Can Computational Models of Cognition Make AI Smarter?

Using computational models of cognition in artificial intelligence (AI) brings up some big challenges. These models are based on how humans think and act, but it’s not easy to apply them to AI.

1. Human Thinking is Complicated:

• Human thinking involves lots of things like logic, feelings, past experiences, and where we come from.
• Many computational models simplify this complexity, which can lead to AI that doesn’t really act like people do.
• For example, models like connectionism or symbolic AI can miss the small, tricky decisions we make every day.

2. Problems with Data:

• To train AI, we usually need a lot of information. However, humans often make decisions based on little bits of information and context.
• These computational models can depend on huge sets of data that don’t always capture the details of human thinking. This can make them not work well in real-life situations.
• For instance, natural language processing systems can have a hard time understanding idioms or sarcasm because they don’t get the context that people naturally understand.

3. Overfitting and General Issues:

• Sometimes, these models can become too focused on the training data, which means they might work great in controlled settings but struggle in the unpredictable real world.
• Finding a way to balance how human thinking works and how flexible AI systems need to be is a big challenge.

4. No Common Agreement:

• There isn’t a clear agreement on the best way to model how we think, which makes it hard to create effective computational models that can work in many situations.
• Different models can predict different things about thinking, which leads to confusion in AI applications.

Possible Solutions:

1. Teamwork Across Fields:

• Working together more closely between cognitive psychologists, computer scientists, and neuroscientists can help us understand how we think better.
• By sharing ideas, these experts can create more powerful computational models that reflect the complexity of human thought.

2. Using Varied Data Sources:

• Using different types of data that recognize different cultural backgrounds, experiences, and emotions can help make AI systems more accurate.
• Instead of collecting lots of data, focusing on fewer, but more meaningful examples can help improve how these systems generalize.

3. Learning Over Time:

• Creating AI that can learn and adjust from new experiences, like humans do, would make them more reliable in changing environments.
• Adding ways for the AI to get feedback and improve continuously could solve some of the problems of fixed models.

In summary, while using computational models of cognition could help make AI better, we need to tackle these tough challenges to truly benefit from what they can offer.