humanize ai writing

humanize ai writing

The Importance of Humanizing AI Writing

1. Introduction

Furthermore, the rise of fake news and deepfakes makes the identification of reliable sources less straightforward. Without AIW, nobody would write completely neutral content. Instead, we see content paid or sponsored by interest groups to distort the truth. In short, only organizations with a strong incentive to give reliable information to their customers can develop AIW. What is important to discuss, therefore, is not the capability, but rather the ethical necessity of developing an AIW. While some disagree with my definitions and opinions, the end of this post should provide a more philosophical discussion into what is ethical in terms of technology. Nonetheless, my post is about a breakthrough in potential for AI, I will not discuss the technical workflow of AIW.

Are you ready for a digital assistant smart enough to write you great content? Fusing artificial intelligence (AI) and sophisticated writing is a big opportunity. Yet many in the AI and computer science communities have been slow to recognize this potential. AI is usually built domain-specific, working on one niche problem at a time, when in reality AI should be applied flexibly in several relevant domains. Most people generally think of writing as a human task, not a problem that AI can solve. Despite this, content creation is a key task for businesses today, manipulated by influencers, blogs, and social media. Because marketing reach and revenues are linked to the ability to communicate, there’s a desire and a demand for a creative Artificially Intelligent Writer (AIW).

2. Benefits of Humanizing AI Writing

NLG is a type of AI that writes human language based on the data fed to it. Today, it is used to automate routine communications that are shared with customers, be it written reports that summarize a company’s revenue to a librarian’s responses to a child’s search on an online library. In addition, this combined system uses conversational AI (dialogue) to guide the user through a knowledge-based conversation. Finally, chatbots use both AI writing systems and machine learning to write their responses. The benefits of a chatbot system are threefold: it smooths communication between businesses and users, saves resources, and learns through user interactions to improve performance.

Automation is touted as the solution for time-consuming tasks, e.g. automating lead generation to streamline communication between businesses and customers or processing claims statements. The Internet of Things (IoT) is just one example. But humans remain an essential part of the process, as their expertise enables them to manage risk and compliance while processing data generated by IoT devices. Automated decision-making can save time and money while eliminating human error, but some operational tasks still need a human-in-the-loop. Natural language generation (NLG) is a type of artificial intelligence (AI) that writes human language and is capable of contributing to conversations with customers. However, like IoT technology that requires human oversight, AI writing needs human-understandable output to prevent things like fake news.

3. Strategies for Humanizing AI Writing

The first measure and perhaps the minimum value of humanizing text writing (i.e. a formal definition of what humanizing actually is) are provided by Eastwood (2018), where she sets various “steps” that humans can take to create more intelligent AIs. According to her, humanizing AI text writing is “in its current level of development, a significant open problem” (p. 2), since machines do not differentiate language processing on the basis of its origin, but seeing, perceiving, and using written language as the only tool that human beings use is what leads to prerogative, limited, and confused-wise language activities (LeBrun and Lemon, 2019). To analyze humanizing methods for AI text writing, I will follow ukance (198) and briefly summarize a long history of makers’ authors and makers. Lear Ltd., 2000) and analyze AI writing, a complex phenomenon depending on human behavior. For the human-computer tools model and the robot architecture it develops, the models, the conceptions, and the approaches gelling with AI writing should be judged lucidly. But in the future, AI writing will become more human and simpler. Interface tasks can enhance the AI writing capabilities required to contribute multiple abilities and design complexity.

The overall outcome of understanding the elements of how context affects communication can enable us to leverage natural language generation and understanding technology (NLG and NLU). This is a measure to reduce the level of pervasiveness of the negative factors that underline the core issues related to the specific characteristics of human cognizance texting. Also, the field of AI intentions and understanding located outside the specific aspects of word or clause choices. The main goal here, therefore, is to provide AI writing with possibilities to understand the real-world paradigm while it simultaneously engages in complex applications of different kinds of narrative tactics. The ongoing research and development of these new models should therefore provide a better knowledge of how to properly design algorithm processing in a language fully understood by humans. They should be able to test and modify the existing model outputs and understand the short and long-term effects of AI writing on human behavior over time.

4. Challenges in Humanizing AI Writing

An additional concern is the potential for large language models to inadvertently provide harmful information. GPT-3 generated answers have been shown to amplify and reproduce biases present in the data rather than reducing them. Qualitative work has raised concerns about the quality of AI-generated advice and the puzzling political answers given by search engines in different countries. Furthermore, recent studies have shown that factually accurate information from AI can create the illusion of an answer’s representational adequacy and lead to misattributions of knowledge. Prompt engineering thus becomes a high-stakes enterprise: ensuring that AI generates text that is both fluent and human-like, and aligns more closely to the kind of content that experts would want the AI to produce.

Despite advances in training large language models, substantial research challenges remain for the task of humanizing AI writing. Large language models have a high capacity to memorize content from the training data, often leading to being perceived as knowledgeable about the world. However, regardless of how well they perform on fact-based benchmarks (e.g., narrative cloze tasks), they manifest a remarkably shallow understanding of the world (e.g., the word “glucose” is not connected to the word “diabetes” at all and the AI is surprised by glucose-monitoring best practices as noted in). Large language models can effectively generate text about artificial topics that depend more on statistics gleaned from data than on reasoning about the underlying subject matter (e.g., Star Wars reviews, poems, and math problems). Furthermore, generating text that is both fluent and human-like is a challenging and complex task that requires a rich and comprehensive knowledge of many fields. Expertise from various communities is thus essential to addressing the challenges of humanizing AI writing.

5. Conclusion

But offering people reassurance, understanding, proof of accomplishment, and carefully worded instructions will set AI writing paths to become much greater than protocol and rhetoric. Meticulously rendering replies for any given subject or task, to us and for us, means proposing knowledge, comprehension, appeal, and “Variety, I believe, really feeds curiosity and helps drive engagement with content.” A key requirement addressed by Arvind Nagrajan to Cards Against Humanity’s AI, whose design intention was to come up with the most entertaining or interesting insights and anecdotes. Besides ethical considerations, how we’ll actually put it to use, and what purpose AI writing will resolve will govern the ways in which companies will benefit or lose already established ground and opportunity to connect with new audiences. Popular news outlets and private entities such as among others have released recent and well-received AI writing language models that are accessible and yet to present implementations characterizing their genuine writing potential on either short-form or long-form content.

We’ve only begun to explore the content composition potential of AI writing. As it takes on an even bigger part in our work, routines, and personal lives, we’ll be tasked to determine what its role and legitimate place is in driving meaningful, emotional, and informational communication. As AI writing prosthetically extends our ability to turn ideas into articulate, comprehensive, and targeted prose, its rules should be as much moral as they are about principles of programming and good writing. In declaring how we want it to impact our work, and what it should yield, we’ll have to corroborate it extensively and teach those who develop it and use it what constitutes ethical and insightful content creation.