Keyword density helper – This tool comes with a built-in keyword density helper in some ways similar to the likes of SurferSEO or MarketMuse the difference being, ours is free! This feature shows the user the frequency of single or two word keywords in a document, meaning you can easily compare an article you have written against a competitor to see the major differences in keyword densities. This is especially useful for SEO’s who are looking to optimize their blog content for search engines and improve the blog’s visibility.
File compare – Text comparison between files is a breeze with our tool. Simply select the files you would like to compare, hit “Upload” and our tool will automatically insert the content into the text area, then simply hit “Compare” and let our tool show you where the differences in the text are. By uploading a file, you can still check the keyword density in your content.
Comparing text between URLs is effortless with our tool. Simply paste the URL you would like to get the content from (in our example we use a fantastic blog post by Sherice Jacob found here) hit “Submit URL” and our tool will automatically retrieve the contents of the page and paste it into the text area, then simply click “Compare” and let our tool highlight the difference between the URLs. This feature is especially useful for checking keyword density between pages!
You can also easily compare text by copying and pasting it into each field, as demonstrated below.
Ease of use
Our text compare tool is created with the user in mind, it is designed to be accessible to everyone. Our tool allows users to upload files or enter a URL to extract text, this along with the lightweight design ensures a seamless experience. The interface is simple and straightforward, making it easy for users to compare text and detect the diff.
Multiple text file format support
Our tool provides support for a variety of different text files and microsoft word formats including pdf file, .docx, .odt, .doc, and .txt, giving users the ability to compare text from different sources with ease. This makes it a great solution for students, bloggers, and publishers who are looking for file comparison in different formats.
Protects intellectual property
Our text comparison tool helps you protect your intellectual property and helps prevent plagiarism. This tool provides an accurate comparison of texts, making it easy to ensure that your work is original and not copied from other sources. Our tool is a valuable resource for anyone looking to maintain the originality of their content.
User Data Privacy
Our text compare tool is secure and protects user data privacy. No data is ever saved to the tool, the users’ text is only scanned and pasted into the tool’s text area. This makes certain that users can use our tool with confidence, knowing their data is safe and secure.
Compatibility
Our text comparison tool is designed to work seamlessly across all size devices, ensuring maximum compatibility no matter your screen size. Whether you are using a large desktop monitor, a small laptop, a tablet or a smartphone, this tool adjusts to your screen size. This means that users can compare texts and detect the diff anywhere without the need for specialized hardware or software. This level of accessibility makes it an ideal solution for students or bloggers who value the originality of their work and need to compare text online anywhere at any time.
OpenAI is an artificial intelligence research body that aims to develop and promote user-friendly AI beneficial to humanity. Launched in 2015, OpenAI is backed by Elon Musk, Peter Thiel, Microsoft, Infosys, and other investors, thus raising over $1 billion in funding.
OpenAI has released machine learning and AI products, including:
OpenAI has three major NLP (natural language processing) model releases. They are GPT, GPT-2, and GPT-3. They also have other models apart from those three.
GPT or GPT-1 was the first NLP model released by OpenAI. It was launched in 2018 and trained using a large BooksCorpus dataset. At the release time, GPT performed better than most other trained, supervised models. It could answer questions well and perform sentiment analysis with good zero-shot learning performance.
Announced in 2019, GPT-2 is the direct successor to GPT. GPT-2 contained over ten times the parameters of the original GPT, and it was trained with over ten times the amount of data used for GPT.
GPT-2 was trained using a dataset of 8 million web pages, containing about 1.5 billion parameters. GPT-2 is adept at generating AI text of superb quality, outperforming other NLP models trained on domain-specific datasets.
GPT-2 performed well on language tasks like reading, comprehension, answering questions, summarization, and translation. Needing only a simple human prompt, GPT-2 could generate believable and coherent bodies of text based on human input. It does all these without task-specific training.
But GPT-2 had shortcomings like repetitive sentences in the generated text and poorly generated content on niche topics.
For safety and security reasons, a smaller version of GPT-2 was released to the public. A miniature version of GPT-2 output datasets was also released for researchers.
Announced in 2020, GPT-3 is the latest version of GPT. GPT-3 was trained on over 175 billion parameters compared to 1.5 billion parameters for GPT-2.
In December 2022, OpenAI debuted GPT-3.5 along with ChatGPT. GPT-3.5 is an improved version of GPT-3, but it isn’t a full-fledged independent release. GPT-4 is still expected in the nearest future.
Whisper is an automatic speech-recognition model from OpenAI. Whisper was trained using over 680,000 hours of multilingual data. The NLP model can recognize multilingual speech and automatically identify the correct language. It can also translate speech from multiple languages.
Whisper is adept at recognizing speech despite accents and background noise. It was trained using a diverse dataset, with a third of the dataset comprising of non-English speech.
Whisper is already being used in applications for faster, direct YouTube search that jumps straight to a relevant video segment using speech recognition as opposed to just searching using the video name and description.
In early 2022, OpenAI released InstructGPT. InstructGPT models are better at following human instructions than GPT-3. GPT-3 and other NLP language models require prompt engineering to generate outputs needed by users. But InstructGPT can follow instructions more accurately without generating untruthful or harmful outputs.
Built on GPT-3.5, ChatGPT is an AI chatbot that generates responses to human prompts. It can write essays, write code, solve mathematical problems, write business plans, translate text and conduct a whole lot of tasks.
ChatGPT is currently OpenAI’s most popular AI product. ChatGPT brought in over 1 million users within just five days. To put this into context, it took GPT-3 two years to gain 1 million users. The estimated number of ChatGPT users is 100 Million
A significant limitation of ChatGPT is that it was trained using a 2021 dataset. So, the bot is limited in giving answers to queries about events that happened after 2021.
GPT-3 provides a significant performance boost over GPT-2. The model provides vastly improved performance from GPT-2, especially for generating text for niche topics. It can also generate computer code. GPT-3’s ability to generate computer code has been used for ChatGPT, Codex, and other AI applications.
Then there’s GPT-3.5, a more refined version of GPT-3. GPT 3.5 is good at writing witty, humane text while adapting to the tone and mannerisms of any popular human.
You could tell it to respond like a child, a popular politician, or a musician. It could write poems, write code, and solve mathematical problems. It can write essays, compose emails, take tests, manipulate data, play games, and explain complex things. It can even write a complete business plan with relevant case studies.
A slight criticism of GPT-3.5 is its verbosity compared to earlier models. It tends to generate unnecessarily lengthy responses to prompts. While this might be unnecessary for the average user, it is helpful for writers and those who need AI help for essays and blog posts.
Many users are now building apps based on ChatGPT/GPT3.5. For example, Addy is an AI email assistant that can write emails 10x faster according to your preferred tone and style.
OpenAI API is based on GPT-3. The API grants developer access to OpenAI models like GPT-3, DALL-E, and Codex. Developers can use the API to build intuitive applications.
Codex is a top-level AI code generator. Based on GPT-3, Codex was trained using natural language and several billion lines of code.
Codex can turn simple human input into code, complete lines of code, rewrite codes, and add comments to written code. You can even tell Codex to find useful APIs and libraries for you.
Thanks to its large library of coding languages, you can use Codex to build a wide variety of no-code apps. Codex can translate simple text in English to Python, SQL, JavaScript, HTML, Swift, Typescript, C#, Perl, Shell, Go, etc.
Codex is flexible with syntax and style. It works even better when you specify the language version you want. Codex can provide helpful suggestions when importing libraries and APIs. But you need to be careful with this because sometimes the suggestions might not work optimally for whatever you are building.
Long completion requests in Codex can lead to errors and repetition. Be as specific as possible when using Codex, and you will get impressive results. You can also set stop tokens to limit query sizes. You can use the comment function to explain difficult codes by starting an explanation comment and letting Codex complete the explanation for you.
Nothing concrete has been announced about GPT-4. But industry rumors predict a potential release date of 2023. GPT-3 had 175 billion parameters. If GPT-4 is 10x larger as expected, it will have over 1 trillion parameters. And it will be trained on significantly larger data than GPT-3. A fine-tuned version of ChatGPT might also be released along with GPT-4.
That said, back in 2021, OpenAI disbanded its robotics team. So, don’t expect to see OpenAI’s neural network and NLP models in physical robots so soon. But things can change, especially if OpenAI partners with a robotic company. OpenAI has achieved a lot within seven years. The future is ripe with possibilities for the organization.
What are GPT models?
GPT or Generative Pre-trained Transformer models are AI Neuro-linguistic programming model that uses deep learning to interpret and produce human text. These GPT models were trained using large text datasets.
Is GPT-3 an NLP?
Yes, GPT-3 is an NLP (Natural Language Processing) model.
What model does GPT-3 use?
GPT-3 uses its own language model trained using hundreds of billions of words.
Is GPT-3 based on BERT?
No, GPT-3 isn’t based on BERT. Neither are GPT-2 and GPT. BERT, or Bidirectional Encoder Representations from Transformers, is an NLP model from Google.
Is GPT better than BERT?
While GPT is more popular, with more use cases than BERT, BERT does have the unique advantage of being bidirectional, which means that it can read text both from left-to-right and right-to-left for better language context. BERT is also open-source, unlike GPT.
What is the difference between BERT and GPT?
BERT is an encoder-only model, while GPT is a decoder-only model that uses transformer encoder blocks. BERT was created by Google, while OpenAI created GPT. GPT was trained using a larger dataset than BERT. BERT is bidirectional, while GPT is not. BERT is open source, while GPT is not.
What dataset is GPT-3 trained on?
GPT-3 is trained on a wide variety of data, including Wikipedia, Common Crawl, books, and web texts. It was trained on hundreds of billions of words.
No, that’s one of the benefits, only fill out the areas which you think will be relevant to the prompts you require.
When making the tool we had to make each prompt as general as possible to be able to include every kind of input. Not to worry though ChatGPT is smart and will still understand the prompt.
Originality.ai did a fantastic job on all three prompts, precisely detecting them as AI-written. Additionally, after I checked with actual human-written textual content, it did determine it as 100% human-generated, which is important.
Vahan Petrosyan
searchenginejournal.com
I use this tool most frequently to check for AI content personally. My most frequent use-case is checking content submitted by freelance writers we work with for AI and plagiarism.
Tom Demers
searchengineland.com
After extensive research and testing, we determined Originality.ai to be the most accurate technology.
Rock Content Team
rockcontent.com
Jon Gillham, Founder of Originality.ai came up with a tool to detect whether the content is written by humans or AI tools. It’s built on such technology that can specifically detect content by ChatGPT-3 — by giving you a spam score of 0-100, with an accuracy of 94%.
Felix Rose-Collins
ranktracker.com
ChatGPT lacks empathy and originality. It’s also recognized as AI-generated content most of the time by plagiarism and AI detectors like Originality.ai
Ashley Stahl
forbes.com
Originality.ai Do give them a shot!
Sri Krishna
venturebeat.com
For web publishers, Originality.ai will enable you to scan your content seamlessly, see who has checked it previously, and detect if an AI-powered tool was implored.
Industry Trends
analyticsinsight.net
Tools for conducting a plagiarism check between two documents online are important as it helps to ensure the originality and authenticity of written work. Plagiarism undermines the value of professional and educational institutions, as well as the integrity of the authors who write articles. By checking for plagiarism, you can ensure the work that you produce is original or properly attributed to the original author. This helps prevent the distribution of copied and misrepresented information.
Text comparison is the process of taking two or more pieces of text and comparing them to see if there are any similarities, differences and/or plagiarism. The objective of a text comparison is to see if one of the texts has been copied or paraphrased from another text. This text compare tool for plagiarism check between two documents has been built to help you streamline that process by finding the discrepancies with ease.
Text comparison tools work by analyzing and comparing the contents of two or more text documents to find similarities and differences between them. This is typically done by breaking the texts down into smaller units such as sentences or phrases, and then calculating a similarity score based on the number of identical or nearly identical units. The comparison may be based on the exact wording of the text, or it may take into account synonyms and other variations in language. The results of the comparison are usually presented in the form of a report or visual representation, highlighting the similarities and differences between the texts.
String comparison is a fundamental operation in text comparison tools that involves comparing two sequences of characters to determine if they are identical or not. This comparison can be done at the character level or at a higher level, such as the word or sentence level.
The most basic form of string comparison is the equality test, where the two strings are compared character by character and a Boolean result indicating whether they are equal or not is returned. More sophisticated string comparison algorithms use heuristics and statistical models to determine the similarity between two strings, even if they are not exactly the same. These algorithms often use techniques such as edit distance, which measures the minimum number of operations (such as insertions, deletions, and substitutions) required to transform one string into another.
Another common technique for string comparison is n-gram analysis, where the strings are divided into overlapping sequences of characters (n-grams) and the frequency of each n-gram is compared between the two strings. This allows for a more nuanced comparison that takes into account partial similarities, rather than just exact matches.
String comparison is a crucial component of text comparison tools, as it forms the basis for determining the similarities and differences between texts. The results of the string comparison can then be used to generate a report or visual representation of the similarities and differences between the texts.
Syntax highlighting is a feature of text editors and integrated development environments (IDEs) that helps to visually distinguish different elements of a code or markup language. It does this by coloring different elements of the code, such as keywords, variables, functions, and operators, based on a predefined set of rules.
The purpose of syntax highlighting is to make the code easier to read and understand, by drawing attention to the different elements and their structure. For example, keywords may be colored in a different hue to emphasize their importance, while comments or strings may be colored differently to distinguish them from the code itself. This helps to make the code more readable, reducing the cognitive load of the reader and making it easier to identify potential syntax errors.
With our tool it’s easy, just enter or upload some text, click on the button “Compare text” and the tool will automatically display the diff between the two texts.
Using text comparison tools is much easier, more efficient, and more reliable than proofreading a piece of text by hand. Eliminate the risk of human error by using a tool to detect and display the text difference within seconds.
We have support for the file extensions .pdf, .docx, .odt, .doc and .txt. You can also enter your text or copy and paste text to compare.
There is never any data saved by the tool, when you hit “Upload” we are just scanning the text and pasting it into our text area so with our text compare tool, no data ever enters our servers.
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This table below shows a heat map of features on other sites compared to ours as you can see we almost have greens across the board!
