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.
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.
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.
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.
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.
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
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.
Deepseek: Features, Pricing & Accessibility
DeepSeek has emerged as a game-changing force in artificial intelligence, challenging established giants like OpenAI and Google with its innovative approach to AI development.
AI Center of Excellence (AI CoE): Meaning & Setup
The adoption of artificial intelligence (AI) is increasing as companies try to capture value from enterprise AI applications. However, according to an IBM survey, challenges such as limited AI expertise, increasing data complexity, and lack of tools for AI development hinder AI adoption for enterprises. To reduce AI project failures, organizations need a dedicated unit to oversee AI initiatives.
What is Composite AI? Business Impact
Artificial intelligence (AI) has opened new capabilities for businesses with a diverse set of use cases across sectors. 78% of organizations now use AI in at least one function Composite AI integrates multiple AI techniques into unified solutions that can address complex business problems more effectively than any single approach.
Top 9 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. Only 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models.