Top 15 Artificial Intelligence Platforms - Predictive Analytics Today


Artificial Intelligence is when a machine mimics the cognitive functions that humans associate with other human minds, such as learning and problem solving, reasoning, problem solving, knowledge representation, social intelligence and general intelligence. The central problems of AI include reasoning, knowledge, planning, learning, natural language processing perception and the ability to move and manipulate objects. Approaches include statistical methods, computational intelligence, soft computing and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. AI platform is defined as some sort of hardware architecture or software framework (including application frameworks), that allows software to run.

How artificial intelligence is transforming software engineering


This paper explores the influences of the growing field of artificial intelligence (AI) on the software development process. Several techniques and their potential positive effects on multiple areas of software development will be explored. The main focus is on requirements engineering, the development process, testing and deployment, and the human factor in the field of software development as a whole. A number of scenarios concerning the future influence of AI on software engineering are presented.

AI Keeps Buggy Code From Crashing The Cloud - InformationWeek


Testing is a thankless chore in all software engineering initiatives. It's a burden that developers and operations personnel usually wish to offload to automated tooling. Automation is an ongoing trend in software development and operations (DevOps). Increasingly, artificial intelligence is the heart of automated software testing in the new world of cloud-native computing and 24x7 DevOps workflows. As modern applications sprawl over complex multi-cloud and mesh environments, AI-driven DevOps automation will become ever more essential.

Why AI and Machine Learning will Redefine Software Testing


With the advent of DevOps and Continuous Delivery, businesses are now looking for real-time risk assessment throughout the various stages of the software delivery cycle. Although Artificial Intelligence (AI) is not really new as a concept, applying AI techniques to software testing has started to become a reality just the past couple years. Down the line, AI is bound to become part of our day-to-day quality engineering process, however, prior to that, let us take a look at how AI can help us achieve our quality objectives. Day after day, QA Engineers face a plethora of difficulties and waste a lot of time to find a proper solution. When it comes to making new additions, the existing code which has already gone through the testing process may stop working.

Pursuing DevOps ROI


Pursuing DevOps ROI (return on investment) is compelling for organizations that adopt this approach to agile development practices. With the evolution toward cloud and mobile apps that run on converged infrastructures companies that implement DevOps processes can realize significant benefits in the three components of ROI. These include reduced costs, enhanced productivity and faster time to revenue. DevOps can also help mitigate risks, such as customer loss due to poor user experience, operational inefficiencies, and non-compliance with GRC (governance, regulatory, compliance) mandates. In contrast, enterprises with legacy systems that adhere to conventional development and operations processes jeopardize being overcome by modern applications and the new computing architectures they require.