ai writing to human

ai writing to human

The Benefits of AI in Human Communication

1. Introduction

Using insights in the field of human-computer interaction (HCI), including the fields of human-centered computing (HCC), participatory design, and accessibility, and employing qualitative analysis of media data, we sought to extend these literatures by interrogating how AI is introduced to public audiences in technological and rhetorical terms, in abstract rhetoric, in normative governance discourse, and in inciting action through human-AI communications. The identification of specific rhetorical features has been useful to inform new studies in AI rhetoric that should be undertaken, as well as the development of methods to overcome some of the limitations that are associated with the study of massive social discourse with the goal to uncover social phenomena such as homophily or informational cascades.

Machine learning continues to grow in influence in countries such as Canada and the United Kingdom. Researchers have increasingly studied theories in social psychology, communication, and related fields to understand the impact of AI on human communication. More recently, as AI has become a more apparent force in developing countries, research has also begun to explore how AI rhetoric is disseminated, received, and subsequently influences behavior in specific contexts. Researchers are now actively examining how AI is positioned within societal discourse, how automated agents afford conversations that were previously unrealizable, and what social repercussions are a result of using increasingly sophisticated AI systems to remediate communicative tasks. These studies frequently attempt to define or classify social theories of AI or suggest experimental designs that may unmask rhetorical functions of the technology.

2. Enhanced Efficiency and Accuracy

Since real-world problems are often uncertain and related to noise, and decisions often provide incomplete information, stability information is important. AI-based decision and data analysis methods may change decision-making strategies due to a large number of observations and may offer potential string choice options. Additionally, from the perspective of managing the complex signal structure of algorithms for behavioral analysis, reinforcement learning used in complex decision making, in continuous time, multiple states, and uncertain dimensions used to learn as a human actor may selectively improve flexibility. As a result, combining these learning methods with human expertise may significantly improve the reliability of decision making.

Several concerns have been raised about the potential negative consequences of the use of AI in human communication and perception. However, these systems can generally assist and improve communication by processing and structuring large volumes of data in a computationally efficient and accurate manner. Additionally, AI achieves reasonably high levels of accuracy, making its potential use in communication in the near future great. Recently, there has been significant speed in algorithm development, particularly in the field of machine learning. The most advanced AI is guaranteed to be within the error range when used for many things. AI is expected to improve the accuracy of information conversions between individuals, especially in cases such as translation of various languages, where the definition of figurativeness in the information content or emotional expression can make it difficult.

3. Improved Customer Experience

Applications that reduce processing times for applications help to streamline processes, improve productivity, and workflow of the operation as a whole. For example, manual mortgage/loan application processing can be time-consuming, error-prone, and includes duplication of manual effort for tasks such as assessing applicant creditworthiness. If repetitive tasks in the process, such as reviewing customer documents and filling long forms, can be avoided (or sped up), mortgage applications can be approved in days instead of weeks. Although AI with its current (or futuristic) capabilities cannot process high-touch and complex mortgage origination tasks, application processing systems can certainly leverage NLP/NLU techniques to extract information about standard mortgage sections, terms and conditions from lengthy printed documents and fill standard loan application forms to remove duplication of effort.

Faster and easier transactions without human touch-points can actually improve customer experience, especially in areas such as biometric identification. This reduces the need for filing lengthy forms to prove identity and provides information about products and services. They also open up the opportunity to reduce ongoing costs for the business associated with customer support. A large proportion of customer service queries (and complaints) relate to product information, shipping status, and company policies. If the queries are resolved without the need for human customer service representatives and with zero waiting time, the customer experience is better, and there is a significant cost reduction for the business.

4. Personalized Recommendations and Assistance

In our research, nearly 40% of people who are impacted by AI think the most important benefit is personalized recommendations and assistance. In another research, a little more than a third of the users felt that recommendation was the most important feature. Is it any wonder that many big brands are sprinting and establishing dedicated teams for their AI and personalization efforts? Many brands/companies/platforms are investing in building personalized recommendation engines. Netflix spent a boatload of money on their recommendation engine. Amazon and Microsoft didn’t remain far behind. With a data of over a decade, Amazon’s recommendation engine is close to a real-time recommendation system. Then there are smaller companies and startups that build recommendation engines. Nanorep does AI for all customer channels and ensures a seamless experience with personalized, proactive recommendations. We also ensure straight-through automation when dealing with low/medium complexity queries. Depict.ai does AI on the creative side. It is all about providing what interests and segues the creative side of humans.

Users seem to be in love with personalized recommendations and assistance. It’s one of the main strengths of AI. A few years back, Amazon’s recommendation system was responsible for 29% of its sales! Let that number sink in… #mindblown. Even outside the retail segment, personalized recommendations and assistance work. I keep going back to my favorite barista at Starbucks, and more often than not, my favorite hot beverage is waiting for me before I even order it. That’s the magic of AI. It harnesses the power of data. An AI system becomes better with more use and more data.

5. Ethical Considerations and Future Implications

Although important, consideration of ethical implications is difficult to resolve ex ante and generally slow to take hold in regulation. Despite the majority of tech firms including the information about a moral agreement toward AI development in company bylaws, ethical considerations are dwarfed in the business world by economic concerns; hence, the slow progression of legal oversight. Moreover, recent political funding decisions in the USA highlighted the need to improve business understanding of ethical implications, particularly as they relate to autonomous political communications and media manipulation. While the political climate continually evolves and ethical decisions have yet to be resolved, the current usage of AI empowers some groups over others. Ethical AI communication cannot therefore occur without a strong understanding of and, in some cases, limitations of the technology. Future consideration will implement and satisfy an evolving series of best practice standards shaped by multidisciplinary scholars; research and empirical investigations should provide the insight and foundations necessary for building a singular moral understanding. Such a standard will help rectify various challenges—present and potential—related to AI communication employing automation and augmentation.

Consideration of ethical implications associated with AI in communication is essential. Much early communication scholarship focused on the idea of technological determinism and its impact on the communicative process. This position argued that the development and advancement of technology will determine social and cultural realities. Therefore, the assumption that AI will continue its evolutionary trajectory places into question human independence, control, and autonomy. Given its potential impact on communication and decision making, particularly in mass communication, scholars have raised a series of moral, ethical, legal, political, and posthuman concerns about both AI (in all its forms) and people that engage with the technology.