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.
Large World Models: Use Cases & Real-Life Examples ['26]
Despite advances in large language models, artificial intelligence remains limited in its ability to understand and interact with the physical world due to the constraints of text-based representations. Large world models address this gap by integrating multimodal data to reason about actions, model real-world dynamics, and predict environmental changes.
Top 5 AI Guardrails: Weights and Biases & NVIDIA NeMo
As AI becomes more integrated into business operations, the impact of security failures increases. Most AI-related breaches result from inadequate oversight, access controls, and governance rather than technical flaws. According to IBM, the average cost of a data breach in the US reached $10.22 million, mainly due to regulatory fines and detection costs.
Specialized AI Models: Vertical AI & Horizontal AI in 2026
While ChatGPT grabbed headlines, the real business value comes from AI built for specific problems. Companies are moving beyond general-purpose AI toward systems designed for their exact needs. This shift is creating three distinct types of specialized AI – each solving different business challenges.
AI Hallucination Detection Tools: W&B Weave & Comet ['26]
We benchmarked three hallucination detection tools: Weights & Biases (W&B) Weave HallucinationFree Scorer, Arize Phoenix HallucinationEvaluator, and Comet Opik Hallucination Metric, across 100 test cases. Each tool was evaluated on accuracy, precision, recall, and latency to provide a fair comparison of their real-world performance.
AI Hallucination: Compare top LLMs like GPT-5.2 in 2026
AI models can generate answers that seem plausible but are incorrect or misleading, known as AI hallucinations. 77% of businesses concerned about AI hallucinations.
No-Code AI: Benefits, Industries & Key Differences in 2026
No-code AI tools allow users to build, train, or deploy AI applications without writing code. These platforms typically rely on drag-and-drop interfaces, natural language prompts, guided setup wizards, or visual workflow builders. This approach lowers the barrier to entry and makes AI development accessible to users without a programming background.
Top 7 Machine Learning Process Mining Use Cases with GenAI
For more than a decade, machine learning process mining has been used to enhance traditional methods. Today, vendors promote process mining AI with features such as predictive analytics and recent generative AI integrations, but many business leaders still struggle to see how these capabilities translate into practical benefits.
When Will AGI/Singularity Happen? 8,590 Predictions Analyzed
We analyzed 8,590 scientists’, leading entrepreneurs’, and the community’s predictions for quick answers on Artificial General Intelligence (AGI) / singularity timeline: Explore key predictions on AGI from experts like Sam Altman and Demis Hassabis, insights from major AI surveys on AGI timelines, and arguments for and against the feasibility of AGI: Artificial General Intelligence timeline
20 Strategies for AI Improvement & Examples in 2026
AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift over time when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions.
AI Reasoning Benchmark: MathR-Eval in 2026
We evaluated eight leading LLMs using a 100-question mathematical reasoning dataset, MathR-Eval, to measure how well each model solves structured, logic-based math problems. All models were tested zero-shot, with identical prompts and standardized answer checking. This enabled us to measure pure reasoning accuracy and compare both reasoning and non-reasoning models under the same conditions.