5 Things to Know About the US Drone Market - Drone Industry Insights

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The United States is home to the largest drone market in the world and has been for the past few years. The US will remain in the lead in the forthcoming years, but the Chinese drone market is slowly closing that gap. While in 2024 the US will still be the largest drone market in the world, the Chinese drone market will be catching up as it will grow faster. Even though the US market won't grow quite as quickly as some others (China, and India), it will still be one of the fastest growing drone markets in the world. By 2024 it is expected to be almost three times the size it was in 2018.



How CMOs Succeed with AI-Powered CX

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Thankfully, AI, powered by Machine Learning and evaluated and fine-tuned by humans, has come a long way in the last decade.


What are some of the Best AI Engines?

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Thinking that AI won't take over the current technologies can be stated as an oblivious truth. We all know that it is bound to happen and there will be a lot of things that will be capable of making decisions on their own. Although, the application of today itself is intelligent enough to give great customer service. Machine Learning now is being applied to various niches these days. Therefore, if you wish to know about the Best AI engines then check out our article by clicking here.


If a robotic hand solves a Rubik's Cube, does it prove something?

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Last week, on the third floor of a small building in San Francisco's Mission District, a woman scrambled the tiles of a Rubik's Cube and placed it in the palm of a robotic hand. The hand began to move, gingerly spinning the tiles with its thumb and four long fingers. Each movement was small, slow and unsteady. But soon, the colors started to align. Four minutes later, with one more twist, it unscrambled the last few tiles, and a cheer went up from a long line of researchers watching nearby.


Sharmila Majumdar, PhD Receives Important NIH HEAL Initiative Grant for The Back-Pain Consortium (BACPAC) Research Program to Address Chronic Low Back Pain

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According to the Centers for Disease Control and Prevention (CDC), an estimated 50 million adults in the U.S. suffered from chronic pain in 2016, and according to the Substance Abuse and Mental Health Services Administration (SAMHSA), an estimated 10.3 million people in the U.S. ages 12 and older misused opioids in 2018. As such, the National Institutes of Health (NIH) have announced the awarding of $945 million in research grants to tackle the national opioid crisis through NIH HEAL Initiative (Helping to End Addiction Long-term Initiative). The UC San Francisco Department of Radiology and Biomedical Imaging is pleased to announce that one such project is the Back Pain Consortium (BACPAC) Research Program of which Sharmila Majumdar, PhD, vice chair for Research, is a part of. At this time, chronic low back pain is one of the most common forms of chronic pain in adults, and current treatments are ineffective, leading to increased use of opioids. This research will also lay the foundation for NIH funded research at the newly established Center for Intelligent Imaging, using artificial intelligence fueled algorithms for fast image acquisition, data analysis, quantitative sensory assessments, brain imaging, and biomechanical evaluation of the spine.


Same same but different: a web-based deep learning application for the histopathologic distinction of cortical malformations

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We trained a convolutional neural network (CNN) to classify H.E. stained microscopic images of focal cortical dysplasia type IIb (FCD IIb) and cortical tuber of tuberous sclerosis complex (TSC). Both entities are distinct subtypes of human malformations of cortical development that share histopathological features consisting of neuronal dyslamination with dysmorphic neurons and balloon cells. The microscopic review of routine stainings of such surgical specimens remains challenging. A digital processing pipeline was developed for a series of 56 FCD IIb and TSC cases to obtain 4000 regions of interest and 200.000 sub-samples with different zoom and rotation angles to train a CNN. Our best performing network achieved 91% accuracy and 0.88 AUCROC (area under the receiver operating characteristic curve) on a hold-out test-set.


Public Preview Cloud-Based Enterprise RPA Platform UiPath

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"As adoption of RPA takes hold across enterprises, customers are pushing for new capabilities to broaden out the usefulness of software robots to support all workers in all roles. Customers are beginning to look beyond pure automation to also use software robots as digital assistants. Making it easier to plan for and build robots, create human-machine interfaces to interact with their robots, leverage AI to for automation and to help with decision-making, and measure performance of the emerging digital workforce are all necessary to satisfy the expansive vision of RPA."


Free Computer Training Seminars -- KNOWLEDGE SYSTEMS INSTITUTE

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KSI offers free seminars and classes on trending IT topics. Please complete the form below if you wish to receive emails about our seminars and further information.


AI Researchers' Open-Source Model Explanation Toolkit AllenNLP Interpret

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Researchers from the Allen Institute for AI and University of California, Irvine, have released AllenNLP Interpret, a toolkit for explaining the results from natural-language processing (NLP) models. The extensible toolkit includes several built-in methods for interpretation and visualization components, as well as examples using AllenNLP Interpret to explain the results of state-of-the art NLP models including BERT and RoBERTa. In a paper published on arXiv, the research team described the toolkit in more detail. AllenNLP Interpret uses two gradient-based interpretation methods: saliency maps, which determine how much each word or "token" in the input sentence contributes to the model's prediction, and adversarial attacks, which try to remove or change words in the input while still maintaining the same prediction from the model. These techniques are implemented for a variety of NLP tasks and model architectures.