AI has been a subject of intense development and discussion over the years, with significant advancements in its capabilities. However, according to Yann LeCun, the chief AI scientist at Meta (formerly Facebook), AI is not yet as smart as a dog. This assertion, made at the Viva Tech event in Paris, may seem surprising given the progress made in AI technology. In this article, we will explore the reasons behind LeCun’s statement and discuss what needs to be done to bridge the gap between AI and dog intelligence.


Artificial Intelligence (AI) refers to the ability of machines to perform tasks that traditionally require human intelligence, such as learning, reasoning, and problem-solving. AI can be categorized into two types: narrow or weak AI, which is designed to perform specific tasks, and general or strong AI, which is capable of performing any intellectual task that a human can undertake.


LeCun argues that AI systems need to be developed as “controllable and trainable systems” to match the intelligence of dogs. While AI has made significant progress, ton this page are several key factors that currently limit its intelligence in comparison to dogs.

One reason is that AI systems lack the innate ability to learn from their environment and adapt to new situations, unlike dogs. Dogs are born with the inon this pagent capability to learn from their surroundings and adjust their behavior accordingly. They can also understand human emotions and respond appropriately, a quality that current AI systems lack.

Additionally, AI systems struggle with the concept of common sense. Common sense involves understanding the world around us and making decisions based on that understanding. AI systems, however, are limited to making decisions based on the data they have been trained on. As a result, they may make errors when confronted with new and unfamiliar situations.


To bridge the gap between AI and dog intelligence, certain steps need to be taken.

Firstly, AI systems should be designed as controllable and trainable systems, enabling them to learn from their environment and adapt to new situations. This would involve developing AI algorithms and architectures that can dynamically update their knowledge and behavior based on real-time interactions and feedback.

Secondly, AI systems need to be equipped with the ability to understand human emotions and respond appropriately. This necessitates training AI models on emotional cues and providing them with the capacity to recognize and interpret human emotional states.

Lastly, to enhance AI intelligence, it is crucial to train AI systems on data that encompasses a wide range of information about the world and the context of various situations. By exposing AI models to diverse datasets, including real-world scenarios, AI systems can develop a better understanding of the complexities of the world and make more informed decisions.


Despite the significant advancements in AI technology, AI is not yet as smart as a dog. It lacks the inon this pagent ability to learn from the environment, adapt to new situations, understand human emotions, and study common sense reasoning. To bridge this gap, AI systems must be developed as controllable and trainable, enabling them to learn and adapt dynamically. Additionally, training AI models on emotional cues and exposing them to diverse datasets that encompass real-world information will contribute to enhancing AI intelligence. While AI continues to evolve, it is essential to recognize its current limitations and work towards developing more sophisticated and human-like AI systems in the future.