Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder with very high prevalence around the world. Research progress in the field of ASD facial analysis in pediatric patients has been hindered due to a lack of well-established baselines. In this paper, we propose the use of the Vision Transformer (ViT) for the computational analysis of pediatric ASD. The presented model, known as ViTASD, distills knowledge from large facial expression datasets and offers model structure transferability. Specifically, ViTASD employs a vanilla ViT to extract features from patients' face images and adopts a lightweight decoder with a Gaussian Process layer to enhance the robustness for ASD analysis. Extensive experiments conducted on standard ASD facial analysis benchmarks show that our method outperforms all of the representative approaches in ASD facial analysis, while the ViTASD-L achieves a new state-of-the-art. Our code and pretrained models are available at https://github.com/IrohXu/ViTASD.

Masks and face coverings have been prevalent in many cultures and work environments for decades. But if you are reading this in the year 2021, we can read your mind -- you are thinking about the pandemic! Masks became a must-have accessory in our daily lives due to Covid-19. Analyzing people's faces has vast applications from retail stores to corporate campuses and experiential marketing. The question is how do we train robust AI models without having access to vast datasets of people wearing masks?

In the last few years, companies have started using such race-detection software to understand how certain customers use their products, who looks at their ads, or what people of different racial groups like. Others use the tool to seek different racial features in stock photography collections, typically for ads, or in security, to help narrow down the search for someone in a database. In China, where face tracking is widespread, surveillance cameras have been equipped with race-scanning software to track ethnic minorities. The field is still developing, and it is an open question how companies, governments and individuals will take advantage of such technology in the future. Use of the software is fraught, as researchers and companies have begun to recognize its potential to drive discrimination, posing challenges to widespread adoption.

In today's post, we are going to take a look at another AI service of AWS, Amazon Rekognition. We focus on the image for object and scene detection, and we learn how to use the service programmatically. Furthermore, you can also check out one of my previous posts about another AI Service, Amazon Kendra. Kendra is a service that lets you build your search engine. You can find the code for this post here.

In early May, a press release from Harrisburg University claimed that two professors and a graduate student had developed a facial-recognition program that could predict whether someone would be a criminal. The release said the paper would be published in a collection by Springer Nature, a big academic publisher. With "80 percent accuracy and with no racial bias," the paper, A Deep Neural Network Model to Predict Criminality Using Image Processing, claimed its algorithm could predict "if someone is a criminal based solely on a picture of their face." The press release has since been deleted from the university website. Tuesday, more than 1,000 machine-learning researchers, sociologists, historians, and ethicists released a public letter condemning the paper, and Springer Nature confirmed on Twitter it will not publish the research.

This week IBM, Microsoft and Amazon announced that they would suspend the sale of their facial recognition technology to law enforcement agencies. But the moves from the tech giants also illustrate the inherent risks of AI, especially when it comes to bias and the potential for invasion of privacy. Note that there are already indications that Congress will take action to regulate the technology. In the meantime, many cities have already instituted bans, such San Francisco. Because of the advances of deep learning and faster systems for processing enormous amounts of data, facial recognition has certainly seen major strides over the past decade.

In early May, a press release from Harrisburg University claimed that two professors and a graduate student had developed a facial-recognition program that could predict whether someone would be a criminal. The release said the paper would be published in a collection by Springer Nature, a big academic publisher. With "80 percent accuracy and with no racial bias," the paper, A Deep Neural Network Model to Predict Criminality Using Image Processing, claimed its algorithm could predict "if someone is a criminal based solely on a picture of their face." The press release has since been deleted from the university website. Tuesday, more than 1,000 machine-learning researchers, sociologists, historians, and ethicists released a public letter condemning the paper, and Springer Nature confirmed on Twitter it will not publish the research.

This week IBM, Microsoft and Amazon announced that they would suspend the sale of their facial recognition technology to law enforcement agencies. But the moves from the tech giants also illustrate the inherent risks of AI, especially when it comes to bias and the potential for invasion of privacy. Note that there are already indications that Congress will take action to regulate the technology. In the meantime, many cities have already instituted bans, such San Francisco. Because of the advances of deep learning and faster systems for processing enormous amounts of data, facial recognition has certainly seen major strides over the past decade.

Artificial Intelligence (AI) and Machine Learning are used to power a variety of important modern software technologies. AI also powers the facial recognition software commonly used by law enforcement, landlords, and private citizens. Of all the uses for AI-powered software, facial recognition is a big deal. Security teams from large buildings that rely on video surveillance – like schools and airports – can benefit greatly from this technology. An AI algorithm has the potential to detect a known criminal or an unauthorized person on the property.

Artificial Intelligence (AI) and Machine Learning are used to power a variety of important modern software technologies. AI also powers the facial recognition software commonly used by law enforcement, landlords, and private citizens. Of all the uses for AI-powered software, facial recognition is a big deal. Security teams from large buildings that rely on video surveillance – like schools and airports – can benefit greatly from this technology. An AI algorithm has the potential to detect a known criminal or an unauthorized person on the property.