Machine Learning: The Time is Now!

David Pitt Machine Learning, React, Tutorial Leave a Comment

Machine Learning enables a system to automatically learn and progress from experience without being explicitly programmed. It’s a subset of the artificial intelligence (AI) technology space being applied and used throughout your everyday life. Think Siri, Alexa, toll booth scanners, text transcription of voicemails – these types of tools are used by just about everyone.

Image recognition and computer vision are also widely being used in production; recently just heard that Los Angeles, CA has made it illegal for law enforcement to use face recognition technology in its numerous public video cameras. The current state of the art allows real-time identification.

Interestingly, the algorithms and know-how for Machine Learning have been around for a long time. Artificial Intelligence was coined and researched as far back as the late 1950s, the advent of the digital computer, and expert systems and neural networks, that theoretically mimics how our brain learns.

The increase in Machine Learning production-ready applications started around 2012, with increased processing, bandwidth, and internet throughput power. This is important as deep learning algorithms like Neural Networks require lots of data and FPUs/GPUs to train.

In this blog, we introduce a conceptual overview of Neural Networks with a simple Neural Net code example implementation using Go. We will interact with it by building a ReactJS interface and train the Neural Network to recognize hand-drawn images of the numbers 0-9. Let’s dive in….

Core ML

Core ML After Dark

Derek Andre Machine Learning, Mobile, Technology Snapshot, Tutorial Leave a Comment

So you’ve made this great social media app, and you are about to sit back and wait for the money to roll in. But, there is a problem: people keep trying to upload nude photos to it.

What if we could have a trained machine learning model that could detect not safe for work (NSFW) content and do it on a iOS device, before any image would be uploaded to a server?

Developing this trained machine learning model is way out of scope for this blog post. Luckily, the good people at Yahoo have already done this with their open-sourced trained Caffe models. The question now is, how can we use this on an iOS device?

In this post: The sultry side of your iPhone can collide with acceptable use policies. We introduce a machine learning solution that can help your application decide what is truly too hot for the internet using Core ML on iOS…

Quick Introduction to the Computer Vision API

Brad Kirtley .NET, ASP.NET, Conversational Apps, Machine Learning, Technology Snapshot, Tutorial 1 Comment

Machine learning is a hot topic these days because the biggest tech companies are focused on taking this technology to a new level. For instance, to help develop autonomous driving cars, better interaction between you and your house with products like the Amazon Echo.

Machine learning is a core sub-area of artificial intelligence. Machine learning enables computers to self-learn without being explicitly programmed. As new data comes available, the computer has the ability to learn, grow, change, and develop itself to make better decision in the future. This technology will help reduce the workload and possible incorrect diagnoses when radiologist read films, reducing the amount of accidents on our highways caused by human error, possible reduction of inappropriate message / images / videos from bullying on social network sites.

This article will touch on one of the many Artificial Intelligence API’s that Microsoft has built for public consumption. We will specifically focus on the step-by-step process of uploading a picture, passing that picture onto the Microsoft Cognitive Services – Computer Vision API, and retrieving different attributes about that image. This is an aspect of AI technology that companies like Facebook & Google are using to try to stop bullying and other issue within social networking. Let’s get started…