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The Struggle for Authenticity in AI-Generated Content

So, you're scrolling through your social media feeds and stumble upon an interesting article. The headline grabs your attention right away, and as you start reading through the opening paragraph, something feels off. The sentences seem awkward and stilted, the ideas cliched and unoriginal. By the end of the second paragraph, it hits you - AI wrote this.

As AI systems get better at generating long-form content, from blog posts to news articles to even full books, we'll likely encounter more and more machine-made writing pieces without realizing it. While AI can churn out high volumes of coherent and grammatically correct text, what's still lacking is a sense of authenticity.

Real human authors have a voice, a style, and a way of connecting with readers that AI has yet to achieve. For now, we're still struggling with how to make AI-generated content sound and feel real - but someday, we may not be able to tell the difference at all. When that day comes, we'll have some hard questions about truth, creativity, and being human.

Originality and Creativity

Generating truly original content has been an ongoing struggle for AI. Sure, bots can churn out blog posts, social media updates, and product descriptions on demand, but producing something profoundly creative or deeply meaningful? Not so much.

AI systems today are trained on massive datasets of human-created content. They learn by analyzing word patterns, language structures, and concept relationships. The problem is they can only recombine elements from what they've been exposed to. They don't have human intuition, emotional intelligence, or life experiences to draw from.

Some companies are trying to address this by feeding their AI huge volumes of more diverse data, like books, movies, music, and art. The idea is the more knowledge and cultural context an AI has, the more creative it can be. Others focus on instilling AI with more human-like qualities, such as emotion, imagination, and abstract thinking.

Contextual Understanding

For AI to generate authentic and meaningful content, it needs to have a strong grasp of context. Content can seem disjointed, irrelevant, or just plain odd without understanding the context surrounding the topic. As AI systems continue to evolve, improving contextual understanding is key to creating high-quality, valuable content.

AI systems can gain contextual understanding by building knowledge graphs - networks of interlinked information about people, places, events, and concepts.

The more knowledge an AI has in its graph, the better it can link new information to relevant concepts and determine how ideas relate. Some companies invest heavily in constructing broad, multifaceted knowledge graphs to give their AI systems a strong, contextual foundation.

Domain Expertise and Accuracy

AI systems today are trained on massive datasets to gain domain expertise and generate accurate content. However, the knowledge and data used to train language models have limitations. The datasets themselves can reflect and even amplify the biases of their human creators.

AI models also struggle with keeping up to date in a fast-changing world. The training data is fixed at a point in time but the world continues to change, so the model's knowledge becomes outdated. Models would need to be continually retrained on the latest data to overcome this, which is challenging and computationally expensive.

AI systems are narrow in scope and cannot match human intelligence and life experiences. They operate based on patterns in data to generate new content but cannot truly understand what they are writing about in a human sense. The content may be coherent and fluent but lacks the depth of reasoning, emotional intelligence, and imagination.

Legal and Intellectual Property Issues

When AI systems generate content, questions arise about who owns the copyright and intellectual property. If an AI creates a work of art, article, or invention, who owns it? The company that created the AI? The researchers? The public? This issue will become increasingly complex as AI systems become more advanced and autonomous.

For now, most experts agree that the company or researchers that develop an AI system likely own the rights to anything it produces. However, as AI generates more complex, creative works, our laws may need to evolve. Some argue AI should own rights to their creations, while others say AIs are tools and cannot own property. There are also concerns about companies or individuals claiming ownership of public data used to train AIs.

AIs that generate text or media can unintentionally plagiarize or mimic the style of their training data. An AI may paraphrase or combine elements from multiple sources, making plagiarism hard to detect. They may also adopt biases and flaws in their datasets. Researchers must ensure AIs properly trained on diverse, high-quality data to avoid these issues.

As technology advances, so does the sophistication of algorithms and machine learning models. But human creativity, emotional intelligence, and life experiences that shape personal voice are hard to replicate. While AI will improve at mimicking these qualities, we must consider if that's what we really want.

Stay close. Iris Mar Sol is launching an AI writing and prompt creation course in the upcoming days, that will help you learn how to work with any AI tool available online, and create content that can really set you apart from the rest of your competitors who are just feeding up words into the machine.


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