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) …
Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death. Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient's deterioration, so choosing the optimal therapy can be a difficult task. For instance, in the early hours of severe sepsis, administering too much fluid intravenously can increase a patient's risk of death. To help clinicians avoid remedies that may potentially contribute to a patient's death, researchers at MIT and elsewhere have developed a machine-learning model that could be used to identify treatments that pose a higher risk than other options.
With interpretability becoming an increasingly important requirement for machine learning projects, there's a growing need to communicate the complex outputs of model interpretation techniques to non-technical stakeholders. SHAP (SHapley Additive exPlanations) is arguably the most powerful method for explaining how machine learning models make predictions, but the results from SHAP analyses can be non-intuitive to those unfamiliar with the approach. For those who wish to dig deeper on certain topics, links to useful resources are provided. Code for reproducing this analysis can be found on GitHub. SHAP is a method that explains how individual predictions are made by a machine learning model.
Model training strategies are described for use in scenarios in which there are limited datasets available. Models developed with data-driven approaches have great potential to shape the future practice of radiology. A wide variety of strategies are available to enhance the performance of models in data-limited settings, including transfer learning, data augmentation, semisupervised training techniques, efficient annotation strategies, federated learning, few-shot learning, and different neural network architectures. Research in diagnostic radiology decision support has progressed rapidly because of the availability of large datasets, powerful machine learning (ML) techniques, and computers to efficiently run ML techniques (1–3). Advances in medical image analysis research include multiple systems that aim to help radiologists detect disease (4–7), identify disease progression (8), localize abnormalities (9), automate time-consuming tasks, and improve the radiology workflow. The performance of deep learning–based algorithms depends on the availability of large-scale annotated data (3,10,11). A large dataset with diverse, high-quality images curated from multiple institutions and different geographic areas is preferable to ensure the generalizability of a model for clinical use (12). However, curating large datasets is challenging because of their volume, limited radiologist availability, and tedious annotation processes. It is particularly challenging to curate data for rare diseases. Additionally, many complexities are introduced in the data de-identification process to comply with patient privacy rules, institutional review board requirements, and local ethical committee protocols (13). If training data are limited, deep learning–based models may suffer from overfitting, which results in poor generalizability. Several reviews have described deep learning–based frameworks for medical imaging (2,3,14).
In 2015, it was found out that the algorithm used for hiring employees for Amazon was biased. It was trained on the number of resumes submitted over the past ten years, and due to the existing gender gap in the industry, as the number of male candidates was higher than female candidates, the algorithm also favored males. In a similar case in the UK, a gymnasium wrongly assumed that a woman was a man, just because she was a doctor. The algorithm used titles of the members to allot a fitting room. The algorithm had accidentally learned that the title of Doctor is given to men, which resulted in this error.
Machine Learning is the new hype around the world. And why won't it be as it has been enhancing almost all the aspects of our lives. However, when one starts to learn about its magic, he gets overwhelmed by its vast information and huge mathematical calculations. So, I have developed a platform where users can train their own deep learning model without writing any line of code. Here is the link to the website and Github repository.
This is a part-1 of the series of tutorials that I am writing on unsupervised/self-supervised learning using deep neural networks. In this tutorial, the focus would be on latent space implementation using autoencoder architecture and its visualization using t-SNE embedding. Before we delve into code, lets define some important concepts which we will encounter throughout the tutorial. The real-world data is often redundant with high dimensions. This poses challenges not only for computational efficiency but also hinders the modelling of the representation.
WikiSQL is one of the most popular benchmarks in semantic parsing. It is a supervised text-to-SQL dataset, beautifully hand-annotated by Amazon Mechanical Turk. Some of the early works on WikiSQL modeled this as a sequence generation problem using seq2seq but we are moving away from it. The text has to be cleaned before passing it to the model like doing decontraction of the words, removing stop words, removing non-alphanumeric text from the corpus. As we have the dataset in SQL queries and headers, so we have to featurize the text using a tokenizer from the nltk library and then concatenate the query and headers.
In probability, Bayes is a type of conditional probability. It predicts the event based on an event that has already happened. You can use Naive Bayes as a supervised machine learning method for predicting the event based on the evidence present in your dataset. In this tutorial, you will learn how to classify the email as spam or not using the Naive Bayes Classifier. Before doing coding demonstration, Let's know about the Naive Bayes in a brief.
According to Taweh Beysolow, "Natural Language Processing (NLP) is a subfield of computer science that is focused on allowing computers to understand language in a'natural' way, as humans do." NLP has evolved so rapidly gaining traction in its applications inn artificial intelligence (AI). In this project, we will explore one of the most exciting NLP applications i.e. We will build a machine learning model that can categorize tweets as positive (pro-vaccine), negative (anti-vaccine) or neutral. Stay tuned and let's jump into the project.
A support vector regression is a popular machine learning model today in this article, I would be giving you a detailed explanation and how this model works. Support vector model can be used for both problems regression as well as classification and it's divided into 2 parts support vector machine (SVM)is used for classification problems and support vector regression (SVR) is mostly used for regression problems but in this article, I would be telling you about support vector regression (SVR) to know more about support vector machine (SVM) go to this link