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The pharmaceutical industry has recently experienced a significant change driven by data. With technological advancements, companies now have unprecedented access to vast amounts of information. This blog discusses the key challenges in data democratization. Read More

Although DeepSeek has several potential applications that might improve productivity and decision-making across several industries, the AI platform has drawbacks such as privacy concerns, reliance on data quality, and ethical challenges. Read More

DeepSeek is a Chinese artificial intelligence company that develops open-source large language models. Based in Hangzhou, Zhejiang, it is owned and funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng. Read More

One of the most significant challenges arising from this increase is reviewer fatigue. Editors struggle to find qualified reviewers for a growing pool of manuscripts, leading to delays and sometimes compromised quality. Read More

For companies prioritizing operational efficiency and cost control, DeepSeek presents a compelling alternative. However, organizations that require highly flexible AI with general-purpose capabilities may still prefer ChatGPT’s broader adaptability. Read More

For businesses involved in manufacturing, equipment failures can have devastating consequences as they can cause production delays, lead to missed deadlines, and, in most cases, lost revenues. Read More

Reduced Downtime: AI’s ability to predict failures allows companies to perform timely maintenance, minimizing disruptions. Companies implementing AI-driven PdM report up to a 50% reduction in unplanned downtime. Read More

GenAI transforms banking with new opportunities and exposes risks like bias, data security threats, and transparency gaps. Banks must navigate these challenges to ensure responsible use and maintain trust. Read More

A proactive approach unlocks innovation faster. For instance, BNY’s AI tool, Eliza, leverages Nvidia hardware and cloud services from Microsoft Azure and Google Cloud, combined with AI models like GPT-4, Gemini, and Llama. Read More

Before deploying AI, banks must establish strong data infrastructure. GenAI thrives on high-quality, clean data, and banks must invest in building data platforms that ensure their information’s availability, consistency, and integrity. Read More

Banks can proactively identify and mitigate potential cost overruns due to regulatory changes, cybersecurity threats, or technological challenges using risk management frameworks. This approach ensures that contingency plans are in place. Read More

AI models, especially those using deep learning, offer powerful predictive capabilities but often work like a “black box,” making it hard for bank staff to fully understand how decisions are made. Read More

Banks are using generative AI models to analyze data and deliver highly personalized customer interactions. Rather than waiting for customer inquiries, AI anticipates needs, delivering tailored advice and product recommendations. Read More

Real-time processing enables systems to capture, analyze, and act on visual data with minimal latency. Unlike batch processing, which deals with accumulated data, real-time systems process data streams constantly. Read More

Imagine seeing thousands of packages tracked in real-time and each movement monitored and recorded with precision in a vast warehouse. The combination of IoT and computer vision enables such capabilities. Read More