AI versus human abilities – The compositional Generalization

Artificial intelligence (AI) has made considerable progress in recent years, achieving state-of-the-art results on a variety of tasks, including computer vision, natural language processing, and machine translation. However, one of the challenges that remains is the ability of AI systems to generalize to new and unseen data (the compositional generalization skill).

Compositional generalization is the ability to understand and apply knowledge to new situations that are a composition of situations seen before. It’s a fundamental aspect of human cognition, allowing us to navigate our complex world.

Some examples of our compositional generalization skills include:

• Commuting: If you know how to ride a bike and how to get to the park, you can ride your bike to the park, even if you’ve never done that exact activity before.
• Cooking: If you know how to boil water and how to make tea, you can combine these skills to make a cup of hot tea, even if you’ve never done it before.

The challenge lies in the fact that most AI systems today are trained on substantial amounts of data and learn to make predictions or take actions based on patterns they identify in this data. These systems, particularly those based on deep learning, often struggle when presented with novel situations that don’t directly match the patterns, they’ve seen in their training data.

The Way Forward

There is ongoing research aimed at improving the compositional generalization capabilities of AI systems. One approach is to incorporate more structured forms of knowledge representation into AI models, such as graphs or symbolic systems, which can more naturally handle compositional concepts.

Another approach to compositional generalization is to use transfer learning. Transfer learning is the process of using knowledge learned from one task to improve performance on another task. In the context of compositional generalization, transfer learning can be used to transfer knowledge from a set of base tasks to a new target task.

Examples of Compositional Generalization in AI

There are a number of examples of compositional generalization in AI. One example is the use of deep learning for image classification. Deep learning models are hierarchical models that learn to represent images at different levels of abstraction. The lower layers of the model learn to represent simple features, such as edges and corners. The higher layers of the model learn to represent more complex features, such as objects and faces.

Another example of compositional generalization in AI is the use of natural language processing for machine translation. Natural language processing models are hierarchical models that learn to represent sentences at different levels of abstraction. The lower layers of the model learn to represent words and phrases. The higher layers of the model learn to represent the meaning of the sentence.

Conclusion

While AI has made impressive progress in many areas, compositional generalization remains a significant challenge. Overcoming this hurdle will be crucial for the development of truly intelligent AI systems. As research in this area advances, we can look forward to AI systems that are more capable of understanding and interacting with the world in a human-like way.

You can find scholarly articles on AI and compositional generalization through various academic databases and search engines. Here are a few articles that might interest you:

1.?????? “Concepts, Properties and an Approach for Compositional Generalization” by Yuanpeng Li1 . This report connects a series of works for compositional generalization and summarizes an approach1 .

2.?????? "Can AI grasp related concepts after learning only one?"2 . This article discusses a technique called Meta-learning for Compositionality (MLC), which outperforms existing approaches and is on par with, and in some cases better than, human performance2 .

3.?????? “Compositional generalization through abstract representations in human and artificial neural networks” by Takuya Ito, Tim Klinger, Douglas H. Schultz, John D. Murray, Michael W. Cole, Mattia Rigotti 3 .

4.?????? “Improving Compositional Generalization in Classification Tasks via …” by Juyong Kim, Pradeep Ravikumar, Joshua Ainslie, Santiago Onta?ón4 . This paper discusses the ability to generalize systematically to a new data distribution by combining known components4 .

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References:

·?????? IGN Brasil. (2023, November 4). Inteligência artificial ultrapassa pela primeira vez uma habilidade humana crucial. Retrieved from https://br.ign.com/tech/115581/news/inteligencia-artificial-ultrapassa-pela-primeira-vez-uma-habilidade-humana-crucial

·?????? Li, Y. (2021). Concepts, Properties and an Approach for Compositional Generalization. arXiv preprint arXiv:2102.04225.

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