Why Personalization Is a Training Problem for Long-Horizon AI Agents
Personalized AI agents need more than prompts and memory. See how training data and human feedback enable long-horizon personalization.
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Trends, terminology, questions, and more — explore insights that shape tomorrow's AI.
Beyond the Caption: What Building Our Own Visual-Logic Benchmark Taught Us About Today’s VLMs
See what Innodata’s visual reasoning benchmark reveals about where today’s VLMs succeed, fail, and misread visual details.
What Robots Eat: There’s No Such Thing as a Free Lunch When You’re Training Robots
Explore the data robots need to learn, from egocentric video and motion capture to teleoperation, retargeting, and safety evaluation.
Six Questions to Ask Before Deploying an AI Agent into an Enterprise Workflow
Explore six questions for aligning agents with enterprise context, controls, systems, evaluation, and oversight.
What’s in a Benchmark? Rethinking “Thinking” in Long Context Conversations
Explore Innodata’s LCCI Benchmark for long-context LLM evaluation across multi-turn conversations, multimodal inputs, and real-world model failures.
Cultural Alignment of LLMs Is More Than Just Trivia. It’s Applied Understanding.
Innodata’s ICAB benchmark evaluates how well LLMs understand implicit cultural context across languages, locales, and multimodal tasks.
The Hidden Problem with AI Optimization and Sampling
Why AI systems favor average results over the best ones, and how robust reinforcement learning improves real-world performance.
AI Can Count Aircraft from Space. Understanding Them Requires a World Model
How kinematics-based motion analysis improves data labeling, automated quality control, and computer vision models for fitness and robotics.
Physical AI Starts With Data, Not Models
Physical AI starts with data, not models. Learn how ontologies and context drive smarter, real-world AI systems.