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.
Be the first to know.
Get our edge ai weekly email digest.
Tagged with
Edge AI
ORGANIZATIONS. SHAPING THE INDUSTRY.
Berkeley Artificial Intelligence Research
Research
The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC...
View more
Latest Posts
Seamless and intuitive user experiences using speech-recognition technology are enabling us to interact with a device simply through voice commands.
Edge AI platforms can assist engineers to monitor battery health systems.
Article #3 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision system that detects and localizes the surface cracks in concrete structures for predictive maintenance.
16 minutes read
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 6 of Bringing Intelligence to the Edge Series: The utilization of edge AI facilitates advanced system optimization, predictive maintenance, and improved anomaly detection, greatly advancing technological capabilities across varied fields.
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.
Article 4 of Bringing Intelligence to the Edge Series: AI is proving to be a more precise and time-efficient tool in processing the big data crunch by recognizing patterns and noticing inconsistencies in real-time.
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 integration and impact of microcontrollers and Edge AI across various industries.
Article #2 of Spotlight on Innovations in Edge Computing and Machine Learning: Edge AI techniques such as Keyword Spotting can turn an ordinary device into a smart appliance.
13 minutes read
