Artificial Intelligence : The Supermind - A Review

Artificial intelligence (AI) is one of the most important technologies in the world today. In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial Intelligence is impacting the future of virtually every industry and every human being. AI has acted as the main driver of emerging technologies like big data, robotics, and IoT, and it will continue to act as a technological innovator for the foreseeable future. AI is simply the study of how to make computer do things which at the moment people do the better. There are many ways to define AI, but one simple definition is “intelligence demonstrated by machines”. Primary goal of AI is to improve computer behaviour so that it can be called intelligent. AI is ubiquitous and is not only limited to computer science but has evolved to include other areas like health, security, education, music, art, and business application. This paper gives an overview of how the AI actually works, its scopes , the different applications of AI, its advantages and disadvantages and many more topics which will give a clear understanding inspite of the boundlessness of AI.

Continuous Delivery of Blockchain Distributed Applications

Ensuring a production-ready state of the application under development is the imminent feature of the Continuous Delivery (CD) approach. In a blockchain network, nodes communicate and store data in a distributed manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research focusing on continuous practices for blockchain and Distributed Ledger Technology (DLT). Specifically, it lacks such works with support for both design and deployment. The author has proposed a solution that takes into account the continuous delivery of a business application to diverse deployment environments in the DLT network. As a result, two continuous delivery pipelines have been implemented using the Jenkins automation server. The first pipeline prepares a business application whereas the second one generates complete node deployment packages. As a result, the framework ensures the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The Smart Contract Design Pattern has been used when building a business application. The modeling aspect of blockchain network installation has required using Unified Modeling Language (UML) and the UML Profile for Distributed Ledger Deployment. The refined model-to-code transformation generates deployment configurations for nodes. Both the business application and deployment configurations are stored in the GitHub repositories. For the sake of verification, tests have been conducted for the electricity consumption and supply management system designed for prosumers of renewable energy.

Towards Continuous Deployment for Blockchain

Ensuring a production-ready state of the application under development is the immanent feature of the continuous delivery approach. In a blockchain network, nodes communicate, storing data in a decentralized manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research, focusing on continuous practices for blockchain and distributed ledger technology. In particular, such works with support for both software development disciplines of design and deployment. Artifacts from considered disciplines have been placed in the 1 + 5 architectural views model. The approach aims to ensure the continuous deployment of containerized blockchain distributed applications. The solution has been divided into two independent components: Delivery and deployment. They interact through Git distributed version control. Dedicated GitHub repositories should store the business application and deployment configurations for nodes. The delivery component has to ensure the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The deployment component is responsible for providing running distributed applications in containers for all blockchain nodes. The approach uses Jenkins and Kubernetes frameworks. For the sake of verification, preliminary tests have been conducted for the Electricity Consumption and Supply Management blockchain-based system for prosumers of renewable energy.

The Practice of Implementing ML Service into an Internet Business Application

Currently, there are a large number of articles describing the theoretical aspects of development in the field of machine learning. However, the experience of their practical application in real systems is described much less often. Basically, authors describe the efficiency, accuracy, and other performance metrics of the resulting solution, but everything stops at the prototype stage. At the same time, how the trained model will behave not on test data, but in real conditions, can be very different from the indicators obtained at the development stage. This article describes the experience of the implementation and real use of a classification service based on machine learning techniques.

Enterprise Financial Risk Identification and Information Security Management and Control in Big Data Environment

With the deepening of business informatization, all kinds of business application data are rapidly gathering, which promotes enterprises to enter the era of big data. Enterprises begin to build the concept of big data, deepen the understanding of big data, extract potential data value, and improve the operation ability of enterprises and information systems. At the same time, big data brings internal control information to the system, which is becoming more and more challenging, so enterprises pay more and more attention to the security of the information system. This paper aims to introduce the enterprise financial risk identification and information security management and control under the big data environment and master the enterprise financial risk identification method so that the enterprise can adapt to the needs of the times competition faster and better. This paper introduces the method of identifying financial risk in the background of big data by classifying the methods of financial risk identification and designing the factor model. Through the experimental investigation of the company's financial asset rate, the enterprise financial risk situation is displayed, and the enterprise can improve the internal management to control the financial risk within a certain range. The experimental results show that from 2016 to 2020, the internal control and asset rate of the enterprise affect the financial risk of the enterprise, 82% of the operators only have a reasonable debt structure and sufficient solvency, the operator can operate in a safe state and then maintain a low financial risk, and the operator should also take measures to prevent the occurrence of risk in advance and realize the business goal of maximizing benefits.

Dynamic Adaptation Method of Business Process Based on Hierarchical Feature Model

With the continuous complexity and frequent changes in business application scenarios, companies urgently need to establish a flexible business process management mechanism that includes dynamic rules, in which dynamic adaptation methods of business processes play a vital role. Aiming at the problem that the current methods only use the preset process template and the decision-making database, it cannot respond quickly to business changes and reconfigure the business process. This research proposes a dynamic adaptation method of business process based on the hierarchical feature model, builds a hierarchical feature model of complex processes, then establishes a hierarchical business policy set to achieve an agile response to business emergencies. By constructing a mapping model, the feature model is associated with the BPMN model to realize the rapid execution of the reconfiguration process model. The feasibility and effectiveness of the proposed method are verified by process examples and the developed business process dynamic adaptation tool.