Since we founded Fritz AI in late 2017, we’ve seen tremendous growth in the Boston-area tech scene. What was once a city whose tech economy centered on academia, biotech, and robotics, Boston has flourished into one of the most attractive locations for all kinds of tech start-ups, early- and late-stage alike.
Continue reading “Fritz AI Named a 2020 Boston “Startup to Watch” by Built In Boston”
Let’s say you want to make an iOS app that can recognize audio signals in real time, followed by the triggering of an action. From a global point of view, we’d need to explore the crossroads of machine learning and and mobile development.
Continue reading “Building a Sound Classification iOS Application using AI”
Over the past few years, deep learning techniques have dominated computer vision. One of the computer vision application areas where deep learning excels is image classification with Convolutional Neural Networks (CNNs).
Transfer learning, an approach where a model developed for a task is reused as the starting point for a model on a second task, is an important approach in machine learning. Prior knowledge from one domain and task is leveraged into a different domain and task.
Transfer learning, therefore, draws inspiration from human beings, who are capable of transferring and leveraging knowledge from what they have learned in the past for tackling a wide variety of tasks.
Continue reading “Using Transfer Learning and Pre-trained Language Models to Classify Spam”
Computer Vision is one of the hottest research fields within Deep Learning at the moment.
It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science).
Continue reading “The 5 Computer Vision Techniques That Will Change How You See The World”
The style transfer is one of the most creative applications of Convolutional Neural Networks. It allows you to retrieve the style of an image and use it to transform any given image.
It is an interesting technique that highlights the capacities and internal representations of neural networks. It can also be useful in certain scientific fields for augmenting or simulating image data.
The almost endless combinations of content and styles possible bring out unique and ever more creative results from neural network enthusiasts.
Lens Studio allows you to import and add audio to your lenses. Adding audio to your lenses enhances the nature of the lenses by enabling the viewer more interaction. It can be added in behavior scripts as a response and even in animations.
Behavior scripts in lens studio enable you to make interactions in your lenses by defining a trigger type and a response for that trigger.
OpenCV is a powerful tool for doing intensive operations in real-time, even for resource-limited mobile devices. Throughout a couple of tutorials, we’re going to create an Android app that applies various effects over images.
Continue reading “Image Effects for Android using OpenCV: Animated GIFs”
Deep Learning algorithms are excellent at solving very complex problems, including Image Recognition, Object Detection, Language Translation, Speech Recognition, and Synthesis, and include many more applications, such as Generative Models.
However, deep learning is extremely compute intensive—it’s generally only viable through acceleration by powerful general-purpose GPUs, especially from Nvidia. Unfortunately, mobile devices have very limited compute capacity; hence, most architectures that have been very successful on desktop computers and servers cannot be directly deployed to mobile devices.
Continue reading “Building an Image Recognition Model for Mobile using Depthwise Convolutions”
With big industry players committing a lot of resources to edge ML, increases in research into model compression/optimization for resource-constrained devices, and an ever-expanding landscape of use cases, we’ve seen the the initial stages of what we believe is a revolution in how and where machine learning operates.
Continue reading “Machine Learning on Mobile and at the Edge”
