Producing AI Content in Human-Like Style: Man vs. Machine

Challenge #1 - Lack of Creativity:

One of the biggest challenges of producing content via AI is producing content in a human-like style to make it sound natural and authentic. Although NLG models can produce content that is grammatically correct and coherent, they often lack the creativity and originality that comes with human-written content. People tend to use contractions, such as "I'm" instead of "I am," and idioms, such as "a dime a dozen" to express their thoughts and ideas. These elements add personality and nuance to the language, making it more relatable and engaging. However, these linguistic features are often absent in AI-generated content, resulting in a monotonous and robotic tone.

Response: 

AI developers have introduced natural language generation (NLG) models that incorporate contractions, idioms, and other colloquialisms into the generated content. These models learn to recognize and use these features by analyzing vast amounts of human-written content. The use of contractions and idioms, for example, can be learned through the analysis of social media posts, informal texts, and other casual conversations. By incorporating these linguistic features into AI-generated content, NLG models can produce content that sounds more human-like and engaging.

Challenge #2 - Difficulty with Emotive Language:

The use of transitional phrases and interjections help to guide the reader through the text and add a sense of emotion and excitement. Emotive language, such as idioms, interjections, and emoticons, can be difficult for NLG models to understand and use effectively. This can make the content sound robotic or insincere. Transitional phrases, such as "in addition" and "however," help to connect ideas and create a smooth flow of information. Interjections, such as "wow" and "oh no," convey emotions such as surprise, excitement, or disappointment. These elements can be challenging for AI to generate because they often require contextual understanding and creativity. In addition to linguistic features, emotive language is another critical aspect of human-like writing. Emotive language includes words and phrases that convey emotions and feelings, such as "delighted," "heartbroken," and "ecstatic." These words and phrases add a sense of personality and emotion to the writing, making it more relatable and engaging. However, the use of emotive language requires an understanding of the context and the intended audience.

Response: 

To address this challenge, NLG models use sentiment analysis to identify appropriate emotive language to use in the generated content. These models analyze the tone and sentiment of the text to determine the appropriate words and phrases to convey the desired emotion. By incorporating emotive language into the generated content, NLG models can produce content that sounds more expressive and engaging. Setting the temperature to 0.9 is another technique that can be used to produce creative and expressive output. The temperature setting controls the level of randomness in the generated content. A lower temperature produces more predictable and conservative output, while a higher temperature produces more unpredictable and creative output. A temperature of 0.9 is considered high and produces content that is more creative and expressive.

Challenge #3 - Limited Contextual Understanding:

Response: 

To help remedy this, NLG models use algorithms that generate sentences of varying lengths and structures. These models analyze the context and the intended audience to determine the appropriate sentence structure and length. For example, a scientific article may require longer and more complex sentences to convey technical information, while a social media post may require shorter and simpler sentences to engage the audience. However, there is a fine line between creating variation and creating confusion. Too much variation can make the text difficult to follow, while too little variation can make it dull and uninteresting. Therefore, NLG models must strike a balance between variation and clarity to produce content that is both engaging and understandable.