Data & AI

Why Physical AI Needs Real-World Autonomous Vehicle Data

Article

The sim-to-real gap in autonomous vehicles only closes with real-world data

Simulation accelerates AV development, but real-world data closes the loop. Learn how physical AI programs tackle the long tail of autonomous driving edge cases.

As Foundation Models Commoditize, Value Moves to Fine-Tuning

Article

Generative AI

When foundation models commoditize, the value moves to fine-tuning and evaluation

As foundation models commoditize, the real value now lies in fine-tuning, evaluation and domain-specific AI data. Here's where differentiation is built.

Article

Physical AI

Why training data yield rate matters in physical AI

Eight hours of robot data collection yield just two to four hours of usable training data. Explore what physical AI leaders say about closing that gap.

Article

AI Strategy

What is an AI developer? The role redefining enterprise software in 2026

What is an AI developer? The term is drifting, with multiple definitions and none of them quite right. Here's the definition that matters for enterprise teams.

Article

AI Strategy

What is an AI system builder? The infrastructure role enterprises are missing

What is an AI system builder? It’s the role most enterprises don't have and can't scale AI without. Here’s a practical definition for engineering leaders.

Article

Agentic AI & Automation Banking, Financial Services & Insurance

Agentic banking doesn’t require a core replacement: What 164 corporate treasurers told us about their banks

What 164 corporate treasurers told us about North American banking’s infrastructure gap — and why closing it doesn’t require a core replacement.

Article

Agentic AI & Automation

From assistants to agents: What Google I/O 2026 means for enterprise leaders

Google I/O 2026 changed how enterprise AI gets built. Here's what leaders need to act on and what can wait.

Article

Agentic AI & Automation

The human bottleneck in AI training: Can expert annotators scale with frontier models?

Discover how TELUS Digital has built the infrastructure for frontier AI, including expert talent pipelines, human-AI collaboration architecture and adversarial safety tools designed for a moment when qualified human judgment, at scale, is the real bottleneck.

Article

Physical AI

A technical primer on world models

Learn how world models differ from LLMs, the five properties that make them useful and why causality and action-conditioned prediction are keys to physical AI.

Article

Physical AI Technology

Physical AI training data: What good pre-training and post-training datasets look like

Master the data requirements for physical AI. Learn how multimodal sensor fusion, 6D pose estimation and high-context annotations bridge the sim-to-real gap.

Solution Update

Generative AI

How to build post-training AI Interfaces at the speed of R&D with Express Interface

Accelerate AI development with Express Interface. Build dynamic, event-driven post-training interfaces fast using a drag-and-drop UI and Python logic. Eliminate engineering bottlenecks, embed real-time LLM-as-a-judge QC and curate high-quality training data for LLMs, VQA and text-to-SQL tasks.

Article

Generative AI

From validation to collaboration: Rethinking human-in-the-loop processes in trust and safety automation

Learn how TELUS Digital combines LLM automation with "human-on-the-loop" policy stewardship to navigate cultural nuance, resolve policy collisions and prioritize accuracy over scale in content moderation.

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Data & AI

The sim-to-real gap in autonomous vehicles only closes with real-world data

When foundation models commoditize, the value moves to fine-tuning and evaluation

Why training data yield rate matters in physical AI

What is an AI developer? The role redefining enterprise software in 2026

What is an AI system builder? The infrastructure role enterprises are missing

Agentic banking doesn’t require a core replacement: What 164 corporate treasurers told us about their banks

From assistants to agents: What Google I/O 2026 means for enterprise leaders

The human bottleneck in AI training: Can expert annotators scale with frontier models?

A technical primer on world models

Physical AI training data: What good pre-training and post-training datasets look like

How to build post-training AI Interfaces at the speed of R&D with Express Interface

From validation to collaboration: Rethinking human-in-the-loop processes in trust and safety automation

Be the first to know