Which of the following options is the key factor that led the return of neural networks?

Question

BlackTom AI · Accepted Answer

The question asks: Which of the following options is the key factor that led the return of neural networks?
Option 1: the back-propagation learning algorithm
This option identifies back-propagation as the pivotal mechanism that enabled practical training of multi-layer neural networks, allowing error signals to be propagated backward through the network to adjust weights. This made deep architectures trainable and significantly boosted performance on a variety of tasks.
Option 2: World Wide Web
The Web indeed popularized data and connectivity, but it did not by itself constitute the key factor that revived neural networks. It provided a platform and data sources, yet training and optimization techniques were the essential enablers for neural networks to regain traction.
Option 3: Dramatically increased Computational power (Cloud computing)
Greater compute power later reinforced deep learning success, but the specific historical revival is closely tied to the learning algorithm that could train deeper networks, not merely the availability of hardware. Compute certainly amplified capabilities, yet the core turning point was the training method.
Option 4: Big data (five Vs)
Big data provided the volume and variety of data that deep learning models can leverage, but the decisive reinvigoration came from how models were trained effectively, not solely from data scale. Without suitable algorithms like back-propagation, large data would not yield dramatic gains.
Option 5: Deep learning algorithms
While deep learning algorithms describe the overall approach, the question asks for the key factor that led the return of neural networks. The revival is primarily attributed to the practical training method (back-propagation) enabling deep networks to learn from data, rather than the existence of deep learning concepts alone.
In summary, the central catalyst for the renewed prominence of neural networks was the development and successful use of the back-propagation learning algorithm, which made training multi-layer networks feasible and effective. The other options contributed to the broader ecosystem or outcomes but did not constitute the principal turning point on their own.

42172 Introduction to Artificial Intelligence - Spring 2025 Practice quiz - Module 1

Which of the following options is the key factor that led the return of neural networks?

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[Multiple choice] Which of the following options is the key factor that led the return of neural networks?

Question at position 2 What does backpropagation refer to in neural networks? Computing gradients for all parametersMapping inputs to outputsNormalizing dataUpdating weights in the forward direction

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What is backpropagation in the context of training a language model?

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42172 Introduction to Artificial Intelligence - Spring 2025 Practice quiz - Module 1

Which of the following options is the key factor that led the return of neural networks?

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类似问题

以下哪一项*正确*描述了反向传播算法的步骤？ Which steps *correctly* describe the backpropagation algorithm?

[Multiple choice] Which of the following options is the key factor that led the return of neural networks?

Question at position 2 What does backpropagation refer to in neural networks? Computing gradients for all parametersMapping inputs to outputsNormalizing dataUpdating weights in the forward direction

What is back propagation?

In the connectionist model, the weights vary as a function of training, in which feedback as to the correctness or incorrectness leads to a mathematical adjustment of the weights. A commonly used training procedure for this weight adjustment is known as ___.

What is backpropagation in the context of training a language model?

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以下哪一项正确描述了反向传播算法的步骤？ Which steps correctly describe the backpropagation algorithm?