This app uses artificial intelligence to turn design mockups into source code

This app uses artificial intelligence to turn design mockups into source code

  • While traditionally it has been the task of front-end developers to transform the work of designers from raw graphical user interface mockups to actual source code, this trend might soon be a thing of the past – courtesy of artificial startup UIzard Technologies has leveraged the latest developments in the field of machine learning to…
  • What is particularly intriguing is that the so-called Pix2Code model has the capacity to produce code for three different platforms, including Android and iOS as well as other web-based technologies.
  • As UIzard founder Tony Beltramelli explains in his research, the novel approach could potentially “end the need for manually-programmed” user interfaces altogether.
  • At present, the method generates code from screenshots with an impressive accuracy of over 77 percent, but the consistency of the algorithm is likely to improve in the future.
  • Watch this brief video demonstration to see the AI-powered app in action: – – While UIzard has already shared some of the principles behind the technology powering its software, the company notes that the source code for the actual app will become available later this year.

Designers might no longer have to rely on front-end developers to turn their user interface mockups into source code – courtesy of artificial intelligence.
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Apple Machine Learning Journal

  • However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly.
  • An alternative to labelling huge amounts of data is to use synthetic images from a simulator.
  • This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images.
  • We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
  • Read the article View the article “Improving the Realism of Synthetic Images”

Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.
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This app uses artificial intelligence to turn design mockups into source code

This app uses artificial intelligence to turn design mockups into source code

  • While traditionally it has been the task of front-end developers to transform the work of designers from raw graphical user interface mockups to actual source code, this trend might soon be a thing of the past – courtesy of artificial startup UIzard Technologies has leveraged the latest developments in the field of machine learning to build a neural network that, once fed with raw screenshots of graphical user interface, proceeds to automatically generate code.
  • What is particularly intriguing is that the so-called Pix2Code model has the capacity to produce code for three different platforms, including Android and iOS as well as other web-based technologies.
  • As UIzard founder Tony Beltramelli explains in his research, the novel approach could potentially “end the need for manually-programmed” user interfaces altogether.
  • At present, the method generates code from screenshots with an impressive accuracy of over 77 percent, but the consistency of the algorithm is likely to improve in the future.
  • Watch this brief video demonstration to see the AI-powered app in action:

    While UIzard has already shared some of the principles behind the technology powering its software, the company notes that the source code for the actual app will become available later this year.

Designers might no longer have to rely on front-end developers to turn their user interface mockups into source code – courtesy of artificial intelligence.
Continue reading “This app uses artificial intelligence to turn design mockups into source code”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
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A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

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  • In machine learning, computers apply statistical learning techniques to automatically identify patterns in data.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.
  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • There are clearly patterns in the data, but the boundaries for delineating them are not obvious.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

Drones and machine learning combine to indentify, protect endangered sea cows

Drones and machine learning combine to identify, protect endangered sea cows

  • The latest version of the detector can find 80 percent of the dugongs in images.
  • ” the technology could be applied to surveys of any species as long as you start off which a set of images to train the detector.”
  • Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow.
  • Given a large image, the region proposal module generates a list of subwindows of the image, centered on candidate blobs.
  • Drones and machine learning combine to indentify, protect endangered sea cows

Researchers in Australia are using drones and machine learning technology to spot sea cows in their natural habit.
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Nowhere to Hide: Algorithms Are Learning to ID Pixelated Faces

Nowhere to Hide: #Algorithms Are Learning to ID Pixelated Faces  #machinelearning

  • Nowhere to Hide: Algorithms Are Learning to ID Pixelated Faces
  • The researchers developed the algorithm using open-source machine-learning software.
  • The researchers developed an algorithm that could identify faces and numbers even after they were blurred out.
  • The algorithm is built using a very simple process.
  • Using the open-source software and standard neural network templates, the researchers could feed the algorithm thousands of examples of faces that have been blurred or pixelated to train it.

The new tech can identify faces or numbers even if they’ve been blurred out.
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