The study suggests that projects using AI code-generating tools still need some level of human oversight and expertise for critical security tasks.
The future of recommender systems is real-time machine learning. Here's everything you need to know about what that means for enterprise applications.
RTInsights is a media partner of apply(recsys) which takes place December 6, 2022. This article is the first in a series on recommender systems. What Data Engineers Need to Know About Recommender Systems, According to ChatGPT Ahead of Tecton’s virtual apply(recsys) conference on December 6, we interviewed OpenAI’s new chatbot, ChatGPT, about some of the […]
Imagine two employees working together to solve a problem, each providing unique expertise, except, one is a software application. As applications gain more control of a business, such collaborations will become increasingly common as organizations look to gain and keep a competitive edge. Software will automate deterministic functions and standardized activities and humans will add experience. […]
Adopting a DevSecOps approach will be the driving force that puts enterprises into pole position as edge leaders and ensure intelligent system success.
Making each step up the human/machine interaction spectrum ladder, from assisted to augmented to an autonomous machine, requires increased trust in the real-time systems.
Real-time systems can have development challenges due to their complexity, distributed nature, and the need for analysis to be done in real-time.
Real-time location systems provide the supervisory tools administrators and healthcare workers need to ensure fast, efficient care.
Imagine two employees working together to solve a problem, each providing unique expertise, except, one is a software application.
Adoption of low-code tools has risen in recent years because they let developers and business executives collaborate to build new innovative applications.