Huawei H13-311_V3.5 - HCIA-AI V3.5 Exam

HarmonyOS provides AI capabilities not only through HMS Core (Huawei Mobile Services Core), but also through other system-level integrations and AI frameworks. While HMS Core is one way to offer AI functionalities, HarmonyOS also has native support for AI processing that can be accessed by external systems or applications beyond HMS Core.

Thus, the statement is false as AI capabilities are not limited solely to HMS Core in HarmonyOS.

HCIA AI References:

Introduction to Huawei AI Platforms: Covers HarmonyOS and the various ways it integrates AI capabilities into external systems.

MindSpore provides several callback functions that can be used to monitor, modify, or control the behavior of the training process. These include:

SummaryCollector: Collects summaries such as loss and accuracy for visualization and monitoring.

ModelCheckpoint: Saves model parameters during or after training.

LossMonitor: Monitors the loss values during training and can stop training if certain conditions are met.

TrainStep is not a callback but rather a fundamental step in training.

AI is not limited to just computer vision and speech processing. In addition to these fields, AI encompasses other important areas such as natural language processing (NLP), robotics, smart finance, autonomous driving, and more. Natural language processing focuses on understanding and generating human language, while other fields apply AI to various industries and applications such as healthcare, finance, and manufacturing. AI is a broad field with numerous application areas.

AI chips, also known as AI accelerators, are specialized hardware designed to enhance the performance of AI workloads, particularly for tasks like matrix multiplication, which is heavily used in machine learning and deep learning algorithms. These chips optimize operations like matrix multiplications because they are computationally intensive and central to neural network computations (e.g., in forward and backward passes).

HCIA AI References:

Cutting-edge AI Applications: Discussion of AI chips and accelerators, with a focus on their role in improving computation efficiency.

Deep Learning Overview: Explains how neural network operations like matrix multiplication are optimized in AI hardware.

In MindSpore, the neural network structure is defined by inheriting the Cell class, which represents a computational node or a layer in the network. Users can customize the network by overriding the __init__ method (for initializing layers) and the construct method (for defining the forward pass of the network). This modular design allows for easy and flexible neural network construction.

Thus, the statement is true because MindSpore's framework allows developers to build neural networks by extending the Cell class and defining custom behavior through the __init__ and construct methods.

HCIA AI References:

AI Development Framework: Detailed coverage of building neural networks in MindSpore, including how to inherit from the Cell class and rewrite key methods for custom network architecture.

In standard Recurrent Neural Networks (RNNs), the Tanh activation function is commonly used in the hidden layers. The Tanh function squashes input values to a range between -1 and 1, allowing the network to learn complex patterns over time by transforming the input data into non-linear patterns.

While other activation functions like Sigmoid can be used, Tanh is preferred in many RNNs for its wider range. ReLU is generally used in feed-forward networks, and Softmax is often applied in the output layer for classification problems.

HCIA AI References:

Deep Learning Overview: Describes the architecture of RNNs, highlighting the use of Tanh as the standard activation function.

AI Development Framework: Discusses the various activation functions used across different neural network architectures.

Activation functions like Sigmoid, tanh, and softsign suffer from the vanishing gradient problem when used in deep networks. This happens because, in these functions, gradients become very small as the input moves away from the origin (either positively or negatively). As a result, the weights of the earlier layers in the network receive very small updates, hindering the learning process in deep networks. This is one reason why activation functions like ReLU, which avoid this issue, are often preferred in deep learning.

The concept of "artificial intelligence" was first formally introduced in 1956 during the Dartmouth Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. This event is widely regarded as the birth of AI as a field of study. The conference aimed to explore the idea that human intelligence could be simulated by machines, laying the groundwork for subsequent AI research and development.

This date is significant in the history of AI because it marked the beginning of a concentrated effort to develop machines that could mimic cognitive functions such as learning, reasoning, and problem-solving.