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

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December 14, 2019
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Yoshua Bengio: Attention and Consciousness (NeurIPS 2019)

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.

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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.

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