AI
Specialized AI Models: Vertical AI & Horizontal AI in 2025
Foundation models like ChatGPT with many capabilities (e.g. translation, text generation) trained on public data have launched the generative AI wave.
Data Quality in AI: Challenges, Importance & Best Practices
Poor data quality hinders the successful deployment of AI and ML projects. Even the most advanced AI algorithms can yield flawed results if the underlying data is of low quality. We explain the importance of data quality in AI, the challenges organizations encounter, and the best practices for ensuring high-quality data.
Top 10 Strategies for AI Improvement with Real Life Examples
AI systems achieved remarkable milestones (e.g., exceeding human performance in image and speech recognition); however, AI progress is slowing down as scaling yields fewer benefits. Additionally, AI and ML models degrade over time unless they are regularly updated or retrained.This makes it critical to utilize all levers to improve AI models continually.
Reproducible AI: Why it Matters & How to Improve it in 2025
Reproducibility is a fundamental aspect of scientific methods, enabling researchers to replicate an experiment or study and achieve consistent results using the same methodology. This principle is equally vital in artificial intelligence (AI) and machine learning (ML) applications, where the ability to reproduce outcomes ensures the reliability and robustness of models and findings.
Top 10 AI in Fashion Use Cases & Examples in 2025
Creative bottlenecks, inefficient supply chains, and rising consumer expectations are pushing fashion brands to seek smarter solutions. According to McKinsey, generative AI can offer a path forward by adding up to $275 billion to operating profits in the fashion, apparel, and luxury sectors until 2028.
5 AI Training Steps & Best Practices in 2025
AI enhances business efficiency, with leaders adopting pre-built solutions or developing their own. However, almost 80% of AI projects underdeliver or fail. One of the biggest challenges in developing AI systems is training the models.
Top 12 Use Cases in AI for Neurology with Real-Life Examples
AI-powered diagnostics and predictive analytics help improve accuracy in identifying neurological conditions, assist in early detection, and support decision-making. These advancements help address challenges in healthcare sector by reducing diagnostic delays and enabling efficient patient management. Explore the top 12 AI for neurology use cases and the ethical considerations healthcare providers should be aware of.
Top 3 AI Applications in the Field of Dermatology in 2025
Artificial intelligence (AI) is revolutionizing many areas in the healthcare sector. One of the emerging areas where AI is being increasingly applied is dermatology. However, understanding of this technology in dermatology is still limited.
Top 10 Sustainability AI Applications & Real Life Examples
According to PwC, GenAI could improve operational efficiency, which might indirectly reduce carbon footprints in business processes. Companies can implement strategies to reduce energy consumption during the development, customization, and inferencing stages of AI models. By leveraging GenAI applications, companies can offset emissions in other areas of their operations.
AI in X-ray Analysis: Benefits & Challenges in 2025
X-ray is the most common form of medical imaging: it is estimated that 3.6 Billion X-ray images are taken each year. However, 45% of radiologists report burnout due to time pressure and the rising volume of scans. AI can analyze and report X-ray results, which can have an impactful effect on radiology.