Improvements to Fritz AI Studio’s UX

When we first released Fritz AI Studio earlier this year, we firmly believed in its technical backbone. Using the Studio, you could synthetically generate labeled datasets, use those to train mobile-ready models without code, deploy them across mobile platforms, and set up pipelines to collect real-world model data, making it easier to improve models over time.

And with a lot of help and feedback from the many conversations with members of our Fritz AI user community, we’ve added the connective tissue to that technical backbone that allows users to more seamlessly build and deploy models.

For our first big UX overhaul, we focused on the following:

• Account and project creation flows
• Project dashboards
• Clarity around model building steps
• Image annotation workflow

I’ll also highlight a couple of smaller improvements that smooth the edges of the model training job workflow.

To show these improvements in action, I’ll work through a simple demo project: building a custom golf ball object detection model.

#fritz-product #ux #heartbeat #mobile-app-development #machine-learning

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Buddha Community

Create a Custom AI-Powered Snapchat Lens with Fritz AI Studio

Introduction

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.

But this software is not primarily intended for ordinary Snapchat fans. Rather, it’s built for designers or even developers and artists who wish to work with a powerful AR creation tool, with the aim of providing the Snapchat community with ever more immersive and fun Lenses. Lenses are a big part of the user’s experience inside Snapchat, used either in stories or in a peer-to-peer fashion, whether to communicate with friends or to simply have fun.

Lens Studio is therefore full-fledged creativity software that allows users to create and save graphic projects using templates and guides designed to inspire.

I can’t express how easy it is to use, especially when you start with fully-featured templates that can get you started in just a few seconds.

Note: Lens Studio, in my experience, seems to be eating up as much CPU and Memory resources it can. I would recommend at least 8GB (16BG preferably) of Memory.

We should also add that the social networking platform said at the launch of Lens Studio that it had partnered with several development agencies specializing in augmented reality to help advertisers create lenses capable of leveraging users’ facial and body attributes. This should help the US firm open up to more brands, as they will no longer have to rely solely on creations designed in-house by Snapchat.

_In this article, I will show you how easily you can create a custom machine learning-powered Lens with _almost no code. As a proof of concept, I chose to create a Lens that will recognize and classify Bulldogs.

The idea is pretty simple—you open the Lens and start taking videos or photos of any Bulldog, and the Lens will classify the object and show us a text, activate confetti, and initiate a dog barking audio clip.

#heartbeat #fritz-ai #lens-studio #snapchat #ai

Improvements to Fritz AI Studio’s UX

When we first released Fritz AI Studio earlier this year, we firmly believed in its technical backbone. Using the Studio, you could synthetically generate labeled datasets, use those to train mobile-ready models without code, deploy them across mobile platforms, and set up pipelines to collect real-world model data, making it easier to improve models over time.

And with a lot of help and feedback from the many conversations with members of our Fritz AI user community, we’ve added the connective tissue to that technical backbone that allows users to more seamlessly build and deploy models.

For our first big UX overhaul, we focused on the following:

• Account and project creation flows
• Project dashboards
• Clarity around model building steps
• Image annotation workflow

I’ll also highlight a couple of smaller improvements that smooth the edges of the model training job workflow.

To show these improvements in action, I’ll work through a simple demo project: building a custom golf ball object detection model.

#fritz-product #ux #heartbeat #mobile-app-development #machine-learning

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers

• Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
• Slowing Down the Weight Norm Increase in Momentum-based Optimizers
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
• Coherent Reconstruction of Multiple Humans from a Single Image
• Variational Bayesian Monte Carlo with Noisy Likelihoods
• GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning
• Are we done with ImageNet?
• How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds
• Background Modeling via Uncertainty Estimation for Weakly-supervised Action Localization
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
• Learning a Unified Sample Weighting Network for Object Detection

Articles

• Deepfake Detection Challenge Results: An open initiative to advance AI
• Slightly Unnerving AI Produces Human Faces Out of Totally Pixelated Photos
• NeoML Released as TensorFlow Alternative
• Google Meet takes on Zoom with AI-powered noise cancellation
• Open Compound Domain Adaptation
• Dense pose for animal classes with transfer learning
• 10 Ways Enterprises Are Getting Results From AI Strategies
• An understanding of AI’s limitations is starting to sink in
• Why we switched from Flask to FastAPI for production machine learning
• Simple Transformers
• The key differences between rule-based AI and machine learning
• Deep learning-based surrogate models outperform simulators and could hasten scientific discoveries
• HANDS-ON GUIDE TO OBJECT DETECTION USING YOLO
• Artificial neural networks that mimic the flexibility and computing power of the brain
• MIT researchers train AI to predict how humans paint works of art
• What Do Machine Learning Engineers Do Anyway?
• AI Is Amazing But Complicated, And We Don’t Necessarily Need To Plunge In Headfirst
• Deep learning networks track up to 100 animals simultaneously
• Is artificial intelligence changing art?

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai