From IoT and robotics to industrial automation and smart devices, AI is fundamentally changing how machines operate. But one of the biggest hurdles to widespread adoption has always been the ...

As the move to physical AI speeds up, how do you make sure these capabilities become broadly accessible and not limited to ...

Artificial intelligence (AI) and machine learning (ML) have undergone significant transformations over the past decade. The revolution of convolutional neural networks (CNNs) and recurrent neural ...

Edge AI is a form of artificial intelligence that in part runs on local hardware rather than in a central data center or on cloud servers. It’s part of the broader paradigm of edge computing, in which ...

The rapid advancements in AI have brought powerful large language models (LLMs) to the forefront. However, most high-performing models are massive, compute-heavy, and require cloud-based inference, ...

Overview: Edge AI devices prioritize local inference to ensure user data remains stored on the physical hardware instead of being transmitted to external server ...

AI's future in the Global South hinges on overcoming connectivity, cost, and compute barriers. Edge AI, running models ...

You can think of Physical AI as the fusion of today’s generative AI and other foundation models with sensors, actuators and control systems, all operating within safety constraints and some ...

‘Hey Google’ find me a suitable keyword spotting (KWS) model for edge devices. While voice control is essential for modern interfaces like Alexa, Siri, and Hey Google, building KWS models on edge ...

Large language models (LLMs) use vast amounts of data and computing power to create answers to queries that look and sometimes even feel “human”. LLMs can also generate music, images or video, write ...