Applications of artificial intelligence

About: Applications of artificial intelligence is a research topic. Over the lifetime, 3406 publications have been published within this topic receiving 64800 citations.

The Applications of Artificial Intelligence in Logistics and Supply Chain

Abstract: The range of potential market shows high growth, which can be achieved by the market in next years. It is expected that strategic allocation and asset positioning can help competitive goals. Estimation of certain regulations introduced by the governments of several countries increases the market revenues. It has been predicted that the availability of appropriate means to make powerful distribution channels can specify the expansion of the market's future in the predicted period. The stability of economic government can mostly help favorable development of global market power. It is also predicted that advances in research and development equipment can affect market growth in the prediction period. Need to provide conditions for emergencies such as natural disasters, pandemics, and international commercial wars can enable the market to fight the challenges. In other words, four aspects have been covered: 1- the most common AI techniques in SCM, 2- potential AI techniques to use in SCM, 3- secondary fields of SCM improved by AI, and 4- secondary fields with high potential to be improved by AI. A certain collection of regulations of inclusion and lack of inclusion are used to investigate the articles in four fields of SCM: logistics, marketing, supply chain, and manufacturing. This study provides insights by systematic analysis and synthesis.

Methods and apparatuses for artificial intelligence application building and operational implementation, and machine device

Abstract: A method for building an artificial intelligence application, a method for operational implementation during artificial intelligence application building, apparatuses, and a machine device The method comprises: receiving a selection instruction for blocks on a graphical interface, wherein the blocks are graphical representations of corresponding mathematical primitives (310); by means of the selection instruction, configuring in a building area the blocks for artificial intelligence application building, and linking the blocks with each other to form an artificial intelligence algorithm configuration of an artificial intelligence application built under the control of a user (330); according to the artificial intelligence algorithm configuration, obtaining a dictionary composed of mathematical primitive identifiers corresponding to the blocks and core parameters, the core parameters being configured to correspond to the blocks (350); by means of the dictionary, triggering the artificial intelligence application to operate on a server end The method meets given artificial intelligence application requirements, implements artificial intelligence application building by means of components corresponding to the mathematical primitives, accurately adapts to real needs, freely implements "what you see is what you get" artificial intelligence applications, enhances interaction performance, and lowers thresholds

Monolithic Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment

Abstract: The field of artificial intelligence (AI) has witnessed tremendous growth in recent years, however some of the most pressing challenges for the continued development of AI systems are the fundamental bandwidth, energy efficiency, and speed limitations faced by electronic computer architectures. There has been growing interest in using photonic processors for performing neural network inference operations, however these networks are currently trained using standard digital electronics. Here, we propose on-chip training of neural networks enabled by a CMOS-compatible silicon photonic architecture to harness the potential for massively parallel, efficient, and fast data operations. Our scheme employs the direct feedback alignment training algorithm, which trains neural networks using error feedback rather than error backpropagation, and can operate at speeds of trillions of multiply-accumulate (MAC) operations per second while consuming less than one picojoule per MAC operation. The photonic architecture exploits parallelized matrix-vector multiplications using arrays of microring resonators for processing multi-channel analog signals along single waveguide buses to calculate the gradient vector of each neural network layer in situ, which is the most computationally expensive operation performed during the backward pass. We also experimentally demonstrate training a deep neural network with the MNIST dataset using on-chip MAC operation results. Our novel approach for efficient, ultra-fast neural network training showcases photonics as a promising platform for executing AI applications.

A proposal of a novel model for Artificial Intelligence Planning

Abstract: Artificial Intelligence (AI) is the intelligence of machines and robots and the branch of computer science that aims to create it. And for making these machines intelligent planning is required.AI planning provides good solutions for solving real world problems like planning in NLP, Robotics, Computer-Vision, Cognitive analysis etc. This paper provides an overview of various AI application areas with a major focus on AI Planning, and proposes a model for AI Planning which includes better interface capabilities in conjunction with the available AI Planners.