Designed as a necklace-style wearable in a 3D-printed case, SpeeChin is capable of recognizing 54 English and 44 Chinese voice commands — even if the wearer never says anything out loud.
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In this episode, we talk about how AI is being leveraged for designing better batteries and training microswimmers.
The in-sensor adaptation strategy widens the range for image perception under different illumination conditions to simplify the complexity of hardware and algorithms.
Sounds provide important information about how well a machine is running. ETH researchers have now developed a new machine learning method that automatically detects whether a machine is "healthy" or requires maintenance.
4 minutes read
Designed to address the OOD data false prediction by adaptively synthesizing virtual outliers that can maintain the model’s decision boundary during training.
Designed to address the exploding computational demands of deep neural networks, physical neural networks (PNNs) branch out from electronics into optics and even mechanics to boost performance and efficiency.
Designed for use in the human body for everything from drug delivery to less-invasive biopsies, these tiny microrobots operate under the control of a deep-learning system trained with no modelling or prior environmental knowledge.
Taking its cues from the haunting electronic instrument, the MoCapaci project sews a theremin into a blazer to feed a deep-learning system with data for accurate gesture sensing and activity recognition.
Over the last decade, deep neural networks have emerged as the solution to several AI complex applications from speech recognition and object detection to autonomous vehicular systems.
Designed to address the risk to front-line staff from COVID-19, this autonomous swab-sampling robot is designed to take the human element out of sample gathering.
Using a now public-access dataset, a research team has created a robotics control system which can generalize to unseen related tasks — allowing robots to interpret natural-language commands and video demonstrations.
With research suggesting feral pigeons do as much as $1.1 billion in damage in the US alone, a project which pairs a commercial drone with machine learning to scare them away — without injury — could prove key to their control.
Attention: Don’t presume human or model-generated labels to be the growth truth for machine learning deployment in autonomous vehicular systems.
The proposed AI system has an accuracy between 70% and 80% that can be employed as a second referral mechanism for cardiovascular examination.
5 Questions to Ask Before Getting Started with Data Annotation: What is data annotation? Why is it essential? What are common label types for models? How do AI-powered data annotation tools help with the computer vision labeling process? What’s the best way to get started?
4 minutes read
