Machine learning (ML) began its ascent into the medical industry when it acquired the ability to detect visual patterns between images—a skill doctors and technicians take years to master.
Specifically, ML models for computer vision tasks in the medical field train on datasets of separated images to learn to recognize their similarities and differences.
Continue reading “Detecting Skin Cancer on iOS with Xcode and Create ML”
Making lenses is a difficult endeavor, but for Shawn there’s another layer: he’s colorblind. The young creator has already made a number of lenses that allow users to see the world the way he does.
On top of a solar system themed series of lenses, he’s created a number of fun, subtle filters. I chatted with the creator to learn more.
If you got past the jargon of the title, you probably have at least a passing interest in computer vision. However, fear not! This is going to be a fairly gentle walk-through of some of my projects at the intersection of Machine Learning and Augmented Reality.
They all share a common denominator: feature point extraction. InstaSaber, Say BARK!, and the puppet videos you see below are a few examples.
Continue reading “Real-Time 2D/3D Feature Point Extraction from a Mobile Camera”
Throughout the Android chat app development series, we’ve built an app that allows users to register, send/receive text messages, open conversations, and receive notifications for incoming messages.
One additional essential feature for chat apps is the ability to update the active, live conversation. Once a new message arrives for the currently active conversation, it should be updated to reflect the new changes (either sent/received messages).
Continue reading “Live Conversation Updates for New Messages in an Android Chat Application”
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.
