As the use of facial recognition technology is expanding in different domains, ensuring its responsible use is gaining more importance. This paper conducts a comprehensive literature review of existing studies on facial recognition technology from the perspective of privacy, which is one of the key Responsible AI principles. Cloud providers, such as Microsoft, AWS, and Google, are at the forefront of delivering facial-related technology services, but their approaches to responsible use of these technologies vary significantly. This paper compares how these cloud giants implement the privacy principle into their facial recognition and detection services. By analysing their approaches, it identifies both common practices and notable differences. The results of this research will be valuable for developers and businesses by providing them insights into best practices of three major companies for integration responsible AI, particularly privacy, into their cloud-based facial recognition technologies.

A social robot is an autonomous robot that interact with people by engaging in social emotive behaviors, skills, capacities, and rules attached to its collaborative role. In order to achieve these goals we believe that modeling the interaction with the user and adapt the robot behavior to the user herself are fundamental for its social role. This paper presents our first attempt to integrate user modeling features in social and affective robots. We propose a cloud-based architecture for modeling the user-robot interaction in order to reuse the approach with different kind of social robots.

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. In this code pattern, learn how to use optical character recognition (OCR) and the IBM Maximo Visual Inspection object recognition service to identify and read license plates. Using IBM Maximo Visual Inspection and the Custom Inference Scripts, you can build an object detection model to identify license plates from images of cars. The models in the IBM Maximo Visual Inspection object recognition service can identify portions of images that represent a license plate. Then, the post custom inference script can crop this area and use open source to perform OCR on the text to return the license plate.

Companies pay cloud-computing providers like Amazon, Microsoft, and Google big money to avoid operating their own digital infrastructure. Google's cloud division will soon invite customers to outsource something less tangible than CPUs and disk drives--the rights and wrongs of using artificial intelligence. The company plans to launch new AI ethics services before the end of the year. Initially, Google will offer others advice on tasks such as spotting racial bias in computer vision systems or developing ethical guidelines that govern AI projects. Longer term, the company may offer to audit customers' AI systems for ethical integrity and charge for ethics advice.

In May 2017, researchers at Google Brain declared the formation of AutoML, an artificial intelligence (AI) that is equipped for producing its own AIs. All the more as of late, they chose to give AutoML its greatest challenge to date, and the AI that can construct AI made a "child" that beat the majority of its human-made partners. With it, Google may soon figure out how to make AI innovation that can incompletely remove the people from building the AI frameworks that many accept are the future of the innovation business. The venture is a piece of a lot bigger exertion to bring the best in class AI techniques to a more extensive collection of organizations and software developers. The Google analysts automated the structure of ML models utilizing a methodology called reinforcement learning.

Tech giants have made some major strides in advancing facial recognition technology. But a new study, called'Gender Shades,' has found that it may not be working for all users, especially those who aren't white males. A researcher from the MIT Media Lab discovered that popular facial recognition services from Microsoft, IBM and Face vary in accuracy based on gender and race. A researcher from MIT tested popular facial recognition services and found that they experienced more errors when the used was a dark-skinned female. To illustrate this, researcher Joy Buolamwini created a data set using 1,270 photos of parliamentarians from three African nations and three Nordic countries. The faces were selected to represent a broad range of human skin tones, using a labeling system developed by dermatologists, called the Fitzpatrick scale.

SEATTLE--For the third year in a row, Microsoft is heavily promoting machine-learning services at its Build developer conference. Over the three years, some of the language used around the services has changed--the "machine learning" term seems to have fallen out of favor, being replaced by the better-known "artificial intelligence," and Microsoft has added many more services. But the bigger change is that ubiquitous intelligence now seems a whole lot more feasible than it did three years ago. Three years ago, the service selection was narrow--a language service that identified important elements from natural language, speech-to-text and text-to-speech, an image-recognition service, a facial recognition service. But outside of certain toy applications, such as Microsoft's age-guessing website, the services felt more than a little abstract. They felt disconnected from real-world applications.