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) …
At a time when India is trying to rekindle productivity and growth, AI promises to fill the gap. AI can boost profitability and transform businesses across sectors through systems that can learn, adapt and evolve with changing times. Such systems are increasingly important in a post-pandemic world where scalable AI solutions may be able to help organizations be prepared even during unprecedented situations. As organisations are working hard to re-architect themselves by changing their business models and technology architecture to survive in the pandemic world, it is time for them to invest in scalable AI solutions to achieve their goals faster. At the same time, technologists and businesses across the world have to advocate for the responsible use of AI.
In a nutshell, proteins are linear chains of multiple amino acids, each of which consists in a constant unit of 4 non-hydrogen atoms plus a sidechain of variable size, ranging from none to around 20 atoms. The amino acids are connected through the constant unit, called backbone, to form a polypeptide that does not remain random but rather acquires one or more arrangements in space. That is, they fold into 3D structures. What exact structure a protein will adopt in 3D depends essentially on the identity of the amino acid sidechains, i.e. its amino acid sequence. Very briefly and simplifying definitions that are quite more complex, amino acid sequences are encoded by genes; the collection of genes of an organism is its genome; and the collection of proteins encoded in a genome is the proteome. To be more precise, and this will be important later, the polypeptide actually can fold into multiple substructures, each of which is called a domain.
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.
The graph represents a network of 1,283 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 30 July 2021 at 10:25 UTC. The requested start date was Friday, 30 July 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 10-hour, 29-minute period from Tuesday, 27 July 2021 at 13:30 UTC to Friday, 30 July 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
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.
Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.
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.
As we are leveraging data for making significant decisions that affect individual lives in domains such as health care, justice, finance, education, marketing, and employment, it is important to ensure the safe, ethical, and responsible use of AI. In collaboration with the Aether Committee and its working groups, Microsoft is bringing the latest research in responsible AI to Azure: these new responsible ML capabilities in Azure Machine Learning and our open source toolkits, empower data scientists and developers to understand machine learning models, protect people and their data, and control the end-to-end machine learning process. In 2015, Claire Cain Miller wrote on The New York Times that there was a widespread belief that software and algorithms that rely on data were objective. Five years later, we know for sure that AI is not free of human influence. Data is created, stored, and processed by people, machine learning algorithms are written and maintained by people, and AI applications simply reflect people's attitudes and behavior.
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.