With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
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High Tech Campus Eindhoven
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Article #4 of Spotlight on Innovations in Edge Computing and Machine Learning: Discover the integration of TinyML and wearable tech as we delve into a project that detects falls in real-time, potentially saving lives in our aging population.
The tinyML Foundation, Sony Semiconductor, and the City of San José are teaming up to use Machine Learning in improving pedestrian safety
Edge AI platforms can assist engineers to monitor battery health systems.
From sleep monitoring wearables to face detection models, tinyML is making a big impact on the way we can gather and apply data.
Highlights from the 3rd Annual tinyML EMEA Innovation Forum include exploring hardware developments, algorithm optimization, and deploying MLOps tools.
In both analytics and machine learning (ML), the value of data cannot be overstated. Understanding its importance is essential for unlocking its full potential and driving informed decision-making, enhancing business processes, and exploring new opportunities across various industry sectors.
Understanding What is Unsupervised Learning, the Mechanisms, Types, and Applications of Various Algorithms and Challenges it presents in Machine Learning
In this episode, we discuss a novel new approach for increasing the field of view of autonomous systems by leveraging reflections of blindspots on reflective bodies.
13 minutes read
Article 3 of Bringing Intelligence to the Edge Series: Balancing the critical metrics of accuracy, power consumption, latency, and memory requirements is key to unlocking the potential of Tiny Machine Learning (TinyML) in low-power microcontrollers and edge computing.
Article 1 of Bringing Intelligence to the Edge Series: With the introduction of AI, IoT devices can become more intelligent and less reliant on external systems— but not without trade-offs in performance and cost. Understanding how to make that decision is key.
Introducing the Bringing Intelligence to the Edge Series: Exploring how Artificial Intelligence is moving to embedded systems, transforming technology across various applications.
Exploring the key concepts related to Unsupervised vs Supervised Learning, understanding the fundamental principles, major algorithms and their real-world applications, and practical distinctions between supervised and unsupervised learning.
Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making
Gain a comprehensive understanding of Edge AI by exploring the fundamental concepts, hardware components, software tools, and real-world applications.
