AI Foundations
Explore foundational concepts, tools, and evaluation methods that support the effective development and deployment of AI in business settings. This section helps organizations understand how to build reliable AI systems, measure their performance, address ethical and operational risks, and select appropriate infrastructure. It also provides practical benchmarks and comparisons to guide technology choices and improve AI outcomes across use cases.
Frugal AI: Principles, Use Cases & Real-life Examples
From healthcare providers in remote regions to manufacturers optimizing production lines, many industries face limits in budget, infrastructure, and energy use when adopting artificial intelligence. Frugal AI addresses these constraints by prioritizing efficiency, sustainability, and inclusivity, enabling AI systems to deliver measurable value with minimal resources.
Large Quantitative Models: Applications & Challenges
Modern systems are becoming too complex for traditional statistical analysis, as institutions now handle massive datasets, including patient data, weather data, and financial market data. Large quantitative models (LQMs) help by processing these datasets, integrating structured and unstructured data, and applying predictive modeling to uncover patterns and provide data-driven insights that traditional methods cannot deliver.
Demand Forecasting in the Age of AI & Machine Learning
Businesses face different inventory challenges when they are dealing with supply chains. Addressing supply chain issues is paramount. Demand forecasting enables businesses to reduce supply chain costs and achieve significant improvements in financial planning, capacity planning, profit margins, and risk assessment decisions.
How to Measure AI Performance: Key Metrics & Best Practices
Measuring AI performance is crucial to ensuring that AI systems deliver accurate, reliable, and fair outcomes that align with business objectives. It helps organizations validate the effectiveness of their AI investments, detect issues like bias or model drift early, and continuously optimize for better decision-making, operational efficiency, and user satisfaction.
AI Fail: 4 Root Causes & Real-life Examples
Whether it’s a self-driving car crash, a biased algorithm, or a breakdown in a customer service chatbot, failures in deployed AI systems can have serious consequences and raise important ethical and societal questions.
Artificial Superintelligence: Opinions, Benefits & Challenges
The prospect of artificial superintelligence (ASI), a form of intelligence that would exceed human capabilities across all domains, presents both opportunities and significant challenges. Unlike current narrow AI systems, ASI could independently enhance its capabilities, potentially outpacing human oversight and control. This development raises concerns regarding governance, safety, and the distribution of power in society.
Google's AI Strategy and 11 Key Developments
Google’s current artificial intelligence strategy emphasizes advanced integration of AI models into enterprise solutions, with significant investments focused on improving functionality, accuracy, and efficiency. Check out 10 recent developments for businesses considering implementing Google’s AI tools and platforms into their workflows. 1. Gemini 2.5: Google’s advanced multimodal AI Google DeepMind launched Gemini 2.
Explainable AI (XAI) in 2026: Guide to enterprise-ready AI
As AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. This approach is problematic since it prevents transparency, trust and model understanding. After all, people don’t easily trust a machine’s recommendations that they don’t thoroughly understand.
AI Explained: Trends and Applications by Industry in 2026
Artificial Intelligence (AI) allows computers to learn from experience, adapt to new inputs and perform human-like tasks. Most of the AI examples you’ve heard about –from chess-playing machines to self-driving cars–rely heavily on deep learning, a subfield of AI.
Image Recognition vs Classification: Applications with Examples
Businesses increasingly leverage AI-powered visual data solutions, but confusion between image recognition and classification leads to inefficiencies. Understanding the key differences helps businesses optimize AI deployment in the security, healthcare, and retail fields. Explore image recognition vs classification, their key differences, and applications with real-life examples.