Yoshua Bengio: Attention and Consciousness (NeurIPS 2019) | Summary and Q&A

TL;DR

Attention is a powerful tool in machine learning, allowing for focused computation and breakthroughs in various applications such as machine translation and memory-based neural networks.

Key Insights

• 🎰 Attention enables focused computation and has been a breakthrough in machine translation.
• 🥺 Content-based soft attention allows for learning where to attend, leading to improved performance.
• 🧠 Attention is relevant in cognitive neuroscience, where it is considered an internal action similar to how the brain controls movement.
• 😫 Attention can change machine learning systems from processing vectors to processing sets, expanding their capabilities.
• 👻 Attention creates a dynamic connection between layers, allowing for more flexibility and information retrieval.
• 🤩 Attention introduces the concept of keys, providing information about where the value is coming from.
• 🆘 Attention is connected to consciousness and can help in formalizing the understanding of consciousness in neuroscience.

Transcript

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Questions & Answers

Q: What is attention and why is it important in machine learning?

Attention is a computational mechanism that enables focused computation in machine learning. It allows machines to selectively focus on relevant elements, leading to breakthroughs in various applications.

Q: How does attention work in machine translation?

In machine translation, attention enables the computation to focus on the relevant words in the source sentence for better translation accuracy. It uses a soft selection mechanism to choose the most relevant elements.

Q: Can attention be learned or is it fixed?

Attention can be learned through a content-based soft attention mechanism. The weights for each element are learned via a softmax function, allowing the machine to learn where to attend based on the context.

Q: How is attention connected to consciousness?

Attention has similarities to how the brain's motor system decides to move a limb. Attention can be thought of as an internal action, and understanding consciousness can help improve machine learning abilities.

Summary & Key Takeaways

• Attention is a form of focused computation that allows machines to sequentially focus on relevant elements, leading to breakthroughs in machine translation.

• Attention involves using a soft selection mechanism to compute a score for each element, determining where the attention should be focused.

• Attention is essential in state-of-the-art NLP systems and can unlock the problem of vanishing gradients when combined with memory.