New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
To develop and validate an automated morphometric analysis framework for the quantitative analysis of geometric hip joint parameters in MR images from the German National Cohort (GNC) study. A secondary analysis on 40 participants (mean age, 51 years; age range, 30–67 years; 25 women) from the prospective GNC MRI study (2015–2016) was performed. Based on a proton density–weighted three-dimensional fast spin-echo sequence, a morphometric analysis approach was developed, including deep learning based landmark localization, bone segmentation of the femora and pelvis, and a shape model for annotation transfer. The centrum-collum-diaphyseal, center-edge (CE), three alpha angles, head-neck offset (HNO), and HNO ratio along with the acetabular depth, inclination, and anteversion were derived. Quantitative validation was provided by comparison with average manual assessments of radiologists in a cross-validation format. High agreement in mean Dice similarity coefficients was achieved (average of 97.52% 0.46 [standard deviation]). The subsequent morphometric analysis produced results with low mean MAD values, with the highest values of 3.34 (alpha 03:00 o'clock position) and 0.87 mm (HNO) and ICC values ranging between 0.288 (HNO ratio) and 0.858 (CE) compared with manual assessments. These values were in line with interreader agreements, which at most had MAD values of 4.02 (alpha 12:00 o'clock position) and 1.07 mm (HNO) and ICC values ranging between 0.218 (HNO ratio) and 0.777 (CE). Automatic extraction of geometric hip parameters from MRI is feasible using a morphometric analysis approach with deep learning.
SAN DIEGO, August 03, 2021--(BUSINESS WIRE)--LumenVox, a leading provider of speech and voice technology, today announced its next-generation Automatic Speech Recognition (ASR) engine with transcription. The new engine, built on a foundation of artificial intelligence (AI) and deep machine learning (ML), outpaces its competition in delivering the most accurate speech-enabled customer experiences. The new LumenVox ASR engine stands apart from the rest with its end-to-end Deep Neural Network (DNN) architecture and its state-of-the-art speech recognition processing capabilities. The new ASR engine not only accelerates the ability to add new languages and dialects but also provides a modern toolset to expand the language model to serve a more diverse base of users. "New demands have redefined the very meaning of Automated Speech Recognition," said Dan Miller, lead analyst at Opus Research.
What is a neural network? As in the structure of a human brain, neurons are interconnected to help make decisions; neural networks are inspired by the neurons, which helps a machine make different decisions or predictions. Neural networks are the web of interconnected nodes where each node has the responsibility of simple calculations. A combination of calculation helps in bringing desired results. In today's machine learning and deep learning scenario, neural networks are among the most important fields of study growing in readiness.
All the sessions from Transform 2021 are available on-demand now. Spell today unveiled an operations platform that provides the tooling needed to train AI models based on deep learning algorithms. The platforms currently employed to train AI models are optimized for machine learning algorithms. AI models based on deep learning algorithms require their own deep learning operations (DLOps) platform, Spell head of marketing Tim Negris told VentureBeat. The Spell platform automates the entire deep learning workflow using tools the company developed in the course of helping organizations build and train AI models for computer vision and speech recognition applications that require deep learning algorithms.
If you are new to the field of deep learning, at some point in time you may have heard of the topic of image augmentation. This article will discuss what image augmentation is and implement it in three different python libraries i.e Keras, PyTorch, and augmentation (specifically for image augmentation). So the first question arises what is image augmentation or in general data augmentation. Augmentation is the action or process of making or becoming greater in size or amount. In deep learning, deep networks require a large amount of training data to generalize well and achieve good accuracy. But in some cases, image data is not large enough.
I have been asked this question a few times so I have decided to write this post about the current services of Livepeer and the future implementations Livepeer could bring to the decentralized world. This may be helpful for new comers that want to learn about Livepeer and have an idea of where Livepeer could take us in the future. But first we need to cover some important topics of where we are right now. First, let's look at the difference between Ethereum Mining and Video Mining. Livepeer has many possible services it could provide.
Using artificial intelligence (AI) in the construction industry typically involves machine learning networks crunching data, data collection computer vision sensors, and the occasional physical robotics doing simple and repeatable manual tasks, such as laying bricks. While it seems construction involving AI is still in its infancy stage, there are still things to consider about the proper places where AI can find its place in the construction industry. Ultimately, AI is being used to make construction processes and projects more efficient and safe for human workers. There is some concern that AI will be removing human jobs, but so far that's not a concern that we've seen materialize. Like other industries involving artificial intelligence, AI is following the path of augmenting human tasks in order to speed up projects and make them safer.
The field of Computer Vision has for years been dominated by Convolutional Neural Networks (CNNs). Through the use of filters, these networks are able to generate simplified versions of the input image by creating feature maps that highlight the most relevant parts. These features are then used by a multi-layer perceptron to perform the desired classification. But recently this field has been incredibly revolutionized by the architecture of Vision Transformers (ViT), which through the mechanism of self-attention has proven to obtain excellent results on many tasks. If this in-depth educational content is useful for you, subscribe to our AI research mailing list to be alerted when we release new material.
All the sessions from Transform 2021 are available on-demand now. This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. One of the key challenges of deep reinforcement learning models -- the kind of AI systems that have mastered Go, StarCraft 2, and other games -- is their inability to generalize their capabilities beyond their training domain. This limit makes it very hard to apply these systems to real-world settings, where situations are much more complicated and unpredictable than the environments where AI models are trained. But scientists at AI research lab DeepMind claim to have taken the "first steps to train an agent capable of playing many different games without needing human interaction data," according to a blog post about their new "open-ended learning" initiative. Their new project includes a 3D environment with realistic dynamics and deep reinforcement learning agents that can learn to solve a wide range of challenges.
Get started quickly with the basics of MATLAB. Get started quickly with the basics of Simulink. Get started quickly using deep learning methods to perform image recognition. Master the basics of creating intelligent controllers that learn from experience. An interactive introduction to signal processing methods for spectral analysis.