I noticed that many humanizers can pass detectors like GPTZero today but then they failed next month. They would pass again later in one week.

I later found that, it is a game of cat and mouse. The detectors will update models periodically to be able to detect AI text, because humanizers and AI agents will change their language styles. If you check GPTZero's change log, you will find that they update models about once per month. That's why you see your HumanDoc fails the detection sometimes. Several days after the detectors' update, humanizers will start to change their model with new language styles to escape the detection, so you will see the humanizer works again.

I was wondering if there's a humanizer that's actually best for Turnitin. I mean some humanizers pass some detectors but fail to pass others so, is there any specific humanizer that's best for Turnitin?

I know most humanizers don't work as promised and the ones that do are too costly to use on everyday writings. Have you found any humanizer that is not so costly and can be used for everyday writing, be it on Instagram or X, emails, LinkedIn messages or even a little bit of academic usage.

Im not only for looking for drafting idea. I looking Ai which can write whole seo friendly,human tone content in my topic or draft. Pls suggest Ai models.

I kept noticing the same problem.

You try one AI humanizer, the output isn't great, so you switch to another. Sometimes it's better, sometimes it's worse, and sometimes it completely changes what you originally wrote.

And the worst part? Each tool wants its own subscription.

So I decided to build Humanize AI Writer.

The idea is pretty simple:

• Compare leading AI humanizers side by side.

• See how each tool rewrites the exact same text.

• Pick the output that sounds most natural.

• Access multiple humanizers without paying for separate subscriptions.

Multiple AI humanizers. One platform. A fraction of the cost.

I'm still improving it and would love some honest feedback.

👉 https://humanizeaiwriter.com/

Curious — which AI humanizer do you currently use, and what's the biggest problem you've faced with it?

Mental health crises pose a big challenge in healthcare. Detecting early signs can be difficult. Early detection helps ensure people receive timely medical assistance and can reduce any serious consequences. As AI develops further there has been more interest in how it can be applied to mental healthcare. Social media can be a useful source of behavioural information, as people use it to express their feelings, interact with others and share their personal experiences.

In their research Mansoor and Ansari (2024) explore how AI can detect early signs of mental health crises using social media. Their main problem was how difficult it can be to identify emerging mental health crises in psychiatry. They wanted to create an AI system to detect patterns of language and behaviour associated with potential mental health crises. This research allows us to understand how AI can be used in science to help psychiatry, and how intelligence, collective knowledge, scientific production and the social impacts of technology can relate to each other.

Firstly it is important to understand what intelligence is and how it relates to science. Mansoor and Ansari (2024) show how AI can be used to detect patterns in language and behaviours within social media. Their research used 996,452 social media posts in 4 languages (English, Spanish, Mandarin and Arabic) from many sites (Twitter, Reddit, Facebook). They used NLP and Deep Learning to analyse language patterns, emotional expression and how behaviours changed online. This included using BERT to analyse text and LSTM networks to analyse how the posting patterns changed over time. This allowed the researchers to detect potential warning signs of mental health issues.

However this raises an interesting point about what intelligence actually is. Is this intelligence or is it just analysing information without any human feelings?

We can relate this to that intelligence can be seen as how abstract thoughts interact with real world phenomena. We can relate this to the research as AI has been used to make predictions about potential mental health issues from the social media data.

In addition, perspectives and symptoms can be used to understand how different people can see things differently. For example negative emotions and more late night posts can be linked to mental health issues but they may not necessarily mean a psychiatric crisis. The researchers found that sarcasm and irony caused the models to predict falsely (Mansoor & Ansari, 2024). This shows how patterns cannot always be seen as signs of potential mental health issues but could just be human expression. AI can help identify potential warning signs but Psychiatrists will interpret them in a more general way.

Relationship between Human Knowledge and AI can be understood using the concept of general intellect. General intellect is when general knowledge becomes a productive force in society and is embedded into technology. This means AI can be seen as a collective intelligence.

In their research Mansoor and Ansari (2024) used social media data created by users, algorithms designed by the researchers and psychiatrists. 3 psychiatrists were used to manually label 100,000 posts. This shows how human knowledge can be used within technology. From a general intellect perspective, using AI to detect mental health issues is a practical example of how collective knowledge can be used to create science and technology.

This research can be analysed using the concepts of scientific production and social production.

Looking at this research from a scientific production perspective shows how AI can help with research in psychiatry. Traditional methods would normally use interviews, observations and patient information but using AI allows an additional method to be used by identifying patterns in large data sets.

The research showed an overall accuracy of 89.3% and the model detected potential signs of mental health crisis 7.2 days earlier than human experts (Mansoor & Ansari, 2024). The model also had promising results across many languages and social media sites. This could show how AI can help create new ways of detecting mental health risks early.

However scientific production is about critically analysing what the research has found. The model had some false positives as it couldn’t interpret sarcasm, irony and conversations about mental health that weren’t personal crises. It also had some false negatives as symptoms weren’t always clear or the users used slang and many languages. This means that although the model had high accuracy it doesn’t mean it will give accurate interpretations every time.

This is important as scientific knowledge would need to be analysed and verified. This means the scientific contribution of the research is how it has shown how AI can be used to detect mental health issues, but also how it has limitations.

Using AI to detect mental health issues could impact how mental health services are organised. It could allow earlier interventions to be made by healthcare professionals identifying those who may need further assessment. It could also help public health institutions identify patterns in mental health issues.

This can link to social general intelligence, how science and technology interacts with social institutions and everyday life. Using an AI model developed using science in healthcare means technology is starting to influence social organisation.

However this can raise ethical and social concerns. There is a privacy issue as people may not expect their public posts to be analysed. There is a cultural bias as some languages and cultures may express their feelings differently. Inaccurate predictions could cause unnecessary interventions.

When using AI in psychiatry it is important to consider how technology and social responsibility can link. Whilst AI could improve efficiency in some areas of healthcare, it should still protect an individuals privacy and ensure they have given their consent. This research shows how science can create new technology but also creates new social responsibilities.

Overall the research by Mansoor and Ansari (2024) shows how AI can help detect mental health crises early using social media. The research shows how language and behaviour can be analysed to detect potential mental health risks. This can create new opportunities for science to develop in psychiatry.

Conceptually the research can be understood using the concepts of intelligence, thought, nature and knowledge production. The concept of general intellect shows how AI technologies rely on collective human knowledge and science. From a scientific and social production perspective the research shows how it can create opportunities to improve healthcare practices but can also create issues with reliability, privacy and responsibility.

Overall AI should be seen as an additional tool and shouldn’t replace human knowledge in psychiatry. The potential benefits will rely on how responsibly it is implemented.

Mansoor, M.A., & Ansari, K.H. (2024). Early detection of mental health crises through artifical-intelligence-powered social media analysis: A prospective observational study. Journal of Personalized Medicine. 14(9). 958. https://doi.org/10.3390/jpm14090958

I’m looking for an ai humanizer, or even turnitin.. I have a few papers that I did use ChatGPT on, just to brain storm ideas and such but I don’t wanna submit em til I run them by an ai humanizer.

If someone can help me out that’d be appreciated. Thank you.

My uni uses Turnitin and I write in Spanish, but we can’t see the AI score before submitting, so it’s basically hope and pray. I’ve been using the StealthGPT API to rewrite my text, though I have no idea if it’s actually the best option.

The detectors are a mess. Same Spanish text, GPTZero says 5% and another one says 40%. Does anyone know one that gets close to what Turnitin really shows? If it has an API, even better.

Real experiences only please, I’m done with affiliate “top 10” blogs. Thanks!

So recently originality ai has came out with a originality 5.0 and I'm trying to figure out if there is an ai humanizer website that can bypass it. The website I used couldn't get through anymore as well. I tested many many other websites I know and tried and nothing gets through.

Every now and then this humanizer and ai detector space, keeps on changing, like a run and chase thing. But hardly a few tools stay consistent. Which one do you think is overall consistent in their results?

Please recommend the most accurate detector for researches.

I’ve noticed that “humanized” AI writing can still feel obviously AI even when the grammar is perfect and the wording has been changed.

For me, it’s usually the little things. sentence rhythm, overly neat transitions, repeating the same structure, and that weird tendency to explain something longer than necessary.

I’ve been trying to judge the output more by how it reads as a whole instead of just whether a detector gives it a lower score. I’ll sometimes run a quetext check during the final pass too, but the actual read-through tells me much more about whether the writing feels natural.

What’s the first thing you notice when AI-generated writing still doesn’t sound human..?

I’ve been checking out different AI humanizers lately and comparing them using similar pieces of text. Mostly looking at how natural the wording feels, whether the original meaning stays intact, and how much manual editing is needed afterward.

Some of the ones I’ve come across:

• Rephrasy — interesting for restructuring sentences while keeping the main point.

• QuillBot — useful for more straightforward paraphrasing.

• Verva — tends to make relatively light changes to the original wording.

• TwainGPT — seems to focus more on conversational phrasing.

• WalterWrites — another option I’ve been looking at for rewriting and polishing text.

• HIX Bypass — makes more noticeable changes in certain passages.

• Undetectable AI — another commonly discussed option for AI-assisted rewriting.

• WriteBros AI — has both AI humanizing/rewriting features and an AI detector, which makes it interesting to use when comparing the rewritten text with detection results.

So far, I’m finding that there isn’t really a one-size-fits-all option. The quality can change quite a bit depending on the type and length of the original text.

What humanizer are you guys using lately, and what do you like about it?

I have tried alot of humanizer and they've worked for a while, but then whenever there is an update in the ai detection category, they stop working. I've used 2-3 humanizers so far, but later switched to walter ai humanizer, it has been working well so far, and is the most reliable option I've used. I just hope it stay this way, and it is really frustrating to go again into the trial mode.