Go gaga over Machine Learning

Here is what in my opinion would be the next thing test automation tools would do: Deep Learning + Building code just by using images as input! Project pix2code: Generating Code from a Graphical User Interface Screenshot Here is a demo of how it works: Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies). Official research page:  https://uizard.io/research#pix2code Here is the github Project:  https://github.com/tonybeltramelli/pix2code

Run an App Services No matter what you do with AWS you'll probably end up using these services as everything else interacts with them. EC2 Should have been called Amazon Virtual Servers Use this to Host the bits of things you think of as a computer. It's like http://www.linode.com","It's handwavy, but EC2 instances are similar to the virtual private servers you'd get at Linode, DigitalOcean or Rackspace. IAM Should have been called Users, Keys and Certs Use this to Set up additional users, set up new AWS Keys and policies. S3 Should have been called Amazon Unlimited FTP Server Use this to Store images and other assets for websites. Keep backups and share files between services. Host static websites. Also, many of the other AWS services write and read from S3. S3 in Plain English    S3 Buckets of Objects  VPC Should have been called Amazon Virtual Colocated Rack Use this to Overcome objections that "all our stuff is o

Solving the most common problem predicting house prices in any suburb using supervised learning. What's amazing is we shall build a Machine Learning model using MLJAR MLJAR is a human-first platform for machine learning. It provides a service for prototyping, development and deploying pattern recognition algorithms. It makes algorithm search and tuning painless! The basics of ML can be read anywhere on github/google. What we need to know is regression/classification and when to use what.  First things first test data: https://github.com/AdyKalra/MachineLearningHousing/tree/master/house_prices#house-prices train sample - (data_train.csv file) for model learning  test samples (data_test.csv) for predictions Create a new project Select Regression as a task Add train and test data Note - When we add test dataset check the option: This dataset will be used only for predictions because we want to predict the sale price Specify columns that we use , target

John Bates the CEO of Testplant has exactly my views on what smart and intelligent test automation would be: Quick snippets: 1. Intelligent automation  - The only way to realistically test a digital app is through an intelligent automation engine accessing the application as a user would - taking control of a machine, actually using the app to exercise workflows and collecting intelligent analytics along the way. This involves technology to understand on-screen images and text, such as smart image search and dynamic neural networks (so called “deep learning”). 2. Intelligent test coverage generation and ‘bug-hunting’  - There are a potentially infinite number of paths through a complex app so which ones should we follow in our automation? We can use AI classification algorithms such as Bayesian networks, to select paths and 'bug hunt'. As these paths are explored, the bug-hunting AI algorithm continues to learn from correlations in data to refine the coverage