AI benefits healthcare by improving the speed of patient diagnosis. Hallucinations are one concern in this process because they can lead to incorrect treatment. Chain-of-thought (CoT) prompting solves this by instructing an LLM to use advanced reasoning to find the best possible answer. Healthcare professionals who use AI can consider using CoT prompting to improve diagnosis speed and accuracy.
Natural disasters are devastating forces that cripple infrastructure and result in deaths if the affected region is unprepared. Traditional weather prediction models are computationally expensive. AI weather prediction models provide faster, higher-quality predictions. Government officials tasked with emergency management can use AI weather prediction models to improve their preparedness and response to natural disasters.
As SMEs push to move their workloads to the cloud, they may neglect to optimize and monitor critical aspects of the performance of their cloud-based applications post-deployment. Metrics such as latency, downtime, and resource inefficiency can deteriorate over time, leading to performance challenges. This article explores how cloud performance engineering can enhance application efficiency, reduce operational costs, and improve user experience. Tech leaders within SMEs will discover practical strategies to implement cloud performance engineering effectively.
Fifty petabytes (PB) of data can be generated by large healthcare institutions each year. However, this data becomes useless because 97% of it is unused. Eventually, this data becomes outdatedāresulting in high storage costs for no ROI. Data minimization helps remove unnecessary data that is being hoarded if it is useful in the future. Chief Data Officers (CDOs) and IT leaders at healthcare institutions should apply data minimization as a data management strategy to lower data storage costs, reduce risk from data breaches, and ensure compliance with data protection regulations.
Effective September 2025, the EU Data Act aims to democratize data access across the EU by granting users of connected devices the right to access and share their data. SMEs will benefit from measures that promote a more favourable business environment. IT managers should adjust strategies to leverage these opportunities.
Businesses still rely on legacy storage because they are satisfied with their setup, do not want to disrupt operations, and want to avoid additional spending to change hardware that is not broken. However, legacy storage has compatibility and performance issues with modernized applications and high costs to maintain and replace. CIOs and IT leaders must upgrade their legacy storage to save costs and improve the efficiency of their applications.
Early adopters will use machine customers to conduct transactions semi-autonomously and autonomously for business by 2028 and 2032, respectively. If businesses selling goods and services continue to only focus on human customers then these machine customers will be missed and acquired by competitors. For retail businesses to stay competitive and increase profits, IT leaders and Customer Experience (CX) experts must plan to target machine customers.
Businesses are continuing to enhance their efficiency by using AI. This increases the need for LLMs that perform well on enterprise tasks. Fine-tuning is not a viable method because it is costly. Prompt caching (context caching) and Retrieval-Augmented Generation (RAG) are more suitable. AI engineers should read this article to learn more about these two methods to create cost-effective LLMs that perform well on their enterprise data.
Chiplet technology is transforming the semiconductor industry from monolithic to modular system-on-chip (SoC) designs. This approach enhances performance, scalability, and flexibility, offering significant advantages in various sectors. Business leaders should understand this emerging chip design trend and its potential applications in their industry as the technology evolves.
The widespread adoption of Generative AI (GenAI) in applications offers substantial advantages but also introduces various threats because of the myriad components they comprise. To ensure the integrity of AI/ML systems, organizations should manage every component through an AI Bill of Materials (AIBOM) to inventory the data, models, and infrastructure used.
Developers, data scientists, and security experts should advance their AI maturity by adopting AIBOMs to secure and optimize their AI systems.