Overall, Java developers love Spring/Spring Boot because it saves them time and supports their testing experiences. The Diffblue Survey found that Spring’s standardized testing approach makes it easier to apply a technique from artificial intelligence (AI) called Reinforcement Learning to automate test-writing. Making this work for Java developers can slash development time as well as improve code coverage.
Software design is also an area where AI can prove to be very effective. There are various benefits of AI-powered coding. AI-assisted tools can help developers to reduce around 50% of the number of keys pressed in the development of software.
The rapid growth of AI also means that technology is making more and more decisions without us questioning them. Whether it is loan approvals, job recruiting or facial recognition, companies now more than ever need to make sure their AI applications do not discriminate against humans. To minimize the risk of AI bias – the unintended distortion of decisions by artificial intelligence – developers must keep a number of things in mind.
Why does DIY AIOps fail and what is the root cause? In many cases, all the time and effort put into a do-it-yourself project simply winds up being wasted. This article looks at how to safely encourage AIOps exploration and measure ROI from AIOps without the risk of failure.
Justin Reock, Chief Architect for OpenLogic at Perforce Software, describes what is software fuzzing and why it is needed. Justin is the author on a chapter about fuzzing in a new book from Perforce Software: “Accelerating Software Quality: Machine Learning & Artificial Intelligence in the Age of DevOps”.
AI makes it possible to process and interpret huge quantities of data far more quickly and efficiently, potentially unlocking a whole host of new insights. In this article, we’ll take a look at what AI is doing for data centers and why you need to make the most effective possible use of this transformative technology.
We spoke with Guy Fighel, General Manager & GVP Product Engineering at New Relic about AI and AIOps. What role does artificial intelligence play when it comes to monitoring and observability? What are the best practices when implementing AI within your team?
We spoke to Adam Smith, founder and CEO of Kite, the AI-powered coding assistant that uses models trained on 40 million open source code files. See how Kite helps developers code smarter and faster, how it handles data privacy, and what’s in store for the future of using deep learning for code.
What does the future of AI and AIOps hold? Will Cappelli, CTO EMEA and Global VP of Product Strategy at Moogsoft outlines the five biggest trends affecting AI and AIOps today and why IT organizations should track their development and their implications.
It is a challenge to make AI-driven models transparent. They are a blackbox and can cause serious issues. The aim of a glassbox is to provide greater transparency in how a model is operating and how its outputs have been reached.
Not implementing AI in some form could mean literally the end of your business as competitors pass you by. AI isn’t taking jobs away; in fact it actually might increase job satisfaction by taking over redundant, mundane tasks.
AI and machine learning are already changing the way we work, and the future will likely see some big changes. AI could also create more jobs and help us recruit candidates as long as people are willing to adapt and work smarter.
We are in the middle of yet another wave of digital transformation with AI at its core. Business leaders’ skills are put to the test here, as transformation is never an easy task. Let’s look at the common hurdles of AI implementation.
In simple terms, AI helps companies by employing ML (machine learning) and problem-solving in the recruitment process. This way, this technology allows companies to find potential clients for a position.