"GPT-3"의 두 판 사이의 차이

수학노트
둘러보기로 가기 검색하러 가기
(→‎노트: 새 문단)
 
 
(같은 사용자의 중간 판 하나는 보이지 않습니다)
156번째 줄: 156번째 줄:
 
===소스===
 
===소스===
 
  <references />
 
  <references />
 +
 +
==메타데이터==
 +
===위키데이터===
 +
* ID :  [https://www.wikidata.org/wiki/Q95726734 Q95726734]
 +
===Spacy 패턴 목록===
 +
* [{'LEMMA': 'GPT-3'}]
 +
* [{'LOWER': 'generative'}, {'LOWER': 'pre'}, {'OP': '*'}, {'LOWER': 'trained'}, {'LOWER': 'transformer'}, {'LEMMA': '3'}]
 +
* [{'LOWER': 'generative'}, {'LOWER': 'pretrained'}, {'LOWER': 'transformer'}, {'LEMMA': '3'}]
 +
* [{'LEMMA': 'GPT3'}]

2021년 2월 17일 (수) 00:19 기준 최신판

노트

위키데이터

말뭉치

  1. Since then, you’ve probably already seen OpenAI’s announcement of their groundbreaking GPT-3 model – an autoregressive language model that outputs remarkably human-like text.[1]
  2. The scope of commercial and creative potential that can be unlocked through the GPT-3 model is profound, with genuinely novel capabilities – most of which we haven’t even imagined yet.[1]
  3. OpenAI will continue to offer GPT-3 and other powerful models via its own Azure-hosted API, launched in June.[1]
  4. “GPT-3 makes an amazing demo, but putting it in a product is another story,” said Shumer.[2]
  5. 언어모델인 GPT-3 를 학습시켜, few-shot 세팅에서 성능을 측정하였다.[3]
  6. In short, GPT-3 is a model which is trained to autocomplete sentences.[4]
  7. GPT-3 is being fed with natural language descriptions (the prompts being entered into the “generate” box), and it’s autocompleting with code that roughly satisfies those descriptions.[4]
  8. GPT-3 is radically different in that it’s way too large for hobbyists (or most companies) to train themselves, or to even run.[4]
  9. This creates a power dynamic where a small number of people have access and say nice things about GPT-3 in order to retain this hotly-contested privilege.[4]
  10. Scarcely a year later, OpenAI has already outdone itself with GPT-3, a new generative language model that is bigger than GPT-2 by orders of magnitude.[5]
  11. Just like its predecessor GPT-2, GPT-3 was trained on a simple task: given the previous words in a text, predict the next word.[5]
  12. Building GPT-3 required a monumental effort from OpenAI researchers.[5]
  13. The details of the GPT-3 model are discussed in the May 2020 paper “Language Models are Few-Shot Learners,” which is 74 pages long and has more than 30 authors.[5]
  14. Summary: I share my early experiments with OpenAI's new language prediction model (GPT-3) beta.[6]
  15. So there are lots of posts for GPT-3 to study and learn from.[6]
  16. I posted about one interesting tech topic every day in May, alternating between using my own words and paraphrasing my previous post with GPT-3’s help.[6]
  17. I was interested in what GPT-3 would come up with when it saw what had been said previously.[6]
  18. “Playing with GPT-3 feels like seeing the future,” Arram Sabeti, a San Francisco–based developer and artist, tweeted last week.[7]
  19. Before asking GPT-3 to generate new text, you can focus it on particular patterns it may have learned during its training, priming the system for certain tasks.[8]
  20. But GPT-3 can do things that previous models could not, like write its own computer code.[8]
  21. Because GPT-3 learns from such language, it, too, can show bias and hate.[8]
  22. This may be one reason that OpenAI has shared GPT-3 with only a small number of testers.[8]
  23. "In this work, we show that performance similar to GPT-3 can be obtained with language models whose parameter count is several orders of magnitude smaller.[9]
  24. 버전이 생성됐고 포어는 블로그 용으로 하나를 선택했으며 거의 편집없이 GPT-3 버전에서 복사해 붙여 넣었다.[10]
  25. So, you’ve seen some amazing GPT-3 demos on Twitter (if not, where’ve you been?).[11]
  26. If you want to try out GPT-3 today, you’ll need to apply to be whitelisted by OpenAI.[11]
  27. GPT-3 is a neural-network-powered language model.[11]
  28. Like most language models, GPT-3 is elegantly trained on an unlabeled text dataset (in this case, the training data includes among others Common Crawl and Wikipedia).[11]
  29. Generative Pre-trained Transformer 3, more commonly known as GPT-3 is an autoregressive language model that was created by OpenAI.[12]
  30. The third version of the GPT model (GPT-3) created a lot of hype in the developer community.[12]
  31. People have been posting tweets on several awesome applications that they built using GPT-3 API.[12]
  32. Several methods to evaluate the performance of GPT-3 were used.[12]
  33. For some observers, GPT-3 — while very definitely not AGI — could well be the first step toward creating this sort of intelligence.[13]
  34. As the name suggests, GPT-3 is the third in a series of autocomplete tools designed by OpenAI.[13]
  35. Like all deep learning systems, GPT-3 looks for patterns in data.[13]
  36. These regularities are unknown to humans, but they’re stored as billions of weighted connections between the different nodes in GPT-3’s neural network.[13]
  37. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.[14]
  38. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model.[14]
  39. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.[14]
  40. We discuss broader societal impacts of this finding and of GPT-3 in general.[14]
  41. Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text.[15]
  42. Thirty-one OpenAI researchers and engineers presented the original May 28, 2020 paper introducing GPT-3.[15]
  43. GPT-3 was used by The Guardian to write an article about AI being harmless to human beings.[15]
  44. GPT-3 is used in AI Dungeon, which generates text-based adventure games.[15]
  45. As we discuss in the GPT-3 paper and model card, our API models do exhibit biases that will be reflected in generated text.[16]
  46. GPT-3 is a computer program created by the privately held San Francisco startup OpenAI.[17]
  47. GPT-3 can respond to any text that a person types into the computer with a new piece of text that is appropriate to the context.[17]
  48. GPT-3 is compute-hungry, putting it beyond the use of most companies in any conceivable on-premise fashion.[17]
  49. GPT-3 is an example of what's known as a language model, which is a particular kind of statistical program.[17]
  50. In this article we will explore how to work with GPT-3 for a variety of use cases from how to use it as a writing assistant to building a highly sophisticated chatbot.[18]
  51. By the end you’ll know how to program GPT-3 to chat with you about your favorite topics.[18]
  52. Do you find it hard to believe that GPT-3 can generate text that is virtually indistinguishable from what a human writer can produce?[18]
  53. The following two paragraphs were generated by the GPT-3 engine to describe itself, after I trained it just by showing it the first paragraph of the GPT-3 article on Wikipedia.[18]
  54. The GPT-3 model uses 175 billion parameters.[19]
  55. Essentially, you just need to tell the GPT-3 model what needs to be done as the input in English and it’ll compute the output for you based on the next word prediction.[19]
  56. While currently, OpenAI has been released GPT-3 in private beta only, the early testers have already shared their findings which have went viral on Twitter.[19]
  57. Though there’s a lot to discuss about the intricacies of GPT-3, I’d skip onto what it means for developers and their jobs.[19]
  58. On September 22nd, Microsoft announced that “Microsoft is teaming up with OpenAI to exclusively license GPT-3”.[20]
  59. For the purposes of this piece, I focus primarily on the "having access to powerful AI models" part of democratization since GPT-3 is such a pre-built AI model.[20]
  60. Others will still be able to access GPT-3 through the API.[20]
  61. GPT-3 and other very very large models created at Microsoft and Google are very concerning in how they affect “democratization” of AI.[20]
  62. I’ve now spent the past few days looking at GPT-3 in greater depth and playing around with it.[21]
  63. A year ago I sat down to play with GPT-3’s precursor dubbed (you guessed it) GPT-2.[21]
  64. A year later, GPT-3 is here, and it’s smarter.[21]
  65. “It surprises me continuously,” Arram Sabeti, an inventor with early access to GPT-3 who has published hundreds of examples of results from the program, told me.[21]
  66. Due to large number of parameters and extensive dataset GPT-3 has been trained on, it performs well on downstream NLP tasks in zero-shot and few-shot setting.[22]
  67. GPT-3 was trained on a mix of five different corpora, each having certain weight assigned to it.[22]
  68. Performance and Summary: GPT-3 was evaluated on a host of language modelling and NLP datasets.[22]
  69. GPT-3 performed better than state-of-the-art for language modelling datasets like LAMBADA and Penn Tree Bank in few or zero-shot setting.[22]
  70. Based on the relatively few samples of text available for examination, GPT-3 is capable of producing excellent syntax.[23]
  71. For instance, one passage written by GPT-3 predicts you could suddenly die after drinking cranberry juice with a teaspoon of grape juice in it.[23]
  72. GPT-3 shows AI will certainly lead to better experiences than what has been available until now.[23]
  73. Part of the problem is the strong illusion of coherence we get from reading a passage produced by AI such as GPT-3 because of our own abilities.[23]
  74. GPT-3 challenges Google’s natural language processing (NLP) and the massive computing power of machine learning from all directions.[24]
  75. OpenAI is planning to turn GPT-3 into a commercial product by next year.[24]
  76. But GPT-3 can make a big difference.[24]
  77. The students that created the film used a tool derived from GPT-3 called Shortly Read to write the screenplay.[25]
  78. And if GPT-3 can write a reasonably convincing screenplay, what can’t it write?[25]
  79. It seems there are algorithms for everything these days, and GPT-3 is among the most impressive of them.[25]
  80. GPT-3 created eight different essays.[26]
  81. Those who took a closer look at GPT-3 found the smooth narrative was lacking in substance.[26]
  82. The GPT-3 hype exemplifies the sort of personification of which we need to be careful.[26]
  83. To test how comprehensive an article GPT-3 could produce, we ran the Guardian article through Optimize to determine how well it addressed the topics that experts mention when writing on this subject.[26]
  84. But arguably the biggest story this week was the beta release of GPT-3, a language model capable of a great range of tasks, like summarization, text generation to write articles, and translation.[27]
  85. Bender hasn’t tested GPT-3 personally, but she said from what she’s seen it is impressive, but with roughly the same architecture as GPT-2.[27]
  86. OpenAI is implementing testing in beta as a safeguard, which may help unearth issues, a spokesperson said, adding that the company is applying toxicity filters to GPT-3.[27]
  87. GPT-3 understandably generates marvel in some people, as it appears to draw closer to the idea of a general model that can do virtually anything with just a few samples of training data.[27]
  88. GPT-3 was created by OpenAI in May 2020 and published here.[28]
  89. More remarkably, GPT-3 also provides a much simpler way of applying the model to NLP tasks.[28]
  90. GPT-3 removes the need for traditional fine-tuning of models for each NLP task.[28]
  91. For example, GPT-3 has been used for the NLP task of machine translation between English and French.[28]
  92. Earlier this month, as reported by users who have access to the beta version of the language model, OpenAI declared the initial pricing plan of GPT-3.[29]
  93. This pricing plan will enable us to better assess what it would take for OpenAI to turn GPT-3 into a profitable business, and what kind of organizations might be able to benefit from the AI.[29]
  94. Ideally, OpenAI would have made GPT-3 available to the public.[29]
  95. Beta testers vetted and approved by OpenAI got free early access to GPT-3.[29]
  96. OpenAI stunned the world with the release of Generative Pre-trained Transformer 3 (GPT-3), the world’s most impressive language-generating AI.[30]
  97. When startup OpenAI, based in San Francisco, released GPT-3, the whole research community stood up and took notice.[31]
  98. OpenAI’s team used three evaluating techniques to measure the performance of GPT-3 in the testing stage — few-shot learning, one-shot learning, and zero-shot learning.[31]
  99. A bot powered by GPT-3 was found to be interacting with people in a Reddit thread.[31]
  100. This gave GPT-3 model the headline and an introduction from which it churned several completed versions.[31]
  101. Developers testing GPT-3 have provided many intriguing use cases.[32]
  102. it’s useful to consider what the commercialization of GPT-3 means for the future.[32]
  103. So where does GPT-3 intersect with artificial intelligence, machine learning, and deep learning?[32]
  104. This makes GPT-3 the most complex language model ever conceived, with 175 billion parameters in its network architecture.[32]
  105. Microsoft recently received an exclusive license to use OpenAI’s GPT-3 (Generative Pre-trained Transformer) language model in its own products and services.[33]
  106. There are several variations of GPT-3, which range from 125 to 175 billion parameters.[34]
  107. The GPT-3 model can generate texts of up to 50,000 characters, with no supervision.[34]
  108. To generate output, GPT-3 has a very large vocabulary, which it can combine to generate sentences.[34]
  109. This is done by feeding GPT-3 with books.[34]
  110. The latest development in this decoupling process is the GPT-3 language model.[35]
  111. Earlier this year, Elon Musk-backed artificial intelligence laboratory, OpenAI, released its latest, much anticipated autoregressive language model, the Generative Pre-trained Transformer 3 (GPT-3).[36]
  112. To understand what GPT-3 is, we must first explain what a neural network is.[36]
  113. What makes GPT-3 so unique as a language model powered by neural networks is its sheer size.[36]
  114. The specific architecture of the GPT-3 is mostly identical to its predecessor, GPT-2, but training this gargantuan-sized model is an engineering feat for the history books.[36]
  115. Commenters noted that although PET produced better results for NLP benchmarks, GPT-3 appeared more flexible.[37]
  116. GPT-3 is an autoregressive language model, using deep learning to produce human-like text and is apparently able to create content better than anything else ever made.[38]
  117. Whether GPT-3 is actually conscious or not is up to everyone to believe, yet there is the question of how far will AI go?[38]
  118. It seems that the GPT-3 can answer a question that can be directed to itself, in a very academic language, as if a human or an academic had written it.[39]
  119. Due to the fact that GPT-3 is a software language model, it needs to be evaluated its risks concerning subjects such as language, expression, spelling, getting news, reporting.[39]
  120. As it is seen, the contents produced by GPT-3 are not a direct reflection of logical and intuitive human thought but take shape according to the data provided by humans to the database used by GPT-3.[39]
  121. It is evaluated that the risks of GPT-3 may violation personal rights and the right to obtain information.[39]
  122. You may have heard about GPT-3 this summer, the new cool kid on the AI block.[40]
  123. In machine learning, a language model like GPT-3 simply tries to predict a word in a sentence given the previous words, called the context.[40]
  124. Thanks to the large size of the model, GPT-3 can be applied on new tasks and ‘few-shot’ demonstrations without any further fine-tuning on specific data.[40]
  125. Similar to the admin tasks above, GPT-3 could help nurses or patients to quickly find a piece of information in a very long document, like finding insurance benefits for specific medical examinations.[40]
  126. In this implementation, GPT-3 generates text from prompts (the tool can do more, such as generating computer code).[41]
  127. Like most things labeled Artificial Intelligence, GPT-3 was fed a lot of data and tasked with finding patterns.[41]
  128. Some reported that GPT-3 produced content that was racist, misogynistic or anti-Muslim, but I could not replicate their findings.[41]
  129. The hype around GPT-3 seems largely due to OpenAI’s excellent publicity skills.[41]
  130. Microsoft will exclusively license the powerful GPT-3 language model from artificial intelligence developer startup OpenAI.[42]
  131. OpenAI caused a stir when it unveiled GPT-3, with some seeing its advances in unsupervised learning as presaging a fundamental shift in conversational AI.[42]
  132. “The scope of commercial and creative potential that can be unlocked through the GPT-3 model is profound, with genuinely novel capabilities – most of which we haven’t even imagined yet.[42]
  133. GPT-3 drew a lot of attention from AI experts when it arrived.[42]
  134. All that is a bit moot by now because not only has OpenAI trained a much larger language model in GPT-3, but you can sign up to access it through their new API.[43]
  135. Comparing GPT-3 to GPT-2 is like comparing apples to, well, raisins, because the model is about that much larger.[43]
  136. While GPT-2 weighed in at a measly 1.542 billion parameters (with smaller release versions at 117, 345, and 762 million), the full-sized GPT-3 has 175 billion parameters.[43]
  137. Approximate size comparison of GPT-2, represented by a human skeleton, and GPT-3 approximated by the bones of a Tyrannosaurus rex.[43]
  138. OthersideAI is among the earliest commercial products to use GPT-3, currently the world’s largest language model, as reported by Slator back in July.[44]
  139. Contrary to earlier reports that it was “built entirely on GPT-3,” OthersideAI is actually built on a three-part system.[44]
  140. CEO Matt Shumer told Slator, “Using GPT-3 would lead to extremely inconsistent results, and a product that wouldn’t be very beneficial.[44]
  141. More broadly, the launching of OthersideAI highlights that all the hype around GPT-3 may not have been misplaced.[44]
  142. Therefore, GPT-3 is extremely powerful without understanding a single word it produces.[45]
  143. Designer Jordan Singer used GPT-3 to build a Figma plugin and called it “Designer”.[45]
  144. GPT-3 can code in Python, CSS, JSX.[45]
  145. Serving as a universal tool for programmers, GPT-3 is another step forward to this simple interaction with software systems.[45]
  146. GPT-3 is an AI language generation model created by OpenAI that automatically produces human-sounding language at scale.[46]
  147. In giving GPT-3 a Turing Test, Kevin Lacker reveals that GPT-3 possesses no expertise and is “still clearly subhuman” in some areas.[46]
  148. GPT-3 can also, unfortunately, create more insidious results than nonsensical sentences.[46]
  149. On June 11, 2020, an AI research and deployment company OpenAI – founded by Elon Musk, Sam Altman, and others – announced its revolutionary language model, GPT-3.[47]
  150. Not everyone can access the GPT-3 API, though – at least just yet.[47]

소스

  1. ↑ 1.0 1.1 1.2 Microsoft teams up with OpenAI to exclusively license GPT-3 language model
  2. ↑ OthersideAI raises $2.6M to let GPT-3 write your emails for you – TechCrunch
  3. ↑ Language Models are Few-Shot Learners(GPT3 논문 설명)
  4. ↑ 4.0 4.1 4.2 4.3 What does GPT-3 mean for AI?
  5. ↑ 5.0 5.1 5.2 5.3 The GPT-3 Model: What Does it Mean for Chatbots & Customer Service?
  6. ↑ 6.0 6.1 6.2 6.3 OpenAI's GPT-3 may be the biggest thing since bitcoin
  7. ↑ OpenAI’s new language generator GPT-3 is shockingly good—and completely mindless
  8. ↑ 8.0 8.1 8.2 8.3 Meet GPT-3. It Has Learned to Code (and Blog and Argue).
  9. ↑ 0.1%의 패러미터만으로 GPT-3 를 능가하기
  10. ↑ GPT-3 활용사례 및 API 신청방법
  11. ↑ 11.0 11.1 11.2 11.3 GPT-3 Explained in Under 3 Minutes
  12. ↑ 12.0 12.1 12.2 12.3 Getting started with GPT-3 model by OpenAI
  13. ↑ 13.0 13.1 13.2 13.3 OpenAI’s latest breakthrough is astonishingly powerful, but still fighting its flaws
  14. ↑ 14.0 14.1 14.2 14.3 GPT-3: Language Models are Few-Shot Learners
  15. ↑ 15.0 15.1 15.2 15.3 Wikipedia
  16. ↑ OpenAI API
  17. ↑ 17.0 17.1 17.2 17.3 What is GPT-3? Everything your business needs to know about OpenAI’s breakthrough AI language program
  18. ↑ 18.0 18.1 18.2 18.3 The Ultimate Guide to OpenAI's GPT-3 Language Model
  19. ↑ 19.0 19.1 19.2 19.3 OpenAI’s GPT-3: The End Of Cargo Cult Programmers
  20. ↑ 20.0 20.1 20.2 20.3 AI Democratization in the Era of GPT-3
  21. ↑ 21.0 21.1 21.2 21.3 GPT-3, explained: This new language AI is uncanny, funny — and a big deal
  22. ↑ 22.0 22.1 22.2 22.3 GPT models explained. Open AI's GPT-1,GPT-2,GPT-3
  23. ↑ 23.0 23.1 23.2 23.3 GPT-3: new AI can write like a human but don't mistake that for thinking – neuroscientist
  24. ↑ 24.0 24.1 24.2 AI in Search: What OpenAI’s GPT-3 means for Google and SEO?
  25. ↑ 25.0 25.1 25.2 OpenAI’s GPT-3 Wrote This Short Film—Even the Twist at the End
  26. ↑ 26.0 26.1 26.2 26.3 GPT-3 Exposed: Behind the Smoke and Mirrors
  27. ↑ 27.0 27.1 27.2 27.3 AI Weekly: The promise and shortcomings of OpenAI’s GPT-3
  28. ↑ 28.0 28.1 28.2 28.3 What Is GPT-3, and What Does It Mean for Natural Language Processing?
  29. ↑ 29.0 29.1 29.2 29.3 The GPT-3 economy
  30. ↑ How GPT-3 Is Shaping Our AI Future
  31. ↑ 31.0 31.1 31.2 31.3 Hits & Misses Of GPT-3: The Most Talked About Innovation Of 2020
  32. ↑ 32.0 32.1 32.2 32.3 GPT-3 and the Next Generation of AI-Powered Services
  33. ↑ Microsoft gets exclusive license to use GPT-3 language model. What does the model mean?
  34. ↑ 34.0 34.1 34.2 34.3 GPT-3
  35. ↑ GPT-3: Its Nature, Scope, Limits, and Consequences
  36. ↑ 36.0 36.1 36.2 36.3 GPT-3 vs. Existing Conversational AI Solutions
  37. ↑ AI Training Method Exceeds GPT-3 Performance with 99.9% Fewer Parameters
  38. ↑ 38.0 38.1 Is AI GPT-3 gaining consciousness?
  39. ↑ 39.0 39.1 39.2 39.3 New Artificial Intelligence Instrument: GPT 3 and Legal Evaluation
  40. ↑ 40.0 40.1 40.2 40.3 Doctor GPT-3: hype or reality?
  41. ↑ 41.0 41.1 41.2 41.3 GPT-3 is a lot of fun, but no game-changer
  42. ↑ 42.0 42.1 42.2 42.3 Microsoft Scores Exclusive License to the Much-Hyped GPT-3 Language Model
  43. ↑ 43.0 43.1 43.2 43.3 What Can You Do with the OpenAI GPT-3 Language Model?
  44. ↑ 44.0 44.1 44.2 44.3 Early Adopter of World’s Largest Language Model, OthersideAI, Points to GPT-3 Potential
  45. ↑ 45.0 45.1 45.2 45.3 OpenAI GPT-3: how it works & why it matters
  46. ↑ 46.0 46.1 46.2 How to Cut Through the Hype of GPT-3
  47. ↑ 47.0 47.1 How we got access to GPT-3 in 5 days

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LEMMA': 'GPT-3'}]
  • [{'LOWER': 'generative'}, {'LOWER': 'pre'}, {'OP': '*'}, {'LOWER': 'trained'}, {'LOWER': 'transformer'}, {'LEMMA': '3'}]
  • [{'LOWER': 'generative'}, {'LOWER': 'pretrained'}, {'LOWER': 'transformer'}, {'LEMMA': '3'}]
  • [{'LEMMA': 'GPT3'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=GPT-3&oldid=51341"