Category: robotics/AI – Page 1,566
Understand whatâs happening on your construction sites with effective reality capture data
See the robotic solution in action with Spot and Trimble Buildings!
Amazonâs Jianbo Ye and Arnie Sen explain that
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to adapt to constant changes in customersâ homes, Astro uses a deep-learning model to produce invariant representations of visual data, estimates sensor reliability to guide the fusion of sensor data, and prunes and compresses map representations: https://amzn.to/36qQUZo
Quantifying Human Consciousness With the Help of AI
A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.
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Summary: A new deep learning algorithm is able to quantify arousal and awareness in humans at the same time.
Source: CORDIS
New research supported by the EU-funded HBP SGA3 and DoCMA projects is giving scientists new insight into human consciousness.
Led by Korea University and projectsâ partner University of LiĂšge (Belgium), the research team has developed an explainable consciousness indicator (ECI) to explore different components of consciousness.
Spot robot dog can smell airborne gas or chemical hazards in real-time
Teledyne FLIR Defense has announced the partnership with MFE Inspection Solutions to integrate the FLIR MUVE C360 multi-gas detector on Boston Dynamicsâ Spot robot and commercial unmanned aerial systems (UAS). The integrated solutions will enable remote monitoring of chemical threats in industrial and public safety applications.
The compact multi-gas detector can detect and classify airborne gas or chemical hazards, allowing inspection personnel to perform their job more safely and efficiently with integrated remote sensing capabilities from both the air and ground.
MUVE C360 is designed to operate on Boston DynamicsâSpot mobile robot, which can autonomously inspect dangerous, inaccessible, or remote environments. It is also compatible with common commercial UAS systems, which allow operators to fly the C360 into a scene to perform hazard assessments in real-time.
A pan-tissue DNA-methylation epigenetic clock based on deep learning
Next, we aimed to determine whether the model type, i.e., a linear regression vs. a neural network, would significantly impact the performance. We, therefore, compared the aforementioned linear models with the neural network AltumAge using the same set of features. AltumAge outperformed the respective linear model with Horvathâs 353 CpG sites (MAE = 2.425 vs. 3.011, MSE = 32.732 vs. 46.867) and ElasticNet-selected 903 CpG sites (MAE = 2.302 vs. 2.621, MSE = 30.455 vs. 39.198). This result shows that AltumAge outperforms linear models given the same training data and set of features.
Lastly, to compare the effect of the different sets of CpG sites, we trained AltumAge with all 20,318 CpG sites available and compared the results from the smaller sets of CpG sites obtained above. There is a gradual improvement in performance for AltumAge by expanding the feature set from Horvathâs 353 sites (MAE = 2.425, MSE = 32.732) to 903 ElasticNet-selected CpG sites (MAE = 2.302, MSE = 30.455) to all 20,318 CpG sites (MAE = 2.153, MSE = 29.486). This result suggests that the expanded feature set helps improve the performance, likely because relevant information in the epigenome is not entirely captured by the CpG sites selected by an ElasticNet model.
Overall, these results indicate that even though more data samples lower the prediction error, AltumAgeâs performance improvement is greater than the increased data effect. Indeed, the lower error of AltumAge when compared to the ElaticNet is robust to other data splits (Alpaydinâs Combined 5x2cv F test p-value = 9.71eâ5).