Overview


AI and Machine Learning - Detailed Analysis, Facts and Figures An Infographic

#artificialintelligence

Machine learning is key technology behind use of artificial intelligence applications. We know that AI applications are growing tremendously and businesses are focusing on efficient use of such applications which is becoming mandate for every organization. We are hereby highlighting some viewpoints, facts, figures as findings on AI and machine learning in form of infographic.


Would You Survive the Titanic? A Guide to Machine Learning in Python

@machinelearnbot

I recommend using the "pip" Python package manager, which will allow you to simply run "pip3 install packagename " to install each of the dependencies: For actually writing and running the code I recommend using IPython, which will allow you to run modular blocks of code and immediately the view output values and data visualizations, along with the Jupyter Notebook as a graphical interface. With all of the dependencies installed, simply run "jupyter notebook" on the command line, from the same directory as the titanic3.xls The Data At First Glance: Who Survived The Titanic, And Why? Before we can feed our dataset into a machine learning algorithm, we have to remove missing values and split it into training and test sets. Interestingly, after splitting by class, the main deciding factor determining the survival of women is the ticket fare that they paid, while the deciding factor for men is their age(with children being much more likely to survive).


The past, present and future of AI in customer experience

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However, AI represents an opportunity to introduce intelligent, scalable engagement and more personalised experiences to help customers accomplish tasks or solve problems while also improving overall satisfaction. Whether they're based in messaging platforms or hardware devices, virtual concierges are bots designed to provide personalised services. We're already seeing the following list of AI applications implemented today: Today's customers live in a multi-screen, omnichannel world. Whether it's integrating back-end CRM, enhancing commerce, personalising experiences, introducing new touch points, predicting behaviors, trends and expectations, successful AI implementations require a new blueprint.


A review of denoising medical images using machine learning approaches

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Machine learning techniques are increasingly demonstrating success in image-based diagnosis, disease detection and disease prognosis. To reduce operator dependency and get better diagnostic accuracy, a computer aided diagnositic (CAD) system is a valuable and beneficial means for breast tumor detection and classification, fetal development and growth, Brain functioning, skin lesions and Lungs diseases [1]. Image denoising using machine learning techniques plays important role in the various application area of medical imaging such as pre-processing (noise removal from Ultrasound (US) images, segmentation (MRI of brain tumors and lung infections using X-rays), Computer aided diagnosis (CAD) for breast cancer, fetus development and many more). Further, denoising of medical images using data mining methods are analyzed.


Would You Survive the Titanic? A Guide to Machine Learning in Python Part 1

@machinelearnbot

This tutorial aims to give an accessible introduction to how to use machine learning techniques for your own projects and datasets. Social classes were heavily stratified in the early 20th, and this was especially true on the Titanic where the luxurious 1st class areas were completely off limits to the middle-class passengers of 2nd class, and especially to those who carried a 3rd class "economy price" ticket. We can also see that the women were younger than the men on average, were more likely to be travelBio: Patrick is a 23 year old Android Developer / IoT Engineer / Data Scientist / wannabe pioneer, originally from Boston and now working at SocialCops. Bio: Patrick Triest is a 23 year old Android Developer / IoT Engineer / Data Scientist / wannabe pioneer, originally from Boston and now working at SocialCops.


Automated Machine Learning -- A Paradigm Shift That Accelerates Data Scientist Productivity @ Airbnb

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A fair amount of our data science projects involve machine learning, and many parts of this workflow are repetitive. Model Diagnostics: Learning curves, partial dependence plots, feature importances, ROC and other diagnostics are extremely useful to generate automatically. AML is a powerful set of techniques for faster data exploration as well as improving model accuracy through model tuning and better diagnostics. The above case study highlights AML's capability to improve model accuracy, however we have realized AMLs other benefits as well.


Building a Bot to Answer FAQs: Predicting Text Similarity

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We'll conduct a nearest neighbour search in Python, comparing a user input question to a list of FAQs. To do this, we'll use indico's Text Features API to find all the feature vectors for the text data, and calculate the distance between these vectors to those of the user's input question in 300-dimensional space. Add the following code to similarity_text(), just below print t.draw(): If the bot's confidence level meets the threshold, it should return the appropriate FAQ answer. Otherwise, it should notify your customer support manager (you'll have to hook that up based on your messaging app's docs): Update run() one last time and then, well, run the code!


Understanding the Bias-Variance Tradeoff: An Overview

@machinelearnbot

While this will serve as an overview of Scott's essay, which you can read for further detail and mathematical insights, we will start by with Fortmann-Roe's verbatim definitions which are central to the piece: Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Again, imagine you can repeat the entire model building process multiple times. Fortmann-Roe ends the section on over- and under-fitting by pointing to another of his great essays (Accurately Measuring Model Prediction Error), and then moving on to the highly-agreeable recommendation that "resampling based measures such as cross-validation should be preferred over theoretical measures such as Aikake's Information Criteria." I recommend reading Scott Fortmann-Roe's entire bias-variance tradeoff essay, as well as his piece on measuring model prediction error.


Thesis: Robust Low-rank and Sparse Decomposition for Moving Object Detection: From Matrices to Tensors by Andrews Cordolino Sobral

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Next, we address the problem of background model initialization as a reconstruction process from missing/corrupted data. A novel methodology is presented showing an attractive potential for background modeling initialization in video surveillance. The algorithm makes use of double constraints extracted from spatial saliency maps to enhance object foreground detection in dynamic scenes. These works address the problem of low-rank and sparse decomposition on tensors.


Using AI to sentence criminals is a 'dangerous idea'

Daily Mail

Earlier this month, researchers unveiled an AI computer that could predict the results of Supreme Court trials better than a human. Earlier this month, researchers unveiled an AI computer that could predict the results of Supreme Court trials better than a human. Technology has brought many benefits to the court room, ranging from photocopiers to DNA fingerprinting and sophisticated surveillance techniques. While technology has brought many benefits to the court room, ranging from photocopiers to DNA fingerprinting and sophisticated surveillance techniques, Mr Markou says that that doesn't mean any technology is an improvement Recent work by Joanna Bryson, professor of computer science at the University of Bath, highlights that even the most'sophisticated' AIs can inherit the racial and gender biases of those who create them.