Elements applications of artificial intelligence in transport and logistics - страница 6

In 2017, Fei-Fei Li highlights the importance of deep learning in data science and describes some of the research that has been done in this area.

Artificial neural networks (ANNs), commonly referred to simply as deep learning algorithms, represent a paradigm shift in artificial intelligence. They have the ability to explore concepts and relationships without any predefined parameters. ANNs are also capable of studying unstructured information that goes beyond the requirements of established rules. Initial ANN models were built in the 1960s, but research has intensified in the last decade.

The rise in computing power opened up a new world of computing through the development of convolutional neural networks (CNNs) in the early 1970s. In the early 1980s, Stanislav Ulam developed the symbolic distance function, which became the basis for future network learning algorithms.

By the late 1970s, several CNNs were deployed on ImageNet. In the early 2000s, floating point GPUs provided exponential performance and low power consumption for data processing. The emergence of deep learning algorithms is a consequence of the application of more general computational architectures and new methods for training neural networks.

With the latest advances in multi-core and GPU technology, training neural networks with multiple GPUs is possible at a fraction of the cost of conventional training. One of the most popular examples is GPU deep learning. Training deep neural networks on GPUs is fast, scalable, and requires low-level programming capabilities to implement modern deep learning architectures.

Optimization of genetic algorithms can be an effective method for finding promising solutions to computer science problems.

Genetic algorithm techniques are usually implemented in a simulation environment, and many common optimization problems can be solved using standard library software such as PowerMorph or Q-Learning.

Traditional software applications based on genetic algorithms require a trained expert to program and customize their agent. To enable automatic scripting, genetic algorithm software can be distributed as executable source code, which can then be compiled by ordinary users.

Genetic algorithms are optimized for known solutions that can be of any type (e.g. integer search, matrix factorization, partitioning, etc.). In contrast, Monte Carlo optimization requires that an optimal solution can be generated by an unknown method. The advantage of genetic algorithms over other optimization methods lies in their automatic control over the number of generations required, initial parameters, evaluation function, and reward for accurate predictions.

An important property of a genetic algorithm is its ability to create a «wild» configuration of parameters (for example, alternating hot and cold endpoints) that correspond to a given learning rate (learning rate times the number of generations). This property allows the user to analyze and decide if the equilibrium configuration is unstable.

The downside of genetic algorithms is their dependence on distributed memory management. While extensive optimization techniques can be used to handle large input sets and multiple processor / core configurations, the complexity of this operation can make genetic algorithm decisions vulnerable to resource constraints that impede progress. Even with the genetic algorithm code, in theory, programs based on genetic algorithms can only find solutions to problems when run on the appropriate computer architecture. Examples of problems associated with a genetic algorithm running on a more limited architecture include memory limits for storing representations of the genetic algorithm, memory limits imposed by the underlying operating system or instruction set, and memory limits imposed by the programmer, such as limits on the amount of processing power, allocated for the genetic algorithm and / or memory requirements.