Large language models (LLMs) can produce convincing'fake text' in domains such as academic writing, product reviews, and political news. Many approaches have been investigated for the detection of artificially generated text. While this may seem to presage an endless'arms race', we note that newer LLMs use ever more parameters, training data, and energy, while relatively simple classifiers demonstrate a good level of detection accuracy with modest resources. To approach the question of whether the models ability to beat the detectors may therefore reach a plateau, we examine the ability of statistical classifiers to identify'fake text' in the style of classical detective fiction. Over a 0.5 version increase, we found that Gemini showed an increased ability to generate deceptive text, while GPT did not. This suggests that reliable detection of fake text may remain feasible even for ever-larger models, though new model architectures may improve their deceptiveness.

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) and Artificial Intelligence (AI), unlocking unprecedented capabilities. This rapid advancement has spurred research into various aspects of LLMs, their text generation & reasoning capability, and potential misuse, fueling the necessity for robust detection methods. While numerous prior research has focused on detecting LLM-generated text (AI text) and thus checkmating them, our study investigates a relatively unexplored territory: portraying the nuanced distinctions between human and AI texts across text segments. Whether LLMs struggle with or excel at incorporating linguistic ingenuity across different text segments carries substantial implications for determining their potential as effective creative assistants to humans. Through an analogy with the structure of chess games-comprising opening, middle, and end games-we analyze text segments (introduction, body, and conclusion) to determine where the most significant distinctions between human and AI texts exist. While AI texts can approximate the body segment better due to its increased length, a closer examination reveals a pronounced disparity, highlighting the importance of this segment in AI text detection. Additionally, human texts exhibit higher cross-segment differences compared to AI texts. Overall, our research can shed light on the intricacies of human-AI text distinctions, offering novel insights for text detection and understanding.

As AI advances in text generation, human trust in AI generated content remains constrained by biases that go beyond concerns of accuracy. This study explores how bias shapes the perception of AI versus human generated content. Through three experiments involving text rephrasing, news article summarization, and persuasive writing, we investigated how human raters respond to labeled and unlabeled content. While the raters could not differentiate the two types of texts in the blind test, they overwhelmingly favored content labeled as "Human Generated," over those labeled "AI Generated," by a preference score of over 30%. We observed the same pattern even when the labels were deliberately swapped. This human bias against AI has broader societal and cognitive implications, as it undervalues AI performance. This study highlights the limitations of human judgment in interacting with AI and offers a foundation for improving human-AI collaboration, especially in creative fields.

Did AI write this article? No--you'd be able to tell. Tools like ChatGPT are notorious for writing text that sounds robotic, repetitive, and just plain awkward. If you've ever had it write your emails or essays (we won't tell), you already know how bad it is. But you might be able to fool some people with this tool that writes realistic AI text.

While extensive research has focused on ChatGPT in recent years, very few studies have systematically quantified and compared linguistic features between human-written and Artificial Intelligence (AI)-generated language. This study aims to investigate how various linguistic components are represented in both types of texts, assessing the ability of AI to emulate human writing. Using human-authored essays as a benchmark, we prompted ChatGPT to generate essays of equivalent length. These texts were analyzed using Open Brain AI, an online computational tool, to extract measures of phonological, morphological, syntactic, and lexical constituents. Despite AI-generated texts appearing to mimic human speech, the results revealed significant differences across multiple linguistic features such as consonants, word stress, nouns, verbs, pronouns, direct objects, prepositional modifiers, and use of difficult words among others. These findings underscore the importance of integrating automated tools for efficient language assessment, reducing time and effort in data analysis. Moreover, they emphasize the necessity for enhanced training methodologies to improve the capacity of AI for producing more human-like text.

In recent years, there has been a rapid proliferation of AI-generated text, primarily driven by the release of powerful pre-trained language models (PLMs). To address the issue of misuse associated with AI-generated text, various high-performing detectors have been developed, including the OpenAI detector and the Stanford DetectGPT. In our study, we ask how reliable these detectors are. We answer the question by designing a novel approach that can prompt any PLM to generate text that evades these high-performing detectors. The proposed approach suggests a universal evasive prompt, a novel type of soft prompt, which guides PLMs in producing "human-like" text that can mislead the detectors. The novel universal evasive prompt is achieved in two steps: First, we create an evasive soft prompt tailored to a specific PLM through prompt tuning; and then, we leverage the transferability of soft prompts to transfer the learned evasive soft prompt from one PLM to another. Employing multiple PLMs in various writing tasks, we conduct extensive experiments to evaluate the efficacy of the evasive soft prompts in their evasion of state-of-the-art detectors.

AI technology is quickly creeping into every industry, prompting new questions about whether online content comes from a human or a computer. Chatbots can write a story for you in seconds, image generators can produce high quality photos by being given just a few words, tools are out there that can clone a voice with just a few minutes of recorded video. AI technology is everywhere, and now converting text into video content is another technology becoming increasingly popular. Lots of different companies have AI software to convert text to video. It is commonly used as a way to create professional-looking content quickly.

ChatGPT, the internet-famous AI text generator, has taken on a new form. Once a website you could visit, it is now a service that you can integrate into software of all kinds, from spreadsheet programs to delivery apps to magazine websites such as this one. Snapchat added ChatGPT to its chat service (it suggested that users might type "Can you write me a haiku about my cheese-obsessed friend Lukas?"), and Instacart plans to add a recipe robot. They will be weirder than you might think. Instead of one big AI chat app that delivers knowledge or cheese poetry, the ChatGPT service (and others like it) will become an AI confetti bomb that sticks to everything.

If you want to convert blog article to video, then this blog is for you. In this article you will learn about two websites that you can use to create video with text, and add voice-over to video. However, with so many options available, choosing the right software for artificial intelligence video creation can be overwhelming. Fliki allows users to transform text-based content into videos with professional-grade voiceovers. One of Fliki's key strengths is its user-friendly interface, making it accessible to non-professionals looking to create high-quality video content.

What's happened: Runway, the generative AI startup that co-created last year's breakout text-to-image model Stable Diffusion, has released an AI model that can transform existing videos into new ones by applying styles from a text prompt or reference image. What it does: In a demo reel posted on its website, Runway shows how the model, called Gen-1, can turn people on a street into claymation puppets, and books stacked on a table into a cityscape at night. Other recent text-to-video models can generate very short video clips from scratch, but because Gen-1adapts existing footage it can produce much longer videos. Why it matters: Last year's explosion in generative AI was fueled by the millions of people who got their hands on powerful creative tools for the first time and shared what they made, and Runway hopes Gen-1 will have a similar effect on generated videos. Last week, OpenAI unveiled a tool that can detect text produced by its AI system ChatGPT.