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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

World Foundation Models: 10 Use Cases & Examples

AI FoundationsNov 18

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.

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AI FoundationsNov 13

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.

AI FoundationsNov 12

AGI Benchmark: Can AI Generate Economic Value

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.

AI FoundationsNov 11

Top 9 AI Providers Compared

The AI infrastructure ecosystem is growing rapidly, with providers offering diverse approaches to building, hosting, and accelerating models. While they all aim to power AI applications, each focuses on a different layer of the stack.

AI FoundationsNov 9

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 five major AI surveys on AGI timelines, and arguments for and against the feasibility of AGI: Artificial General Intelligence

AI FoundationsOct 30

Deepseek: Features, Pricing & Accessibility

A Chinese hedge fund spent $294,000 training an AI model that beats OpenAI’s O1 on reasoning benchmarks. Then they open-sourced it. DeepSeek isn’t your typical AI startup. High-Flyer, an $8 billion quantitative hedge fund, funds the entire operation. No venture capital. No fundraising rounds.

AI FoundationsOct 30

AI Scientist: Automating the Future of Scientific Discovery

AI scientists mark a major advance toward fully automatic scientific discovery, aiming to perform the entire research process independently. Unlike traditional tools, these automated labs can expedite research processes by generating hypotheses, designing and executing experiments, interpreting results, and communicating findings.

AI FoundationsOct 30

AI Hallucination: Comparison of the Popular LLMs

AI models sometimes generate data that seems plausible but is incorrect or misleading; known as AI hallucinations. 77% of businesses concerned about AI hallucinations. We benchmarked 29 different LLMs with 60 questions to measure their hallucination rates: AI hallucination benchmark results Our benchmark revealed that Anthropic Claude 3.7 has the lowest hallucination rate (i.e.

AI FoundationsOct 28

Custom AI: When to Build Your Own Solutions

While ready-made AI tools can meet many business needs, they often fall short in areas that require deep data understanding or specialized workflows. Organizations working in complex or niche industries may find that generic systems don’t fully align with their operations or leverage their proprietary data.

AI FoundationsOct 28

10 Steps to Developing AI Systems

IBM identifies the top AI adoption challenges as concerns over data bias (45%), lack of proprietary data (42%), insufficient generative AI expertise (42%), unclear business value (42%), and data privacy risks (40%).These obstacles can hinder AI implementation, slow innovation, and reduce the return on investment for organizations adopting AI technologies.

AI FoundationsOct 27

Top 5 Facial Recognition Challenges & Solutions

Facial recognition is now part of everyday life, from unlocking phones to verifying identities in public spaces. Its reach continues to grow, bringing both convenience and new possibilities. However, this expansion also raises concerns about accuracy, privacy, and fairness that need careful attention.