My godson, Michael, is a playful, energetic 15-year-old, with a deep love of Star Wars, a wry smile, and an IQ in the low 70s. His learning disabilities and autism have made his journey a hard one. His parents, like so many others, sometimes rely on screens to reduce stress and keep him occupied. They monitor the apps and websites he uses, but things are not always as they initially appear. When Michael asked them to approve installing Linky AI, a quick review didn't reveal anything alarming, just a cartoonish platform to pass the time.

In 2010, Barack Obama signed the Plain Writing Act into law, requiring that federal government documents use clear, straightforward language. Plain-language documents serve a dual purpose: they can make information more accessible to people with disabilities that affect cognition and memory, but also address the fact that legislation is already complicated for most people to read--a case in point of how accessibility practices benefit even those without a particular disability. New Disabled South, a disability justice nonprofit founded in 2022, is trying to make more information available to disabled people on legislation that affects them, launching its Plain Language Policy Dashboard in November to cover 14 Southern states. As of now, the bills it explains fall into six categories: accessibility, civil rights, criminalization, poverty and care, democracy, and education. Dom Kelly, New Disabled South's CEO, told me that he hopes the dashboard--which uses AI to translate texts into plain language, which is then checked for accuracy--can also help "combat myths and disinformation" that spread on social media, like whether a mental health–related bill could actually lead to more institutionalization.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' Researchers are proposing using artificial intelligence technology to help diagnose autism spectrum disorder. In a recent article published in Scientific Reports, researchers from Brazil, France and Germany reportedly used magnetic resonance imaging to train a machine learning algorithm. The work – in which the "quantitative diagnostic method" is proposed – was based on brain imaging data for 500 people, with more than 240 that had been diagnosed with autism. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Social robots, also known as service or assistant robots, have been developed to improve the quality of human life in recent years. Socially assistive robots (SAR) are a special type of social robots that focus on providing support through social interaction. The design of socially capable and intelligent robots can vary, depending on the target user groups. In this work, I assess the effect of socially assistive robots' roles, functions, and communication approaches in the context of a social agent providing service or companionship to users with developmental disabilities. In this thesis, I describe an exploratory study of interaction design for a socially assistive robot that supports people suffering from developmental disabilities. While exploring the impacts of visual elements to robot's visual interface and different aspects of robot's social dimension, I developed a series of prototypes and tested them through three user studies that included three residents with various function levels at a local group home for people with developmental disabilities. All user studies had been recorded for the following qualitative data analysis. Results show that each design factor played a different role in delivering information and in increasing engagement, and there are more aspects of HRI to consider besides robot's graphical user interface and speech, such as proxemics and robot's physical appearance and dimensions. I also note that some fundamental design principles that would work for ordinary users did not apply to our target user group. I conclude that socially assistive robots could benefit our target users and acknowledge that these robots were not suitable for certain scenarios based on the feedback from our users.

Augmentative and alternative communication (AAC) is a practical means of communication for people with language disabilities. In this study, we propose PicTalky, which is an AI-based AAC system that helps children with language developmental disabilities to improve their communication skills and language comprehension abilities. PicTalky can process both text and pictograms more accurately by connecting a series of neural-based NLP modules. Moreover, we perform quantitative and qualitative analyses on the essential features of PicTalky. It is expected that those suffering from language problems will be able to express their intentions or desires more easily and improve their quality of life by using this service. We have made the models freely available alongside a demonstration of the Web interface. Furthermore, we implemented robotics AAC for the first time by applying PicTalky to the NAO robot.

Transition to Adulthood is an essential life stage for many families. The prior research has shown that young people with intellectual or development disabil-ities (IDD) have more challenges than their peers. This study is to explore how to use natural language processing (NLP) methods, especially unsupervised machine learning, to assist psychologists to analyze emotions and sentiments and to use topic modeling to identify common issues and challenges that young people with IDD and their families have. Additionally, the results were compared to those obtained from young people without IDD who were in tran-sition to adulthood. The findings showed that NLP methods can be very useful for psychologists to analyze emotions, conduct cross-case analysis, and sum-marize key topics from conversational data. Our Python code is available at https://github.com/mlaricheva/emotion_topic_modeling.

Autism spectrum disorder is a developmental disorder characterized by significant social, communication, and behavioral challenges. Individuals diagnosed with autism, intellectual, and developmental disabilities (AUIDD) typically require long-term care and targeted treatment and teaching. Effective treatment of AUIDD relies on efficient and careful behavioral observations done by trained applied behavioral analysts (ABAs). However, this process overburdens ABAs by requiring the clinicians to collect and analyze data, identify the problem behaviors, conduct pattern analysis to categorize and predict categorical outcomes, hypothesize responsiveness to treatments, and detect the effects of treatment plans. Successful integration of digital technologies into clinical decision-making pipelines and the advancements in automated decision-making using Artificial Intelligence (AI) algorithms highlights the importance of augmenting teaching and treatments using novel algorithms and high-fidelity sensors. In this article, we present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans to AUIDD individuals. By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior using reinforcement-based augmented or virtual reality and other mobile platforms. Thus, AI-ABA could assist clinicians to focus on making precise data-driven decisions and increase the quality of individualized interventions for individuals with AUIDD.

BOSTON--Among the phrases used to describe the AI business solution being offered by startups at AI World 2019 "machine learning automation" is used quite frequently. It's an automation services play in many cases. Companies wanting to get started in AI or get to the next step in the journey to operating AI applications, need help. Getting the data to a stage where machine learning can be employed is required. Some startups have an employment skew, offering to help companies find the needed data science or other AI team, or offering a remote workforce.

Harvard University researchers have developed a soft, non-toxic wearable sensor that unobtrusively attaches to the hand and measures the force of a grasp and the motion of the hand and fingers. The research was published in Advanced Functional Materials and is a collaboration between the Wyss Institute for Biologically Inspired Engineering, The Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), Beth Israel Deaconess Medical Center, and Boston Children's Hospital. One novel element of the sensor is a non-toxic, highly conductive liquid solution. "We have developed a new type of conductive liquid that is no more dangerous than a small drop of salt water," said Siyi Xu, a graduate student at SEAS and first author of the paper. "It is four times more conductive than previous biocompatible solutions, leading to cleaner, less noisy data."

We are bringing you four challenges to choose from–sustainability, games, health and inclusivity. Now you can be inspired to create machine learning projects with AWS DeepLens and make a difference at the same time! Use these challenges to gain machine learning experience with fun, collaborative, and inspiring projects. In addition, you'll be making a positive impact on improving people's lives and supporting non-profit organizations that benefit our society. You are invited to enter a single challenge or as many of them as you want.