2024 is going to be an important transition year for artificial intelligence. 2023 was the public debut of generative AI and large language models (LLMs), a year of amazement, excitement, occasional panic and, yes, more than a little bit of hype. The year ahead is when businesses begin to make the promise of advanced artificial intelligence real, and we’ll begin seeing the effects on how we work and live.
In the age of rapid innovation, organizations are increasingly adopting low-code business process automation (BPA) to meet demand for speedy custom application delivery and seamless digital workflows. Perhaps that’s why global demand for low-code automation platforms (LCAPs) is expected to grow 25% to a $10 billion market value in 2023. So what does the ascendance of BPA tell us about the evolution of innovation?
In one of our recent blog posts, about six key predictions for Enterprise AI in 2024, we noted that while businesses will know which use cases they want to test, they likely won’t know which ones will deliver ROI against their AI and ML investments. That’s problematic, because in our first survey this year, we found that 57% of respondents’ boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year, while 37% expect a single-digit increase.
In 2022, Marriott, the renowned global hotel and resort company, fell victim to a cunning social engineering attack. During this security breach, cyber attackers successfully exfiltrated a staggering 20 gigabytes of highly sensitive customer data, including personal information and credit card numbers. In that very year, InterContinental Hotels Group (IHG) found itself in the crosshairs of a cyberattack that effectively crippled its booking systems and rendered its mobile apps inoperative.
In Kubernetes environments, a suite of essential tools has emerged, addressing various aspects of deployment, management, and troubleshooting within distributed systems. These Kubernetes tools collectively aim to streamline complex processes, enhance productivity, and alleviate challenges inherent in managing modern applications. Efficient deployment, management, monitoring, and troubleshooting are crucial for achieving optimal productivity in Kubernetes ecosystems.
In Swift there are 3 primary types of collections to store your data in a structured way, namely: In this article we aim to give you an overview of each. Specifically we want to show how they’re declared, illustrate the most common operations of each, provide comparisons between them where applicable and highlight the various performance considerations.
