Construct theory in social psychology, developed by George Kelly are mental constructs to predict and anticipate events. Constructs are how humans interpret, curate, predict and validate data; information. AI today is biased because it is trained with a narrow construct as defined by the training data labels. Machine Learning algorithms for facial recognition discriminate against darker skin colors and in the ground breaking research papers (Buolamwini, Joy and Timnit Gebru. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. FAT (2018), the inclusion of phenotypic labeling is proposed as a viable solution. In Construct theory, phenotype is just one of the many subelements that make up the construct of a face. In this paper, we present 15 main elements of the construct of face, with 50 subelements and tested Google Cloud Vision API and Microsoft Cognitive Services API using FairFace dataset that currently has data for 7 races, genders and ages, and we retested against FairFace Plus dataset curated by us. Our results show exactly where they have gaps for inclusivity. Based on our experiment results, we propose that validated, inclusive constructs become industry standards for AI ML models going forward.

You can use the camera feed that ARCore captures in a machine learning pipeline with the ML Kit and the Google Cloud Vision API to identify real-world objects, and create an intelligent augmented reality experience. The image at left is taken from the ARCore ML Kit sample, written in Kotlin for Android. This sample app uses a machine learning model to classify objects in the camera's view and attaches a label to the object in the virtual scene. The ML Kit API provides for both Android and iOS development, and the Google Cloud Vision API has both REST and RPC interfaces, so you can achieve the same results as the ARCore ML Kit sample in your own app for Unity (AR Foundation). See Use ARCore as input for Machine Learning models for an overview of the patterns you need to implement.

There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different manipulations. Recent works have only focused on typical adversarial attacks when evaluating the robustness of vision APIs. We propose two new aspects of adversarial image generation methods and evaluate them on the robustness of Google Cloud Vision API's optical character recognition service and object detection APIs deployed in real-world settings such as sightengine.com, picpurify.com, Google Cloud Vision API, and Microsoft Azure's Computer Vision API. Specifically, we go beyond the conventional small-noise adversarial attacks and introduce secret embedding and transparent adversarial examples as a simpler way to evaluate robustness. These methods are so straightforward that even non-specialists can craft such attacks. As a result, they pose a serious threat where APIs are used for high-stakes applications. Our transparent adversarial examples successfully evade state-of-the art object detections APIs such as Azure Cloud Vision (attack success rate 52%) and Google Cloud Vision (attack success rate 36%). 90% of the images have a secret embedded text that successfully fools the vision of time-limited humans but is detected by Google Cloud Vision API's optical character recognition. Complementing to current research, our results provide simple but unconventional methods on robustness evaluation.

Current neural network-based image classifiers are susceptible to adversarial examples, even in the black-box setting, where the attacker is limited to query access without access to gradients. Previous methods --- substitute networks and coordinate-based finite-difference methods --- are either unreliable or query-inefficient, making these methods impractical for certain problems. We introduce a new method for reliably generating adversarial examples under more restricted, practical black-box threat models. First, we apply natural evolution strategies to perform black-box attacks using two to three orders of magnitude fewer queries than previous methods. Second, we introduce a new algorithm to perform targeted adversarial attacks in the partial-information setting, where the attacker only has access to a limited number of target classes. Using these techniques, we successfully perform the first targeted adversarial attack against a commercially deployed machine learning system, the Google Cloud Vision API, in the partial information setting.

You might have heard about Google's new Cloud Vision API. If you haven't, you should check it out. It lets you upload an image and get a TON of machine-learning based information out of it, including landmark detection, face detection, emotion detection, adult content detection, and even OCR. My favorite feature has to be the label detection. Give Cloud Vision a image, and it will tell you what's in the image!

Have you often wondered about the accuracy of Google Images and thought about how you could incorporate some of that technology in your applications? Google with its years of data and machine learning experience, and backed by its infrastructure, has been announcing not just how much of their own applications are utilizing Machine Learning but also opening up their platform for developers to use. In this article we'll cover the Google Cloud Vision API, which enables you to give vision capability to your applications backed by Google's Machine Vision Infrastructure. We will provide a high level overview of the API and its features and then show you how to get started with basic examples that let you exercise the API's features. There are multiple references in the article that will help you as you go deeper into this API.