In the past few years we’ve seen many startups and even mature companies coming up with new mobile apps or features powered by machine learning and AI. These features require some heavy, real-time processing by neural networks.
The potential killers of these ML-powered experiences? Data roundtrips for inference, the cost of backend servers to support millions of devices, concerns surrounding user data privacy.
Continue reading “Comparing Mobile Machine Learning Frameworks”
In recent years, AI and (more specifically) machine learning have become so popular that the hype surrounding them has become contagious.
It’s becoming apparent that the widespread use of deep learning doesn’t stop at just computer scientists and programmers but has made its way into every domain, ranging from physics, biology, and chemistry to healthcare, transportation, and finance.
Continue reading “How next-gen AI accelerators will transform mobile machine learning”
In Spark AR Studio, you use the Simulator to perceive how an effect will appear on a device while you are creating it.
The Simulator is positioned on the top right side of the Spark AR Studio interface. You will see that the Simulator previews an effect on an iPhone 8 by default.
Continue reading “Understanding the Simulator in Spark AR Studio”
An insider threat is any user or individual with authorized permission to access physical or virtual corporate assets. Insider threats hold credentials that, if abused, can potentially violate the trust of the organization.
Continue reading “An Introduction to Insider Threat Detection With Machine Learning”
Core ML is the most popular and powerful framework for adding machine learning and AI to iOS apps. High-level APIs provided by tools like Turi Create and Create ML make it possible to train mobile-friendly models without ML expertise.
But as projects grow in scale and complexity, it’s often necessary to dive deeper into the capabilities of Core ML to deliver the best user experiences possible.
In this article, we’ll build a simple neural network using Keras. We’ll assume you have prior knowledge of machine learning packages such as scikit-learn and other scientific packages such as Pandas and Numpy.
The journey to AR filters wasn’t easy for Paris-born Rocha, but the creator, who studied Art History at the Sorbonne University, is making the most of it. Now making filters for clients and for pleasure, the former makeup artist is making waves in filter innovation. I learned more about the artist and the story behind her incredible, fun, and surreal filters.
In this tutorial you will learn how to work with hair segmentation effects by creating texture and adding interactivity to your filters using Spark AR Studio.
Continue reading “How to Create Hair Segmentation Effects Using Spark AR Studio”
Recently, Snapchat released a new feature where AI developers can “easily” integrate their own custom models—one of which is the ability to classify single objects in order to trigger fun and immersive AR effects. We’ll cover this ML task in this article.
Lens Studio, an augmented reality creativity suite released by Snapchat in 2017, gives users the opportunity to create their own augmented reality (AR) Lenses.
Continue reading “Create a Custom AI-Powered Snapchat Lens with Fritz AI Studio”
This year, Apple introduced Core ML 2 at WWDC 2018, with a focus on making machine learning more flexible and powerful for developers to use.
With the release of this new and improved framework, Apple also announced a freshly updated version of coremltools. This handy Python library can be used to convert trained models into a Core ML format as well as making predictions directly from your machine.
Continue reading “Reducing Core ML 2 Model Size by 4X Using Quantization in iOS 12”
