"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
AI technology has grown in leaps and bounds over the past few years, and one of its main implementations is internet search engines. From correcting misspelled words to predicting what a user wants to search for, AI has made searching the web so much easier. Google is the leader when it comes to the sheer volume of search queries that it handles. Naturally, it has implemented an AI-based algorithm that helps improve your search experience. Exactly how does AI do this?
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.
AI (Artificial Intelligence) is a technology that feels like it came out of a comic book. What we once considered to be the future, is here now. AI as we know it today has footprints that date back to the classic philosophers, who attempted to explain human thinking as a symbolic system. However, the term AI was formally coined in the year 1956, at a conference at Dartmouth College in Hanover, New Hampshire. In a report by PWC, it is stated that AI-enabled activities could raise the global GDP to 14percent by the end of 2030, which sums up to $15.7 Trillion. This is evidence of the potential that AI software development has today and in the future to come.
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.
Designers looking to implement artificial intelligence (AI) algorithms on inference processors at the edge are under constant pressure to lower power consumption and development time, even as processing demands increase. Field programmable gate arrays (FPGAs) offer a particularly effective combination of speed and power efficiency for implementing the neural network (NN) inference engines required for edge AI. For developers unfamiliar with FPGAs, however, conventional FPGA development methods can seem complex, often causing developers to turn to less optimal solutions. This article describes a simpler approach from Microchip Technology that lets developers bypass traditional FPGA development to create trained NNs using FPGAs and a software development kit (SDK), or use an FPGA-based video kit to move immediately into smart embedded vision application development. Edge computing brings a number of benefits to Internet of Things (IoT) applications in segments as varied as industrial automation, security systems, smart homes, and more.
The Mac's mouse, the iPod's click wheel, the iPhone's multitouch display, and the Apple Watch's digital crown are all part of Apple lore in which a new device class mandated a new user interface. But there's a significant exception to that established pattern: Siri. The voice agent emerged as a way to control some of the iPhone's features but was never a way to completely control it the way Alexa served as the Echo's main user interface. Rather, it could retrieve bits of information and complete simple tasks online. Now, an app called Natural seeks to go beyond what agents such as Siri and Alexa can achieve in terms of transactions while remaining wed to the smartphone's -- or any connected device's -- touchscreen.
Mastering Machine Learning With Python In Six Steps About this Book Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered.