Meta Chief says AI is not as smart as a dog – see why

AI has been a topic of discussion for many years, and its development has been rapid. However, according to Yann LeCun, the chief AI scientist at Facebook’s parent company, Meta, AI is not yet as smart as a dog. He made the statement at the Viva Tech event in Paris yesterday. He claims that the current level of AI intelligence is not as good as that of dogs and thus should not be considered a threat to humanity. This statement may come as a surprise to many, given the advancements in AI technology. In this article, we will look at why LeCun thinks AI is not yet as smart as a dog and what needs to be done to bridge the gap.

What is AI?

AI refers to the ability of machines to perform tasks that would typically require human intelligence. These tasks include learning, reasoning, and problem-solving. AI systems can be classified into two categories: narrow or weak AI and general or strong AI. Narrow AI is designed to perform specific tasks, while general AI is designed to perform any intellectual task that a human can perform.

Why is AI intelligence not as high as that of a dog?

Despite the advancements in AI intelligence, AI is not yet as smart as a dog, why did LeCun make this statement? According to LeCun, AI systems need to be created as “controllable and trainable systems”. This means that AI systems need to be designed to learn from their environment and adapt to new situations. Dogs, on the other hand, are born with the ability to learn from their environment and adapt to new situations. They can also understand human emotions and respond to them accordingly.

Another reason why AI is not as smart as a dog is that AI systems lack common sense. Common sense is the ability to understand the world around us and make decisions based on that understanding. AI systems, on the other hand, lack this ability. They can only make decisions based on the data they have been trained on. This means that AI systems can make mistakes when faced with new situations that they have not been trained on.

What needs to be done to bridge the gap?

To bridge the gap, AI systems need to be designed to learn from their environment and adapt to new situations. This means that AI systems need to be created as “controllable and trainable systems”. AI systems also need to be designed to understand human emotions and respond to them accordingly. This will require AI systems to be trained on data that includes emotional cues.

Another way to bridge the gap is to develop AI systems that have common sense. This will require AI systems to be trained on data that includes information about the world around us. AI systems will also need to be designed to understand the context of a situation and make decisions based on that understanding.

Conclusion

AI is not yet as smart as a dog despite the advancements in AI technology. These systems lack the ability to learn from their environment and adapt to new situations. They also lack common sense, which is the ability to understand the world around us and make decisions based on that understanding. To bridge the gap, AI systems need to be designed to learn from their environment and adapt to new situations. They also need to be designed to understand human emotions and respond to them accordingly. Finally, AI systems need to be trained on data that includes information about the world around us and the context of a situation.