Large language models (LLMs) are just too useful to be confined in the cloud. With thousands of applications being developed around their base models, they're more than just an online chatbot.

There’s a shift happening in how intelligent systems are designed and deployed. AI models are getting more efficient. Devices are getting smaller, faster, and more power-aware. Now, real-time decision-making is no longer just a cloud problem.

Talk to any engineer working on healthcare AI and you’ll hear a mix of excitement and caution. There’s no question that deep learning has found its way into the field. It’s not always in the places that make headlines; but instead, it’s quietly becoming part of the clinical workflow, embedded in tools that doctors use every day.

Did you know? The first paper to lay the foundation of the modern neural network neurons made zero references to papers on the biology of real neurons. It was in the 1940s, long before the concept of “deep learning” entered the mainstream, when the American neurophysiologist Warren McCulloch laid the mathematical groundwork for neural computation. His ideas not only shaped the architecture of artificial neural networks but also influenced how we build and think about intellig

Today—a day before our 6th anniversary—we came together to celebrate the years we’ve progressed as one. On April 17, 2019, CEO and CTO Dongjoo Shin founded Mobilint with a vision where artificial intelligence technology would live inside portable machines, and not just in cloud systems or expensive data centers.

A neural processing unit (NPU) is more than just a new type of AI hardware. Here’s a simplified answer to what it does and why it matters.