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OpenAI Publications and Papers

We researched Scopus and Google Scholar (leading databases for academic research and publications) to curate a complete list of OpenAI publications and papers.

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Introduction

Our text compare tool is a fantastic, lightweight tool that provides plagiarism checks between two documents. Whether you are a student, blogger or publisher, this tool offers a great solution to detect and compare similarities between any two pieces of text. In this article, I will discuss the different ways to use the tool, the primary features of the tool and who this tool is for. There is an FAQ at the bottom if you run into any issues when trying to use the tool.

What makes Originality.ai’s text comparison tool stand out?

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.

Ways to compare

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.

URL compare

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!

Simple text compare

You can also easily compare text by copying and pasting it into each field, as demonstrated below.

Features of Originality.ai’s Text Compare Tool

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.

We looked at two leading databases for academic research and publications, Scopus and Google Scholar, to curate a complete list of OpenAI publications and papers. The data is organized in an Airtable for convenient analysis.

This list will remain updated as an easy-to-reference location for OpenAI-affiliated publications. 

To learn more about OpenAI, read our OpenAI Partnerships List and OpenAI Patent List.

Overview of Airtable Columns 

The Airtable includes the following columns, each representing critical information about the publications:

  • Year: The year the publication was released.
  • Source Title: The title of the journal or conference where the publication appeared.
  • Title: The title of the publication.
  • Authors: The authors who contributed to the publication.
  • Page Count: The number of pages in the publication.
  • DOI: The Digital Object Identifier, a unique identifier for the publication.
  • Link: A link to the publication (Scopus and/or Google Scholar).
  • Affiliations: The affiliations of the authors.
  • Abstract: A brief summary of the publication.
  • Publisher: The entity that published the journal or conference proceedings.
  • Document Type: The type of document (e.g., article, conference paper).
  • PubMed ID: The PubMed identifier, if applicable.

Findings of Note

Number of publications by document type 

Out of the 164 papers found in the Scopus database:

  • 122 (74%) were conference papers 
  • 34 (20.7%) were articles
  • The rest were spread between reviews, editorials, book chapters, etc. 

Number of publications by subject area

Out of the 164 papers found in the Scopus database:

  • Computer Science dominated with 45.5% of the publications.
  • Engineering followed with 13.0%.
  • Social Sciences contributed 11.4% of the papers.
  • Arts and Humanities accounted for 9.7%.
  • Mathematics made up 7.7%.
  • Multidisciplinary fields had 2.0% of the publications.
  • Agricultural and Biological Sciences, Biochemistry, Genetics and Molecular Biology, and Physics and Astronomy each had 1.7%.

The remaining publications were grouped under "Other" and constituted 5.7% of the total publications.

This distribution highlights the focus on Computer Science and Engineering, which together make up the majority of OpenAI's research outputs.

Number of publications by country 

Out of the 164 papers found in the Scopus database:

  • The United States came on top with 162 of the papers.
  • The United Kingdom followed with 18 papers.
  • Canada was next with 16 papers.
  • Germany had 8 papers, while Switzerland had 7 papers.
  • Belgium contributed 6 papers.
  • Japan and the Netherlands each had 5 papers.
  • Poland and France both had 4 papers.

The distribution shows that the majority of publications are concentrated in a few key countries, with the United States leading by a significant margin.

2024 OpenAI Publications — A Brief Overview

1. A Qubit, a Coin, and an Advice String Walk Into a Relational Problem

  • Year: 2024
  • Source title: Leibniz International Proceedings in Informatics, LIPIcs
  • Page count: N/A
  • DOI: 10.4230/LIPIcs.ITCS.2024.1
  • Link: Link
  • Affiliations: University of Texas at Austin, TX, United States
  • Publisher: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, Germany
  • Authors: Aaronson S.; Buhrman H.; Kretschmer W.
  • Document Type: Conference paper
  • PubMed ID: N/A
  • Abstract: Relational problems (those with many possible valid outputs) are different from decision problems, but it is easy to forget just how different. This paper initiates the study of FBQP/qpoly, the class of relational problems solvable in quantum polynomial-Time with the help of polynomial-sized quantum advice, along with its analogues for deterministic and randomized computation (FP, FBPP) and advice (/poly, /rpoly). Our first result is that FBQP/qpoly/= FBQP/poly, unconditionally, with no oracle - a striking contrast with what we know about the analogous decision classes. The proof repurposes the separation between quantum and classical one-way communication complexities due to Bar-Yossef, Jayram, and Kerenidis. We discuss how this separation raises the prospect of near-Term experiments to demonstrate "quantum information supremacy," a form of quantum supremacy that would not depend on unproved complexity assumptions. Our second result is that FBPP/ FP/poly - that is, Adleman s Theorem fails for relational problems - unless PSPACE NP/poly. Our proof uses IP = PSPACE and time-bounded Kolmogorov complexity. On the other hand, we show that proving FBPP/FP/poly will be hard, as it implies a superpolynomial circuit lower bound for PromiseBPEXP. We prove the following further results: Unconditionally, FP/= FBPP and FP/poly/= FBPP/poly (even when these classes are carefully defined). FBPP/poly = FBPP/rpoly (and likewise for FBQP). For sampling problems, by contrast, SampBPP/poly/= SampBPP/rpoly (and likewise for SampBQP).

2. Demonstrating a Long-Coherence Dual-Rail Erasure Qubit Using Tunable Transmons

  • Year: 2024
  • Source title: Physical Review X
  • Page count: NaN
  • DOI: 10.1103/PhysRevX.14.011051
  • Link: Link
  • Affiliations: AWS Center for Quantum Computing, Pasadena, 91125, CA, United States
  • Publisher: American Physical Society
  • Authors: Levine H.; Haim A.; Hung J.S.C.; Alidoust N.; Markovitch I.; O’Brien T.E.; Vool U.
  • Document Type: Article
  • PubMed ID: N/A
  • Abstract: Quantum error correction with erasure qubits promises significant advantages over standard error correction due to favorable thresholds for erasure errors. To realize this advantage in practice requires a qubit for which nearly all errors are such erasure errors, and the ability to check for erasure errors without dephasing the qubit. We demonstrate that a "dual-rail qubit"consisting of a pair of resonantly coupled transmons can form a highly coherent erasure qubit, where transmon T1 errors are converted into erasure errors and residual dephasing is strongly suppressed, leading to millisecond-scale coherence within the qubit subspace. We show that single-qubit gates are limited primarily by erasure errors, with erasure probability perasure=2.19(2)×10-3 per gate while the residual errors are ∼40 times lower. We further demonstrate midcircuit detection of erasure errors while introducing <0.1% dephasing error per check. Finally, we show that the suppression of transmon noise allows this dual-rail qubit to preserve high coherence over a broad tunable operating range, offering an improved capacity to avoid frequency collisions. This work establishes transmon-based dual-rail qubits as an attractive building block for hardware-efficient quantum error correction. 

3. Correction to “Compressed sensing in the presence of speckle noise”

  • Year: 2024
  • Source title: IEEE Transactions on Information Theory
  • Page count
  • DOI: 10.1109/TIT.2024.3409274
  • Link: Link
  • Affiliations: OpenAI, San Francisco, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
  • Publisher: Institute of Electrical and Electronics Engineers
  • Authors: Zhou W.; Jalali S.; Maleki A.
  • Document Type: Article
  • PubMed ID: N/A
  • Abstract: This paper presents a correction to Theorem 2 in [1] which follows from fixing an error in Lemma 5 and a minor correction in the constant of Lemma 3. Despite modifications to upper bounds and constants, the core conclusions of the original paper remain unaffected. The revised proofs now feature precise constants for clarity, maintaining the original findings’ integrity. IEEE

4. AI is a viable alternative to high throughput screening: a 318-target study

  • Year: 2024
  • Source title: Scientific Reports
  • Page count: NaN
  • DOI: 10.1038/s41598-024-54655-z
  • Link: Link
  • Affiliations: Atomwise Inc., San Francisco, United States; Amazon Web Services, USA
  • Publisher: Nature Research
  • Authors: Wallach I.; Bernard D.; Nguyen K.; Ho G.; Morris Q.
  • Document Type: Article
  • PubMed ID: N/A
  • Abstract: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

5. Beyond dominance and Nash: Ranking equilibria by critical mass

  • Year: 2024
  • Source title: Games and Economic Behavior
  • Page count: 16
  • DOI: 10.1016/j.geb.2024.01.011
  • Link: Link
  • Affiliations: OpenAI, 3180 18th Street, San Francisco, 94110, United States
  • Publisher: Academic Press Inc.
  • Authors: Kalai A.T.; Kalai E.
  • Document Type: Article
  • PubMed ID: NaN
  • Abstract: Strategic interactions pose central issues that are not adequately explained by the traditional concepts of dominant strategy equilibrium (DSE), Nash equilibrium (NE), and their refinements. A comprehensive analysis of equilibrium concepts within the von Neumann-Nash framework of n-person optimization reveals a decreasing hierarchy of n nested concepts ranging from DSE to NE. These concepts are defined by the “critical mass,” the number of players needed to adopt and sustain the play of a strategy profile as an equilibrium. In games with n>2 players, the n−2 intermediate concepts explain strategic issues in large social systems, implementation, decentralization, as well as replication studied in economics, operations management, and political games. 

6. Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations

  • Year: 2024
  • Source title: IEEE Transactions on Information Theory
  • Page count: 0
  • DOI: 10.1109/TIT.2024.3416202
  • Link: Link
  • Affiliations: Princeton University, United States; Stanford University, United States
  • Publisher: Institute of Electrical and Electronics Engineers
  • Authors: Chen M.; Meng J.; Bai Y.; Ye Y.; Vincent Poor H.
  • Document Type: Article
  • PubMed ID: N/A
  • Abstract: In real-world reinforcement learning (RL) systems, various forms of impaired observability can complicate matters. These situations arise when an agent is unable to observe the most recent state of the system due to latency or lossy channels, yet the agent must still make real-time decisions. This paper introduces a theoretical investigation into efficient RL in control systems where agents must act with delayed and missing state observations. We present algorithms and establish near-optimal regret upper and lower bounds, of the form Õ(√poly( H ) SAK ), for RL in the delayed and missing observation settings. Here S and A are the sizes of state and action spaces, H is the time horizon and K is the number of episodes. Despite impaired observability posing significant challenges to the policy class and planning, our results demonstrate that learning remains efficient, with the regret bound optimally depending on the state-action size of the original system. Additionally, we provide a characterization of the performance of the optimal policy under impaired observability, comparing it to the optimal value obtained with full observability. Numerical results are provided to support our theory.

7. PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation

  • Year: 2024
  • Source title: International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)
  • Page count: 18
  • DOI: 10.1145/3620665.3640366
  • Link: Link
  • Affiliations: Meta; OpenAI; Quansight; Intel; University of California, Berkeley
  • Publisher: Association for Computing Machinery
  • Authors: Ansel J.; Yang E.; He H.; Gimelshein N.; Jain R.
  • Document Type: Conference paper
  • PubMed ID: NaN
  • Abstract: This paper introduces two extensions to the popular PyTorch machine learning framework, TorchDynamo and TorchInductor, which implement the torch.compile feature released in PyTorch 2. TorchDynamo is a Python-level just-in-time (JIT) compiler that enables graph compilation in PyTorch programs without sacrificing the flexibility of Python. It achieves this by dynamically modifying Python bytecode before execution and extracting sequences of PyTorch operations into an FX graph, which is then JIT compiled using one of many extensible backends. TorchInductor is the default compiler backend for TorchDynamo, which translates PyTorch programs into OpenAI's Triton for GPUs and C++ for CPUs. Results show that TorchDynamo is able to capture graphs more robustly than prior approaches while adding minimal overhead, and TorchInductor is able to provide a 2.27× inference and 1.41× training geometric mean speedup on an NVIDIA A100 GPU across 180+ real-world models, which outperforms six other compilers. These extensions provide a new way to apply optimizations through compilers in eager mode frameworks like PyTorch.

Sources and Methodology

The data for this article was primarily sourced from Scopus and Google Scholar, two leading databases for academic publications. We cross-referenced these sources to compile a comprehensive list of OpenAI's publications. 

  • Scopus: A comprehensive abstract and citation database for peer-reviewed literature.
  • Google Scholar: A freely accessible web search engine that indexes the full text or metadata of scholarly literature.

Then, the data was organized in an Airtable to facilitate analysis and visualization.

Jonathan Gillham

Founder / CEO of Originality.ai I have been involved in the SEO and Content Marketing world for over a decade. My career started with a portfolio of content sites, recently I sold 2 content marketing agencies and I am the Co-Founder of MotionInvest.com, the leading place to buy and sell content websites. Through these experiences I understand what web publishers need when it comes to verifying content is original. I am not For or Against AI content, I think it has a place in everyones content strategy. However, I believe you as the publisher should be the one making the decision on when to use AI content. Our Originality checking tool has been built with serious web publishers in mind!

Frequently Asked Questions

Do I have to fill out the entire form?

No, that’s one of the benefits, only fill out the areas which you think will be relevant to the prompts you require.

Why is the English so poor for some prompts?

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.

In The Press

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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.

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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.

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After extensive research and testing, we determined Originality.ai to be the most accurate technology.

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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%.

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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

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Originality.ai Do give them a shot! 

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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.

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Frequently Asked Questions

Why is it important to check for plagiarism?

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.

What is Text Comparison?

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.

How do Text Comparison Tools Work?

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.

What is Syntax Highlighting?

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.

How Can I Conduct a Plagiarism Check between Two Documents Online?

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.

What Are the Benefits of Using a Text Compare Tool?

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.

What Files Can You Inspect with This Text Compare Tool?

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.

Will My Data Be Shared?

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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