Building destruction during war is a specific form of violence which is particularly harmful to civilians, commonly used to displace populations, and therefore warrants special attention. Yet, data from war-ridden areas are typically scarce, often incomplete and highly contested, when available. The lack of such data from conflict zones severely limits media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, as well as the study of violent conflict in academic research. One approach has been to use remote sensing to identify destruction in satellite images[1]. This approach is gaining momentum as high-resolution imagery is becoming readily available and is updated ever quicker yielding weekly or even daily frequency. At the same time recent methodological advances related to deep learning have provided sophisticated tools to extract data from these images [2, 3, 4, 5].

This includes applications in microscopy and histopathology [1, 2], time-continuous biosignal analysis [3, 4], and, quite prominently, medical image analysis for volumetric imaging data as generated by computed tomography [5, 6], positron emission tomography [7, 8] or magnetic resonance imaging [9, 10, 11]. In the field of medical imaging, recent work has demonstrated a variety of applications for DNNs, such as organ segmentation [12], anomaly detection [13], lesion detection [14], segmentation [15] and assessment [16], providing major advantages and even repeatedly outperforming gold-standard human assessment [17]. A nearby field of similarly growing research interest established with the publications of Kumar et al. and Aerts et al. [18, 19] is,,Radiomics" using traditional machine learning (ML) techniques. Compared to deep learning techniques, traditional ML methods like random forests and support vector machines have a largely transparent decision-making process, which is generally easier to comprehend and/or depict - a clear argument for their preference in clinical practice. Many publications have shown the advantages of DNNs in comparison to traditional machine learning techniques, such as the ability to learn descriptive features from data instead of a complex and expensive handcrafted feature design, as well as an improved classification performance on medical imaging tasks [20, 21], with some architectures being on par with gold-standard human assessment [17]. However, as DNNs learn features from the given data, the semantic of these features is in general not immediately evident. Thus, clinicians understandably approach these methods with a high degree of skepticism.

Find numerous blogs on big data, blockchain, IoT, drones, artificial intelligence, machine learning, deep learning and augmented reality. "Google will fulfill its mission only when its search engine is AI-complete. You guys know what that means? "Deep learning will revolutionize supply chain automation." "The first to fully integrate the following technologies will create a near autonomous supply chain: IoT, Big Data, Blockchain, 3D Printing, Artificial Intelligence, Machine Learning and Deep Learning." "Artificial intelligence is the future and the future is here." "Integration of the following technologies will revolutionize supply chain: IoT, Big Data, Blockchain, 3D Printing, Artificial Intelligence, and Augmented Reality." "Artificial intelligence will disrupt all industries.

Researchers from Cornell University have created an earphone system that can track a wearer's facial expressions even when they're wearing a mask. C-Face can monitor cheek contours and convert the wearer's expression into an emoji. That could allow people to, for instance, convey their emotions during group calls without having to turn on their webcam. "This device is simpler, less obtrusive and more capable than any existing ear-mounted wearable technologies for tracking facial expressions," Cheng Zhang, director of Cornell's SciFi Lab and senior author of a paper on C-Face, said in a statement. "In previous wearable technology aiming to recognize facial expressions, most solutions needed to attach sensors on the face and even with so much instrumentation, they could only recognize a limited set of discrete facial expressions."

In this episode of the Data Exchange I speak with Viral Shah, co-founder and CEO, Julia Computing. Along with his Julia language co-creators, Viral was awarded the 2019 Wilkinson prize, for outstanding contributions in the field of numerical software. I first tweeted about Julia at the beginning of March 2012 after seeing Jeff Bezanson give a talk in Stanford. I've dabbled with it here and there, but have never used it for a major project. Over the past few years, Julia continued to add packages at a steady pace and the package manager is really quite impressive and solid.

Package Name: Small Cap Forecast Recommended Positions: Long Forecast Length: 3 Days (10/4/2020 – 10/8/2020) I Know First Average: 11.18% In this 3 Days forecast for the Small Cap Forecast Package, there were many high performing trades and the algorithm correctly predicted 10 out 10 trades. The highest trade return came from NTZ, at 45.79%. Other notable stocks were NMIH and HIBB with a return of 16.96% and 14.57%. The package itself saw an overall return of 11.18%, providing investors with a 8.24% premium above the S&P 500's return of 2.94% for the same time period.

Trey Research Inc. delivers innovative solutions for manufacturers. They specialize in identifying and solving problems for manufacturers that can run the range from automating away mundane but time-intensive processes to delivering cutting edge approaches that provide new opportunities for their manufacturing clients. Trey Research is looking to provide the next generation experience for connected car manufacturers by enabling them to utilize AI to decide when to pro-actively reach out to the customer thru alerts delivered directly to the car's in-dash information and entertainment head unit. For their PoC, they would like to focus on two maintenance related scenarios. In the first scenario, Trey Research recently instituted new regulations defining what parts are compliant or out of compliance.

Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Every once in while, there comes a library or framework that provides us new insights into the field of Deep Learning, that allows attaining remarkable progress. PyTorch is an AI framework developed by Facebook. It's a Python-based package for serving as a replacement of Numpy to make use of the power of GPU and to provide flexibility as a Deep Learning Development Platform. It is surely a framework worth learning.

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Science fiction is becoming reality as increasingly intelligent machines are gradually emerging -- ones that not only specialize in things like chess, but that can also carry out higher-level reasoning, or even answer deep philosophical questions. For the past few decades, experts have been collectively bending their efforts toward the creation of such a human-like artificial intelligence, or a so-called "strong" or artificial general intelligence (AGI), which can learn to perform a wide range of tasks as easily as a human might. But while current AI development may take some inspiration from the neuroscience of the human brain, is it actually appropriate to compare the way AI processes information with the way humans do it? The answer to that question depends on how experiments are set up, and how AI models are structured and trained, according to new research from a team of German researchers from the University of Tübingen and other research institutes. The team's study suggests that because of the differences between the way AI and humans arrive at such decisions, any generalizations from such a comparison may not be completely reliable, especially if machines are used to automate critical tasks.