Huawei H13-311_V3.5 Practice Test - Questions Answers, Page 2

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.

Artificial intelligence is a broad field that encompasses several subfields. Two key subfields are:

Expert systems, which are computer programs that mimic the decision-making abilities of a human expert by reasoning through bodies of knowledge. These systems are used in various domains such as healthcare, engineering, and finance.

Computer vision, which enables machines to interpret and understand visual data from the world. It includes tasks such as object detection, image recognition, and video analysis.

While options like backpropagation and smart finance are related to AI, they represent specific algorithms or application areas rather than broad subfields.

Computer vision, a subfield of AI, has various application scenarios that involve the analysis and understanding of images and videos. Some key application scenarios include:

Video action analysis: Identifying and analyzing human actions or movements in videos.

Image search: Using visual information to search for similar images in large databases.

Smart albums: Organizing and categorizing photos using AI-based image recognition algorithms to group them by themes, people, or events.

Voice navigation is a part of natural language processing and speech recognition, not computer vision.

OpenCV is a library used primarily for computer vision tasks like image and video processing. It is not considered an AI computing framework in the same way as PyTorch, MindSpore, or TensorFlow, which are commonly used frameworks for developing AI and machine learning models. AI frameworks like PyTorch, TensorFlow, and Huawei's MindSpore are designed to facilitate the development and deployment of deep learning models.

While speech recognition technology has improved significantly, its accuracy can still be affected by external factors such as noise, background sound, accents, and speech clarity. Although systems can achieve over 90% accuracy under controlled conditions, the accuracy drops in noisy or complex real-world environments. Therefore, the statement that today's speech processing technology can always achieve high recognition accuracy is incorrect.

Speech recognition systems are sophisticated but still face challenges in environments with heavy noise, where the technology has difficulty interpreting speech accurately.

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.

The gradient descent method is a core optimization technique in machine learning, particularly for neural networks and deep learning models. The common gradient descent methods include:

Batch Gradient Descent (BGD): Updates the model parameters after computing the gradients from the entire dataset.

Mini-batch Gradient Descent (MBGD): Updates the model parameters using a small batch of data, combining the benefits of both batch and stochastic gradient descent.

Stochastic Gradient Descent (SGD): Updates the model parameters for each individual data point, leading to faster but noisier updates.

Multi-dimensional gradient descent is not a recognized method in AI or machine learning.

Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it updates the model parameters for each individual training example, which can introduce a significant amount of noise into the optimization process. This leads to a less smooth and more chaotic path toward the global minimum compared to methods like batch gradient descent or mini-batch gradient descent, which provide more stable updates.

The k-nearest neighbors (k-NN) algorithm is a non-parametric algorithm used for both classification and regression. In classification tasks, it typically uses majority voting to assign a label to a new instance based on the most common class among its nearest neighbors. The algorithm works by calculating the distance (often using Euclidean distance) between the query point and the points in the dataset, and then assigning the query point to the class that is most frequent among its k nearest neighbors.

For regression tasks, k-NN can predict the outcome based on the mean of the values of the k nearest neighbors, although this is less common than its classification use.