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With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
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With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
Featured
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.
Featured
The tinyML Foundation, Sony Semiconductor, and the City of San José are teaming up to use Machine Learning in improving pedestrian safety
Engineers Wiki. Most Asked Questions.
This article explores TPU vs GPU differences in architecture, performance, energy efficiency, cost, and practical implementation, helping engineers and designers choose the right accelerator for AI workloads today!
TPU vs GPU: Comprehensive Technical Comparison
By Umar Waseem.
14 minutes read.
NPU vs GPU: Understanding the Key Differences and Use Cases
By Ravi Rao.
13 minutes read.
RAG vs Fine-Tuning: Differences, Benefits, and Use Cases Explained
By Ravi Rao.
18 minutes read.
LLM Training: Mastering the Art of Language Model Development
By Ravi Rao.
30 minutes read.
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ORGANIZATIONS. SHAPING THE INDUSTRY.
High Tech Campus Eindhoven
High Tech
High Tech Campus Eindhoven is Europe's smartest square km and has the ultim...
49 Posts
ETH Zurich
University for science and technology
Freedom and individual responsibility, entrepreneurial spirit and open-min...
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TAGGED WITH machine learning
Nakahira received a Young Investigator Award from the Japan Science and Technology Agency for developing a machine that builds autonomous systems.
Princeton researchers have developed a new tool to speed the discovery of advanced materials known as metal organic frameworks, or MOFs.
EPFL roboticists have shown that when a modular robot shares power, sensing, and communication resources among its individual units, it is significantly more resistant to failure than traditional robotic systems, where the breakdown of one element often means a loss of functionality.
MIT researchers' DiffSyn model offers recipes for synthesizing new materials, enabling faster experimentation and a shorter journey from hypothesis to use.
TAGGED WITH motion capture systems
Latest Posts
With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
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.
Article 5 of Bringing Intelligence to the Edge Series: Integrating voice user interface technology into microcontroller units for offline, edge-based voice recognition is set to redefine the landscape of home automation and smart industrial applications.
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 2 of Bringing Intelligence to the Edge Series: Advancements in AI and embedded vision technologies are revolutionizing various industries, enabling real-time decision-making, enhancing security, and facilitating automation in various applications.
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.
