Curiosity artificial intelligence (AI) mimics the natural curiosity of humans that allows us to learn things on our own. The goal is to develop curiosity through machine learning (ML) algorithms so AI systems can seek solutions to new problems independently.
For instance, robots can be programmed to explore their environment and learn without being told precisely what to do. This AI approach can help machines learn faster. While most ML approaches are too dependent on human intervention and instruction, curiosity AI trains systems to investigate unfamiliar data or events.
Curiosity artificial intelligence is also known as “curiosity AI,” “curious algorithm,” “algorithm curiosity,” and “artificial curiosity.”
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Even as children, humans don’t back down when faced with an unfamiliar toy or appliance. Instead, our curiosity drives us to explore and know more about it. Curiosity AI aims to imitate this behavior in ML algorithms.
How Does Curiosity Artificial Intelligence Work?
Curiosity artificial intelligence uses reinforcement learning (RL), a method of rewarding AI systems for behaviors that bring them closer to achieving their goal. This technique is helpful in gaming, robotics, and advertising. A classic example of RL is providing product recommendations to Internet users. The system analyzes user interests and presents relevant recommendations. It is then rewarded with a user conversion or review.
In mobile games, RL helped develop superhuman players, as in the case of AlphaGo Zero, the first computer to defeat a human world champion.
However, RL in curiosity artificial intelligence is tweaked so AI systems get more rewards for behaviors that lead them to new discoveries. In essence, curiosity artificial intelligence can get two types of rewards—goal-related and novel observations.
- Extrinsic or goal-related rewards: A computer program gets rewards as it gets closer to reaching its goal. A bot playing chess, for example, gets a classic RL reward when moving pieces throughout the game.
- Intrinsic or novel-related rewards: A computer program gets more rewards for discovering data that does not exist in its memory. The chess bot player gets more points for recognizing an opponent’s new move.
A curiosity AI system can get rewards simultaneously by observing new and goal-oriented data.
How Can Curiosity AI Help Solve Problems?
Most AI approaches rely heavily on the program developed by humans. They can only do what they were programmed to do. They can only learn what they are taught. When faced with new environments, these systems get stuck.
For example, computers don’t innately know how to handle malware because it’s not in their programming. Intelligent cars won’t recognize a tree unless they are taught. The warehouse robots in the following video can efficiently pack thousands of groceries by following grids. But what would happen if their environment was changed?
With the introduction of curiosity, AI systems can explore new environments. As a result, the production robots packing groceries won’t crash into one another when the grids are rearranged or removed. With artificial curiosity, they will be intelligent enough to assess the situation and make appropriate decisions.
What Are Some Applications of Curiosity Artificial Intelligence?
Among the first applications of curiosity artificial intelligence is in gaming. Developers explored it to create superhuman players, as previously mentioned.
Curiosity artificial intelligence is also currently employed in business automation processes, such as human resources, data analytics, customer service, and project management.
Take chatbots, for example—it’s common to see chatbots that can answer frequently asked questions (FAQs). On the other hand, customer service quality can significantly improve if chatbots have a certain level of perceived emotional intelligence that can be achieved by injecting curiosity-driven behaviors.
For one, they can be configured to ask relevant follow-up questions when faced with an unknown user response. Instead of teaching them to respond, “Sorry, I’m afraid I can’t help you with that,” curiosity AI enables chatbots to explore other responses, such as, “I see you mentioned you have a problem with our service. Do you want me to walk you through the troubleshooting process?”
Curiosity artificial intelligence is also valuable for automating industrial processes, such as supply chain management (SCM), product quality control, and predictive maintenance.
Key Takeaways
- Curiosity artificial intelligence is also called “artificial curiosity,” “curiosity AI,” “curious algorithm,” and “algorithm curiosity.”
- Curiosity AI is an AI approach that aims to imitate human curiosity in ML algorithms.
- Curiosity artificial intelligence is currently applied in customer service, data analysis, and other business processes.
- The approach is also helpful in improving industrial automation processes.
- With curiosity artificial intelligence, computer programs don’t need human input all the time. They are taught to explore new environments and get rewarded for new discoveries.
- Curiosity artificial intelligence uses a reward system from an advanced RL where they are rewarded for goal-related actions and receive more rewards for new observations.
- Curiosity artificial intelligence can make AI systems more intelligent.
