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.
Big data has been present in the industry for quite some time now. These huge data chunks helps enterprises keep a clear track of the information regarding their customers, products, environment and about themselves.
Artificial intelligence could help us fight the coronavirus crisis. AI can, for example, already identify pneumonia on a CT scan in seconds with a high degree of accuracy. See what other things it can do to help flatten the curve.
The initial release of Elyra AI Toolkit has been announced. This toolkit developed by IBM consists of different extensions for Jupyter Notebooks. They are designed to extend its capabilities for developing artifical intelligence models, so let’s take a closer look.
Every Monday, we take a step back and look at all the cool stuff that went down during the previous week. Last week, we spoke to Red Hat’s Jan Wildeboer about open source software development, interviewed the creator of Eclipse Theia, and learned how to combat bias in AI algorithms. Let’s take a closer look.
When artificial algorithms are biased, this can create unethical results, which in turn can lead to PR disasters for businesses. In this article, you will learn about three different types of AI bias – algorithmic, technical, and emergent – and what measures can be used to limit them.
We’re going to need high dimensional probability and statistics and model it in high dimensional geometry. This is why AIOps is inevitable. This article examines four of the reasons why people distrust AI and what properties define big data.
Take a look at our brand new ML Magazine, in which the authors will give you a full idea of the cutting edge of machine learning—from tutorials for certain tools to insights about the relationship between AI and ethics. Don’t miss our first issue that includes an interview with Noam Chomsky.
