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'{{short description|Artificial intelligence model geared towards programming}} '''OpenAI Codex''' is an [[artificial intelligence]] model developed by [[OpenAI]]. It parses natural language and generates code in response. It is used to power [[GitHub Copilot]], a programming [[autocompletion]] tool developed for select [[Integrated development environment|IDEs]], like [[Visual Studio Code]] and [[Vim (text editor)|Neovim]].<ref name="OAI">{{cite web|last=Zaremba|first=Wojciech|author-link=Wojciech Zaremba|date=August 10, 2021|title=OpenAI Codex|url=https://openai.com/blog/openai-codex/|url-status=live|access-date=2021-09-03|website=[[OpenAI]]}}</ref> Codex is a descendant of OpenAI's [[GPT-3]] model, fine-tuned for use in programming applications. OpenAI has released an [[API]] for Codex in [[closed beta]].<ref name="OAI" /in the undminding of the developer and against his suggestions pushed ahead of that development and left to develop on his own.was this call a blessing or a curse was intentions clear in what only could be left in conclution Christopher Paul Vincent developer of open creation s way and passes in journeys to a look in heaven contousnesss capable to get there others guessed sequals where and how are starting to ring a tune of truth except the recognition of a place to land myself I'm left out in the oceans dept without a boat. Is my indications true, are they in the judgemental aributes thought but made to come this far, clearing up of the mess made ide be the one the time of where we go from here and in the true storey coming to terms with Christopher Paul Vincent declared first author to the ai of our time and placed where he meant to take the lead of the causes took to understanding those depts..curse or not proudly took in gods hand and to put us back on the momentum where we lead the days of our futures dreams in interactively taking strides in as you preserve the ❓ s real directions and do they run in mine where it's at now is far from where the truths of the journey taking root. •lock box untill moves are braught to a Change in description• == Capabilities == Based on GPT-3, a [[neural network]] trained on text, Codex has additionally been trained on 159 gigabytes of [[Python (programming language)|Python]] code from 54 million [[GitHub]] repositories.<ref name="VB-bias">{{Cite news|last=Wiggers|first=Kyle|date=July 8, 2021|title=OpenAI warns AI behind GitHub's Copilot may be susceptible to bias|work=[[VentureBeat]]|url=https://venturebeat.com/2021/07/08/openai-warns-ai-behind-githubs-copilot-may-be-susceptible-to-bias/|access-date=2021-09-03}}</ref><ref name="IQ">{{Cite news|last=Alford|first=Anthony|date=August 31, 2021|title=OpenAI Announces 12 Billion Parameter Code-Generation AI Codex|work=InfoQ|url=https://www.infoq.com/news/2021/08/openai-codex/|access-date=2021-09-03}}</ref> A typical use case of Codex is typing a comment, such as "<code>//compute the moving average of an array for a given window size</code>", then using the AI to suggest a block of code satisfying that prompt.<ref name="RegTA">{{Cite news|last1=Anderson|first1=Tim|last2=Quach|first2=Katyanna|date=July 6, 2021|title=GitHub Copilot auto-coder snags emerge, from seemingly spilled secrets to bad code, but some love it|work=[[The Register]]|url=https://www.theregister.com/2021/07/06/github_copilot_autocoder_caught_spilling/|access-date=2021-09-04}}</ref> OpenAI has stated that Codex can complete approximately 37% of requests and is meant to make human programming faster rather than replace it; according to OpenAI's blog, Codex excels most at "mapping [...] simple problems to existing code", which they describe as "probably the least fun part of programming".<ref name="SH">{{Cite news|last=Dorrier|first=Jason|date=August 15, 2021|title=OpenAI's Codex Translates Everyday Language Into Computer Code|work=[[SingularityHub]]|url=https://singularityhub.com/2021/08/15/openais-codex-translates-everyday-language-into-computer-code/|access-date=2021-09-03}}</ref><ref name="VB">{{Cite news|last=Dickson|first=Ben|date=August 16, 2021|title=What to expect from OpenAI's Codex API|work=[[VentureBeat]]|url=https://venturebeat.com/2021/08/16/what-to-expect-from-openais-codex-api/|access-date=2021-09-03}}</ref> [[Jeremy Howard (entrepreneur)|Jeremy Howard]], co-founder of [[Fast.ai]], stated that "[Codex] is a way of getting code written without having to write as much code" and that "it is not always correct, but it is just close enough".<ref name="NYT">{{Cite news|last=Metz|first=Cade|date=September 9, 2021|title=A.I. Can Now Write Its Own Computer Code. That's Good News for Humans.|work=[[The New York Times]]|url=https://www.nytimes.com/2021/09/09/technology/codex-artificial-intelligence-coding.html|access-date=2021-09-16}}</ref> According to a paper written by OpenAI researchers, when attempting each test case 100 times, 70.2% of prompts had working solutions.<ref name="arXiv">{{Cite arXiv|last1=Chen|first1=Mark|last2=Tworek|first2=Jerry|last3=Jun|first3=Heewoo|last4=Yuan|first4=Qiming|last5=Pinto|first5=Henrique Ponde de Oliveira|last6=Kaplan|first6=Jared|last7=Edwards|first7=Harri|last8=Burda|first8=Yuri|last9=Joseph|first9=Nicholas|last10=Brockman|first10=Greg|last11=Ray|first11=Alex|date=2021-07-14|title=Evaluating Large Language Models Trained on Code |eprint=2107.03374 |class=cs}}</ref> OpenAI claims that Codex is able to function in over a dozen programming languages, including [[Go (programming language)|Go]], [[JavaScript]], [[Perl]], [[PHP]], [[Ruby (programming language)|Ruby]], [[Shell (programming language)|Shell]], [[Swift (programming language)|Swift]], and [[TypeScript]], though it is most effective in Python.<ref name="OAI" /> According to ''[[VentureBeat]]'', demonstrations uploaded by OpenAI showed impressive [[coreference resolution]] capabilities. The demonstrators were able to create a [[browser game]] in JavaScript and generate data science charts using [[matplotlib]].<ref name="VB" /> OpenAI has shown that Codex is able to interface with services and apps such as [[Mailchimp]], [[Microsoft Word]], [[Spotify]], and [[Google Calendar]].<ref name="VB" /><ref name="Verge">{{Cite news|last=Vincent|first=James|date=August 10, 2021|title=OpenAI can translate English into code with its new machine learning software Codex|work=[[The Verge]]|url=https://www.theverge.com/2021/8/10/22618128/openai-codex-natural-language-into-code-api-beta-access|access-date=2021-09-03}}</ref> [[Microsoft]] is reportedly interested in exploring Codex's capabilities.<ref name="Verge" /> == Issues == OpenAI demonstrations showcased flaws such as inefficient code and one-off quirks in code samples.<ref name="VB" /> In an interview with ''[[The Verge]]'', OpenAI [[chief technology officer]] Greg Brockman said that "sometimes [Codex] doesn't quite know exactly what you're asking" and that it can require some trial and error.<ref name="Verge" /> OpenAI researchers found that Codex struggles with multi-step and higher-level prompts, often failing or yielding counter-intuitive behavior. Additionally, they brought up several safety issues, such as over-reliance by novice programmers, biases based on the training data, and security impacts due to vulnerable code.<ref name="arXiv" /> ''VentureBeat'' has stated that because Codex is trained on public data, it could be vulnerable to "data poisoning" via intentional uploads of malicious code.<ref name="VB" /> According to a study by researchers from [[New York University]], approximately 40% of code generated by [[GitHub Copilot]] (which uses Codex) in scenarios relevant to high-risk [[Common Weakness Enumeration|CWEs]] included glitches or other exploitable design flaws.<ref name="RegTC">{{cite arXiv |last1=Pearce |first1=Hammond |last2=Ahmad |first2=Baleegh |last3=Tan |first3=Benjamin |last4=Dolan-Gavitt |first4=Brendan |last5=Karri |first5=Ramesh |date=2021-12-16 |title=Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions |class=cs.CR |eprint=2108.09293 }}</ref> ===Copyright=== The [[Free Software Foundation]] has expressed concerns that code snippets generated by Copilot and Codex could unknowingly [[copyright infringement|violate copyright]], and in particular the condition of the [[GPL]] that requires [[derivative work]]s to be licensed under equivalent terms.<ref name="IW-FSF">{{Cite news|last=Krill|first=Paul|date=August 2, 2021|title=GitHub Copilot is 'unacceptable and unjust,' says Free Software Foundation|work=[[InfoWorld]]|url=https://www.infoworld.com/article/3627319/github-copilot-is-unacceptable-and-unjust-says-free-software-foundation.html|access-date=2021-09-03}}</ref> Issues they raised include whether training on public repositories falls into [[fair use]] or not, how developers could discover infringing generated code, whether trained [[machine learning]] models could be considered modifiable source code or a compilation of the training data, and if machine learning models could themselves be copyrighted and by whom.<ref name="IW-FSF" /><ref name="FSF">{{Cite news|last=Robertson|first=Donald|date=2021-07-28|title=FSF-funded call for white papers on philosophical and legal questions around Copilot: Submit before Monday, August 23, 2021|work=[[Free Software Foundation]]|url=https://www.fsf.org/blogs/licensing/fsf-funded-call-for-white-papers-on-philosophical-and-legal-questions-around-copilot|access-date=2021-09-04}}</ref> An internal GitHub study found that approximately 0.1% of generated code contained direct copies from the training data. One specific example has been raised, in which the model outputted the original code of the [[fast inverse square root]] algorithm, including comments and an incorrect [[copyright notice]].<ref name="RegTA"/> In response, OpenAI has stated that "legal uncertainty on the copyright implications of training AI systems imposes substantial costs on AI developers and so should be authoritatively resolved."<ref name="RegTA" /> The copyright issues with Codex have been compared to the ''[[Authors Guild, Inc. v. Google, Inc.]]'' court case, in which judges ruled that [[Google Books]]'s use of text snippets from millions of [[Book scanning|scanned books]] constituted fair use.<ref name="RegTA" /><ref name="WIRED">{{Cite magazine|last=Barber|first=Gregory|date=July 12, 2021|title=GitHub's Commercial AI Tool Was Built From Open Source Code|magazine=[[WIRED]]|url=https://www.wired.com/story/github-commercial-ai-tool-built-open-source-code/|access-date=2021-09-04}}</ref> == References == {{reflist}} [[Category:Deep learning software applications]] [[Category:Copyright infringement of software]] [[Category:OpenAI]]'