How AI Increases Web Accessibility

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Artificial Intelligence (AI) is an ever-growing technology that allows web users to receive much information and facilitate life using elaborate algorithms. What once was thought to be science fiction about the role of cybernetics now is an inevitable reality. AI-powered innovations have changed multiple vehicles, devices, and other equipment in every sphere of human activity, but most importantly, they influenced the digital world (Abou-Zahra et al., 2018). They impacted the web developers and their users by providing a range of products and services that alleviate net surfing. In particular, artificial intelligence made it possible for impaired people by creating summarization, image, and voice recognition. Even though AI technologies can make the web more accessible to disabled people with the help of assistive technologies, they also have several imperfections.

An essential part of network availability is the versatility of the content per the needs and inclinations of individual customers. This can be a significant visual change to the content, such as changing the text style, size, and division, to make the content more fundamentally customized. In particular, progress in characteristic language provides several examples of how human-created consciousness can support such a change in substance. AI can calculate peoples needs and preferences and adapt content to them. Nonetheless, the web should also be suitable to disabled peoples intentions; therefore, AI helps them to access the net with special assistive technologies.

Primarily, language recognition technologies based on AI allow website users to translate texts and see captions and subtitles. Many leading companies created platforms for improving captioning and translating because such an approach helps disabled people receive information (Wolf, 2020). Moreover, speech recognition algorithms empower deaf or half-deaf people to use networks adjusting to their needs. Language recognition machines also help emit grammatical, punctuational, and semantical mistakes in the text, allowing users to sound more literate. Some of the innovative organizations also create different interpretations of the language and the subtitles of the disableds answers. As part of its goal of creating a more comprehensive organization, Microsoft has made Microsoft Translator, a human-made innovation of mind-based correspondence for deaf and hard hearing people. Although the design still has some flaws including wrong translation and incorrect subtitles insertion. Nevertheless, it creates numerous opportunities for impaired people to perceive the text.

Another point concerns automatic image recognition in the worldwide nets, such as Instagram or Facebook. The technology was implemented to help blind or half-blind people understand the content of the images presented. For instance, Google developed an algorithm that lets the disabled recognize images and differentiate objects in them; it also sorts the pictures to fall under the safe search category (Thompson, 2018). What is more, this innovation allows describing the photos to visually impaired web users. Therefore, Facebook launched such a tool, which is powered by neural networks (Thompson, 2018). Besides, image identification has been utilized in different domains and received much attention due to its accuracy of algorithms. As a result, multiple visual databases use this tool to organize images automatically. Although technology may be imprecise due to its low level of blurred and group photos recognition, it provides people with an incredible opportunity to identify the pictures content and maintain them in order.

Lip-reading algorithms were also created as part of the invention of artificial intelligence. They allow people with hearing impairments to receive an instant interpretation. For example, Google made a program called DeepMind that analyzed more than 5,000 hours of various TV shows in different languages and tracked lip movements to decipher them (Morris, 2020). As a result, this technology provides real-time speech recognition and translation and decodes it into text with high accuracy. This implementation has several drawbacks including poor recognition of foreign words and misinterpretation of alike words.

Finally, to alleviate users experience of accessing websites, artificial intelligence was implemented to summarize all Internet sources information. Even though the majority of websites contain videos and audio, the text remains a critical component; however, impaired people find it hard to read much information. Therefore, AI-based instruments for text summarizations were created to transform a voluminous article into a couple of paragraphs (Morris, 2020). For instance, this can help break down long and complicated information into several sections for blind and visually impaired users. It means that the technology identifies proper words for compiling and producing an accurate summary. For example, the widely recognized AI-based Salesforce model uses the most innovative tools to transmit critical information. Moreover, it helps people with cognitive issues because it can explain complicated phenomena in simple words without ruining the main idea. In general, information summarization is an effective method of perceiving and learning new ideas and facts despite having difficulties summarizing quantitative research.

To conclude, it seems reasonable to state that artificial intelligence has drastically changed impaired peoples lives by providing access to multiple technologies, especially to the digitally advanced world. Primarily, artificial intelligence helped to translate websites and produce subtitles for the videos and records so that users could see or hear additional information. AI-based innovations allowed impaired or partially disabled users to recognize the content of the images and evaluate them. Finally, it facilitated data perception by summarizing extensive articles and texts. However, it is just the beginning of the innovative technologies invasion into peoples lives.

References

Abou-Zahra, S., Brewer, J., & Cooper, M. (2018). Association for Computing Machinery, 20, 1-4. Web.

Morris, M. (2020). . Communications of the ACM, 63(6), 35-37. Web.

Thompson, P. W. (2018). Artificial intelligence, advanced technology, and learning and teaching algebra. Research Issues in the Learning and Teaching of Algebra: The Research Agenda for Mathematics Education, 4, 135-161. Web.

Wolf, C. (2020). Democratizing AI? Experience and accessibility in the age of artificial intelligence. XRDS, 26(4), 12-15. Web.

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