The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Radiographs were grouped according to age and into'dislocation' (dislocation and subluxation) and'non-dislocation' (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis.

A research paper is mcq?

The news about artificial intelligence is mostly dominated by sensational stories such as the ominous threat of deepfakes, deep learning algorithms that create fake blogs, AI bots that create their own language, and generative adversarial networks that create realistic portraits of non-existent people. But the practical use of AI algorithms is much farther behind than the hype caused by the media. From peer-reviewed breakthrough research presented at mainstream AI conferences to PR-style videos created by large tech companies and well-funded research labs, only a trickle of the innovation we see in the field makes it into real business processes and applications. And the organizations that are putting AI to good use are those who understand the powers and limits of today's technology and master the challenges of integrating it into their processes and solutions. "AI does offer a lot of business value, but much of that value isn't terribly sexy or visible. Products and processes will be made somewhat better and easier to use. Decisions will be better informed. We'll continue -- and perhaps even accelerate a bit -- the amazing progress that we've seen over the last couple of decades in data and analytics. But as all of the early adopters have discovered, it's still difficult to create systems that think and communicate like humans -- even in narrow domains," academic and business author Thomas H. Davenport writes in his book The AI Advantage: How to Put the Artificial Intelligence Revolution to Work.

The series of data points plotted against time is known as time series. It is a de-facto analysis technique used in market evaluation and in weather forecast. It is an exciting topic to study as it somehow tends to predict the future, which we are always interested in. We can make forecast of tomorrow's weather by observing the weather of past few days. If the weather was sunny for last 4–5 days then there is high chance for weather to be sunny tomorrow.

Top row: benchmark virtual monoenergetic (VM) images from dual-energy CT projection data reconstructed at 80 and 110 keV, respectively. Bottom row: corresponding VM images produced at the same energies from only 140-kVp images, using a deep learning approach. Conventional clinical CT scans generate a spectrally integrated attenuation image that shows tissue morphology, but does not directly provide any information regarding tissue composition. Dual-energy CT (DECT) systems, which acquire two spectrally distinct datasets, can reconstruct virtual monoenergetic (VM) and material-specific images that provide information about tissue composition. Compared with conventional CT, however, DECT is more expensive and complex, and often requires an increased radiation dose.

Researchers from Texas Tech University developed a deep-learning algorithm called a convolutional neural network able to distinguish between the …

Making code run fast on GPUs requires a very different approach to making code run fast on CPUs because the hardware architecture is fundamentally different. If you come from a background of efficient coding on CPU then you'll have to adjust some assumptions about what patterns are best. Machine learning engineers of all kinds should care about squeezing performance from their models and hardware -- not just for production purposes, but also for research and training. In research as in development, a fast iteration loop leads to faster improvement. This article is a practical deep dive into making a specific deep learning model (Nvidia's SSD300) run fast on a powerful GPU server, but the general principles apply to all GPU programming.

Artificial Intelligence (AI) is changing the way healthcare industry functions. It is helping doctors diagnose patients more accurately. AI makes predictions about patients' future health and recommends better treatment. Henceforth, healthcare organizations of all sizes, types, and specialties are becoming increasingly interested in employing AI for better patient care and improved efficiency. Medicine is one of the fastest-growing and important application areas with unique challenges.

You are free to share this article under the Attribution 4.0 International license. There are far more trees in the West African Sahara Desert than you might expect, according to a study that combined artificial intelligence and detailed satellite imagery. Researchers counted over 1.8 billion trees and shrubs in the 1.3 million square kilometer (501,933 square miles) area that covers the western-most portion of the Sahara Desert, the Sahel, and what are known as sub-humid zones of West Africa. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert, because up until now, most people thought that virtually none existed," says Martin Brandt, professor in the geosciences and natural resource management department at the University of Copenhagen and lead author of the study in Nature. "We counted hundreds of millions of trees in the desert alone. Doing so wouldn't have been possible without this technology. Indeed, I think it marks the beginning of a new scientific era."

BERT (Bidirectional Encoder Representations from Transformers) is a research paper published by Google AI language. Unlike previous versions of NLP architectures, BERT is conceptually simple and empirically powerful. BERT has a benefit over another standard LM because it applies deep bidirectional context training of the sequence meaning it considers both left and right context while training whereas other LM model such as OpenAI GPT is unidirectional, every token can only attend to previous tokens in attention layers. Such restrictions are suboptimal for sentence-level tasks (paraphrasing) or token level tasks (named entity recognition, question-answering) where it is crucial to incorporate context from both directions. In earlier versions of LM such as Glove, we have fixed embeddings of the words, for example, for the word "right" the embedding is the same irrespective of its context in the sentence.