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Artificial Intelligence and Machine Learning in the News, March 19, 2018

 What is Windows Machine Learning and how to get started

With the next release of Windows, developers will be able to evaluate trained machine learning models locally on Windows 10 devices, allowing developers to use pre-trained models within their applications with hardware-accelerated performance by leveraging the device’s CPU or GPU to compute evaluations for both classical Machine Learning algorithms and Deep Learning.

2018-03-15 00:00:00 

CloudQuant Thoughts : Once the supply chain for GPUs stabilizes after the BitCoin rush we should see incredibly powerful personal machines capable of doing most ML tasks that ordinary users will require.


 AI matches human performance in translating news from Chinese to English

To reach the human parity milestone, three research teams worked together to add a number of other training methods that would make the system more fluent and accurate. In many cases, these new methods mimic how people improve their own work iteratively, by going over it again and again until they get it right.

“Much of our research is really inspired by how we humans do things,” said Tie-Yan Liu, a principal research manager with Microsoft Research Asia in Beijing, who leads a machine learning team that worked on this project.

2018-03-14 06:00:16-07:00

CloudQuant Thoughts: Repeating until you get it right and Curve fitting are not the same thing!


 Top 5 Data Science & Machine Learning Repositories on GitHub in Feb 2018

FastPhotoStyle – FastPhotoStyle is a python library developed by NVIDIA. The model takes a content photo and a style photo as inputs. It then transfers the style of the style photo to the content photo.

Twitter Scraper – If you’ve ever scraped tweets from Twitter, you have experience working with it’s API. It has it’s limitations and is not easy to work with. This python library was created with that in mind – it has no API rate limits (does not require authentication), no limitations, and is ultra quick. You can use this library to scrape the tweets of any user trivially

Handwriting Synthesis – This is an implementation of the handwriting synthesis experiments presented in the ‘Generating Sequences with Recurrent Neural Networks’ paper by Alex Graves. As the name of the repository suggests, you can generate different styles of handwriting. The model is based on priming and biasing. Priming controls the style of the samples and biasing controls the neatness of the samples.

ENAS PyTorch – This is a PyTorch implementation of “Efficient Neural Architecture Search (ENAS) via Parameters Sharing”. What do ENAS do? They reduce the computational requirement, that is, the GPU Hours of the Neural Architecture Search by an incredible 1000 times. They do this via parameter sharing between models that are subgraphs within a large computational graph.

Sign Language – This is a relatively straightforward, yet utterly fascinating, use of machine learning. Using a convolutional neural network in python, the developer has built a model that can recognize the hand gestures and convert it into text on the machine.
2018-03-15 10:19:42+05:30

CloudQuant Thoughts: I can immediately see a use for that Twitter Scraper!

 You can now Build your own 3D Digital Face Emoji using Deep Learning

It is amazing how machine learning is making hardware obsolete. From 3D face scanners to dual lenses (as we covered in the Pixel 2 article), hardware is just not a necessary component when algorithms are as intelligent as this one.

The deep learning model can build a remarkably accurate facial and hair 3D digital avatar. Built using an extremely deep neural network with over 50 layers.
Over 40,000 images of various hairstyles used to train the neural network

2018-03-16 13:37:44+05:30

CloudQuant Thoughts: I always warned my daughter not to make digital versions of her friends, that it was a road to ruin, they would never be flattered.. Now am I going to have to teach my AI the same lesson?!


 We should remember Stephen Hawking’s opinion of AI

To be clear, Hawking was no great fan of general artificial intelligence. He repeatedly said that a superintelligent AI could spell the end of humanity. His argument was fairly straightforward: A superintelligence would be able to pursue its goals incredibly competently, and if those goals weren’t aligned with humanity’s, we’d get run over.

2018-03-16 00:00:00

CloudQuant Thoughts: A great human being has passed. His thought’s on AI remind me of  “the paperclip maximizer” thought experiment which showed that general artificial intelligence, even one designed competently and without malice, could ultimately destroy humanity.

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