ingenuity, not replace it. As we continue to refine these technologies, the key will be balancing ambition with caution, ensuring that AI evolves in ways that genuinely enhance human capabilities.

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