AI: Intelligence or Just Impressive Memory?

Leading artificial intelligence companies promote their models as the most advanced in the world, boasting top performance in benchmarks—standardized tests used to measure and compare AI capabilities in tasks like language comprehension, text generation, problem-solving, and image analysis.

However, the scientific community has questioned these results, suggesting that AI models might be optimized to “ace the test” rather than truly understanding the concepts.

AI Accuracy Drops by Over 50%

Recently, researchers at UNED designed an experiment to assess AI’s actual comprehension. The key twist? They modified traditional tests by replacing the correct answer with “None of the above.”

This forced AI models to reason rather than simply retrieve stored responses. The results were eye-opening:

• Performance dropped between 50% and 57% on average.
• Models trained in English struggled with Spanish, revealing language biases.
• The findings confirmed that today’s AI systems are not truly intelligent in the human sense—they don’t understand the world, they don’t reason, and they certainly aren’t conscious.

Advanced Pattern Recognition, Not True Intelligence

Most AI systems function as highly advanced memory and pattern recognition tools. When AI answers a question or generates text, it’s predicting the most likely response based on the data it has been trained on.

Why AI Training Matters

Training is at the heart of AI’s capabilities. AI models must be fed high-quality, relevant datasets to learn trends and patterns effectively.

Once trained, they are tested with new data to verify that they have actually learned and are not just memorizing.

Not all AI models are created equal. Choosing the right algorithm is crucial for accurate predictions.

In industrial environments, AI applications range from statistical models to deep neural networks, depending on the complexity of the problem.

AI: Explainability & Continuous Monitoring

Initial training is just the beginning. AI requires ongoing maintenance, as data and conditions constantly evolve. Periodic retraining with updated information is necessary to prevent outdated or inaccurate predictions.

Equally important is understanding how and why AI reaches certain conclusions. Explainability and real-time monitoring help detect deviations or errors before they cause significant operational impact.

In industries like pharmaceuticals and food production, AI-driven insights must be highly precise—because even the smallest error can have serious consequences for health and safety.