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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.

Explore AI Foundations

AGI Benchmark: Can AI Generate Economic Value

AI FoundationsSep 26

AI will have its greatest impact when AI systems start to create economic value autonomously. We benchmarked whether frontier models can generate economic value. We prompted them to build a new digital application (e.g., website or mobile app) that can be monetized with a SaaS or advertising-based model.

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AI FoundationsSep 26

When Will AGI/Singularity Happen? 8,590 Predictions Analyzed

We analyzed 8,590 scientists’, leading entrepreneurs’, andthe 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 five major AI surveys on AGI timelines, and arguments for and against the feasibility of AGI: Artificial General Intelligence timeline

AI FoundationsSep 19

Top 15 Strategies for AI Improvement & 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.

AI FoundationsSep 18

Hands-On Top 10 AI-Generated Text Detector Comparison

We conducted a benchmark of the most commonly used 10 AI-generated text detector.

AI FoundationsSep 18

AI Hallucination: Comparison of the Popular LLMs

AI models sometimes generate data that seems plausible but is incorrect or misleading; known as AI hallucinations. According to Deloitte, 77% of businesses who joined the study are concerned about AI hallucinations.

AI FoundationsSep 10

Top 5 Facial Recognition Challenges & Solutions

Facial recognition technology has rapidly integrated into daily life, powering applications ranging from access control systems to law enforcement investigations. Yet its widespread adoption has also exposed a range of technical, ethical, and societal challenges. Discover the top 5 facial recognition challenges and solutions to prevent fraud and misuse. 1.

AI FoundationsSep 3

Specialized AI Models: Vertical AI & Horizontal AI

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 FoundationsSep 1

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.

AI FoundationsAug 29

Large World Models: Use Cases & Real-Life Examples

Artificial intelligence has advanced significantly with the development of large language models; however, these systems continue to struggle to comprehend and interact with the physical world. Text alone cannot capture spatial relationships, dynamic environments, or the causal impact of actions, thereby limiting progress in fields such as robotics, healthcare, and autonomous systems.

AI FoundationsAug 27

World Foundation Models: 10 Use Cases & Examples

Training robots and autonomous vehicles (AVs) in the physical world can be costly, time-consuming, and risky. World Foundation Models offer a scalable alternative by enabling realistic simulations of real-world environments. These models accelerate development and deployment in robotics, AVs, and other domains by reducing reliance on physical testing.

AI FoundationsAug 25

Time Series Foundation Models: Use Cases & Benefits

Time series foundation models (TSFMs) build on advances in foundation models from natural language processing and vision. Using transformer-based architectures and large-scale training data, they achieve zero-shot performance and adapt across sectors such as finance, retail, energy, and healthcare.