If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This report presents a hands-on introduction to natural language processing (NLP) of radiology reports with deep neural networks in Google Colaboratory (Colab) to introduce readers to the rapidly evolving field of NLP. The implementation of the Google Colab notebook was designed with code hidden to facilitate learning for noncoders (ie, individuals with little or no computer programming experience). The data used for this module are the corpus of radiology reports from the Indiana University chest x-ray collection available from the National Library of Medicine's Open-I service. The module guides learners through the process of exploring the data, splitting the data for model training and testing, preparing the data for NLP analysis, and training a deep NLP model to classify the reports as normal or abnormal. Concepts in NLP, such as tokenization, numericalization, language modeling, and word embeddings, are demonstrated in the module.
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner In deep learning, the'deep' talks more about the architecture and not about the level of understanding that the algorithms are capable of producing. Take the case of a video game. A deep learning algorithm can be trained to play Mortal Kombat really well and will even be able to defeat humans once the algorithm becomes very proficient. Change the game to Tekken and the neural network will need to be trained all over again. This is because it does not understand the context.
Using the blend of technologies similar to Artificial Intelligence like Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, etc, These decision support systems outshines its ability to analyze patterns, simplify processes by examining large amounts of volumetric data, and spot business opportunities. With the help of computerized models using self-learning technologies like data mining, pattern recognition, and natural language processing, Cognitive computing synthesizes the data fed to machine learning algorithms from different information sources to suggest the best possible answers. Pitching on the grounds of learning, reasoning, and self-correction and assisting humans to make smarter decisions, Cognitive Computing applications include speech recognition, sentiment analysis, face detection, risk assessment, and fraud detection.
To develop and evaluate deep learning models for the detection and semiquantitative analysis of cardiomegaly, pneumothorax, and pleural effusion on chest radiographs. In this retrospective study, models were trained for lesion detection or for lung segmentation. The first dataset for lesion detection consisted of 2838 chest radiographs from 2638 patients (obtained between November 2018 and January 2020) containing findings positive for cardiomegaly, pneumothorax, and pleural effusion that were used in developing Mask region-based convolutional neural networks plus Point-based Rendering models. Separate detection models were trained for each disease. The second dataset was from two public datasets, which included 704 chest radiographs for training and testing a U-Net for lung segmentation.
Astronomers have designed and trained a computer program that can classify tens of thousands of galaxies in just a few seconds, a task that usually takes months to accomplish. In research published today, astrophysicists from Australia have used machine learning to speed up a process that is often done manually by astronomers and citizen scientists around the world. "Galaxies come in different shapes and sizes," said lead author Mitchell Cavanagh, a Ph.D. candidate based at the University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR). "Classifying the shapes of galaxies is an important step in understanding their formation and evolution, and can even shed light on the nature of the Universe itself." Cavanagh said that with larger surveys of the sky happening all the time, astronomers are collecting too many galaxies to look at and classify on their own.
As the space race heats up, debris has become a burning issue. Since the beginning of the space age in the 1950s, thousands of satellites and rockets have been sent to space and are marooned there. The Union of Concerned Scientists Satellite database has listed more than 4,084 operational satellites currently orbiting the Earth. In 2010, this number was less than a thousand. In the distant future, this problem can extend to the lunar surface and the asteroid belt (the current count stands at 34,000 pieces of space junk bigger than 10 centimetres in size and millions of smaller pieces).
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