Pythagoras0: /* 노트 */ 새 문단

2020-12-21T08:59:49Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q95726734 Q95726734]
===말뭉치===
# 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.<ref name="ref_012bd1c6">[https://blogs.microsoft.com/blog/2020/09/22/microsoft-teams-up-with-openai-to-exclusively-license-gpt-3-language-model/ Microsoft teams up with OpenAI to exclusively license GPT-3 language model]</ref>
# 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.<ref name="ref_012bd1c6" />
# OpenAI will continue to offer GPT-3 and other powerful models via its own Azure-hosted API, launched in June.<ref name="ref_012bd1c6" />
# “GPT-3 makes an amazing demo, but putting it in a product is another story,” said Shumer.<ref name="ref_820392ff">[https://techcrunch.com/2020/11/12/othersideai-raises-2-6m-to-let-gpt-3-write-your-emails-for-you/ OthersideAI raises $2.6M to let GPT-3 write your emails for you – TechCrunch]</ref>
# 언어모델인 GPT-3 를 학습시켜, few-shot 세팅에서 성능을 측정하였다.<ref name="ref_94bf6883">[https://greeksharifa.github.io/nlp(natural%20language%20processing)%20/%20rnns/2020/08/14/OpenAI-GPT-3-Language-Models-are-Few-Shot-Learners/ Language Models are Few-Shot Learners(GPT3 논문 설명)]</ref>
# In short, GPT-3 is a model which is trained to autocomplete sentences.<ref name="ref_ae98fb64">[https://towardsdatascience.com/what-does-gpt-3-mean-for-ai-58cd66616051 What does GPT-3 mean for AI?]</ref>
# 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.<ref name="ref_ae98fb64" />
# GPT-3 is radically different in that it’s way too large for hobbyists (or most companies) to train themselves, or to even run.<ref name="ref_ae98fb64" />
# 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.<ref name="ref_ae98fb64" />
# 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.<ref name="ref_681ceb49">[https://vanrijmenam.nl/gpt-3-model-what-mean-chatbots-customer-service/ The GPT-3 Model: What Does it Mean for Chatbots & Customer Service?]</ref>
# 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.<ref name="ref_681ceb49" />
# Building GPT-3 required a monumental effort from OpenAI researchers.<ref name="ref_681ceb49" />
# 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.<ref name="ref_681ceb49" />
# Summary: I share my early experiments with OpenAI's new language prediction model (GPT-3) beta.<ref name="ref_0804cff5">[https://maraoz.com/2020/07/18/openai-gpt3/ OpenAI's GPT-3 may be the biggest thing since bitcoin]</ref>
# So there are lots of posts for GPT-3 to study and learn from.<ref name="ref_0804cff5" />
# 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.<ref name="ref_0804cff5" />
# I was interested in what GPT-3 would come up with when it saw what had been said previously.<ref name="ref_0804cff5" />
# “Playing with GPT-3 feels like seeing the future,” Arram Sabeti, a San Francisco–based developer and artist, tweeted last week.<ref name="ref_6e208410">[https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/ OpenAI’s new language generator GPT-3 is shockingly good—and completely mindless]</ref>
# 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.<ref name="ref_75376825">[https://www.nytimes.com/2020/11/24/science/artificial-intelligence-ai-gpt3.html Meet GPT-3. It Has Learned to Code (and Blog and Argue).]</ref>
# But GPT-3 can do things that previous models could not, like write its own computer code.<ref name="ref_75376825" />
# Because GPT-3 learns from such language, it, too, can show bias and hate.<ref name="ref_75376825" />
# This may be one reason that OpenAI has shared GPT-3 with only a small number of testers.<ref name="ref_75376825" />
# "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.<ref name="ref_6fde8508">[https://blog-ko.allganize.ai/beating-gpt-3-with-only-0-1-of-the-parameters/ 0.1%의 패러미터만으로 GPT-3 를 능가하기]</ref>
# 버전이 생성됐고 포어는 블로그 용으로 하나를 선택했으며 거의 편집없이 GPT-3 버전에서 복사해 붙여 넣었다.<ref name="ref_e2121d4a">[https://uipath.tistory.com/44 GPT-3 활용사례 및 API 신청방법]</ref>
# So, you’ve seen some amazing GPT-3 demos on Twitter (if not, where’ve you been?).<ref name="ref_d20c38bf">[https://daleonai.com/gpt3-explained-fast GPT-3 Explained in Under 3 Minutes]</ref>
# If you want to try out GPT-3 today, you’ll need to apply to be whitelisted by OpenAI.<ref name="ref_d20c38bf" />
# GPT-3 is a neural-network-powered language model.<ref name="ref_d20c38bf" />
# 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).<ref name="ref_d20c38bf" />
# Generative Pre-trained Transformer 3, more commonly known as GPT-3 is an autoregressive language model that was created by OpenAI.<ref name="ref_449c0151">[https://blog.accubits.com/getting-started-with-gpt-3-model-by-openai/ Getting started with GPT-3 model by OpenAI]</ref>
# The third version of the GPT model (GPT-3) created a lot of hype in the developer community.<ref name="ref_449c0151" />
# People have been posting tweets on several awesome applications that they built using GPT-3 API.<ref name="ref_449c0151" />
# Several methods to evaluate the performance of GPT-3 were used.<ref name="ref_449c0151" />
# For some observers, GPT-3 — while very definitely not AGI — could well be the first step toward creating this sort of intelligence.<ref name="ref_fdb1a585">[https://www.theverge.com/21346343/gpt-3-explainer-openai-examples-errors-agi-potential OpenAI’s latest breakthrough is astonishingly powerful, but still fighting its flaws]</ref>
# As the name suggests, GPT-3 is the third in a series of autocomplete tools designed by OpenAI.<ref name="ref_fdb1a585" />
# Like all deep learning systems, GPT-3 looks for patterns in data.<ref name="ref_fdb1a585" />
# 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.<ref name="ref_fdb1a585" />
# 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.<ref name="ref_c26bd832">[https://github.com/openai/gpt-3 GPT-3: Language Models are Few-Shot Learners]</ref>
# 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.<ref name="ref_c26bd832" />
# Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.<ref name="ref_c26bd832" />
# We discuss broader societal impacts of this finding and of GPT-3 in general.<ref name="ref_c26bd832" />
# Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text.<ref name="ref_f67d2969">[https://en.wikipedia.org/wiki/GPT-3 Wikipedia]</ref>
# Thirty-one OpenAI researchers and engineers presented the original May 28, 2020 paper introducing GPT-3.<ref name="ref_f67d2969" />
# GPT-3 was used by The Guardian to write an article about AI being harmless to human beings.<ref name="ref_f67d2969" />
# GPT-3 is used in AI Dungeon, which generates text-based adventure games.<ref name="ref_f67d2969" />
# As we discuss in the GPT-3 paper and model card, our API models do exhibit biases that will be reflected in generated text.<ref name="ref_11e6bd6c">[https://openai.com/blog/openai-api/ OpenAI API]</ref>
# GPT-3 is a computer program created by the privately held San Francisco startup OpenAI.<ref name="ref_52d3bd97">[https://www.zdnet.com/article/what-is-gpt-3-everything-business-needs-to-know-about-openais-breakthrough-ai-language-program/ What is GPT-3? Everything your business needs to know about OpenAI’s breakthrough AI language program]</ref>
# 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.<ref name="ref_52d3bd97" />
# GPT-3 is compute-hungry, putting it beyond the use of most companies in any conceivable on-premise fashion.<ref name="ref_52d3bd97" />
# GPT-3 is an example of what's known as a language model, which is a particular kind of statistical program.<ref name="ref_52d3bd97" />
# 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.<ref name="ref_b0b981dc">[https://www.twilio.com/blog/ultimate-guide-openai-gpt-3-language-model The Ultimate Guide to OpenAI's GPT-3 Language Model]</ref>
# By the end you’ll know how to program GPT-3 to chat with you about your favorite topics.<ref name="ref_b0b981dc" />
# Do you find it hard to believe that GPT-3 can generate text that is virtually indistinguishable from what a human writer can produce?<ref name="ref_b0b981dc" />
# 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.<ref name="ref_b0b981dc" />
# The GPT-3 model uses 175 billion parameters.<ref name="ref_82fc48e4">[https://towardsdatascience.com/openais-gpt-3-the-end-of-cargo-cult-programmers-23102f70f855 OpenAI’s GPT-3: The End Of Cargo Cult Programmers]</ref>
# 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.<ref name="ref_82fc48e4" />
# 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.<ref name="ref_82fc48e4" />
# 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.<ref name="ref_82fc48e4" />
# On September 22nd, Microsoft announced that “Microsoft is teaming up with OpenAI to exclusively license GPT-3”.<ref name="ref_040276d9">[https://thegradient.pub/ai-democratization-in-the-era-of-gpt-3/ AI Democratization in the Era of GPT-3]</ref>
# 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.<ref name="ref_040276d9" />
# Others will still be able to access GPT-3 through the API.<ref name="ref_040276d9" />
# GPT-3 and other very very large models created at Microsoft and Google are very concerning in how they affect “democratization” of AI.<ref name="ref_040276d9" />
# I’ve now spent the past few days looking at GPT-3 in greater depth and playing around with it.<ref name="ref_bc3734e9">[https://www.vox.com/future-perfect/21355768/gpt-3-ai-openai-turing-test-language GPT-3, explained: This new language AI is uncanny, funny — and a big deal]</ref>
# A year ago I sat down to play with GPT-3’s precursor dubbed (you guessed it) GPT-2.<ref name="ref_bc3734e9" />
# A year later, GPT-3 is here, and it’s smarter.<ref name="ref_bc3734e9" />
# “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.<ref name="ref_bc3734e9" />
# 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.<ref name="ref_4308f63e">[https://medium.com/walmartglobaltech/the-journey-of-open-ai-gpt-models-32d95b7b7fb2 GPT models explained. Open AI's GPT-1,GPT-2,GPT-3]</ref>
# GPT-3 was trained on a mix of five different corpora, each having certain weight assigned to it.<ref name="ref_4308f63e" />
# Performance and Summary: GPT-3 was evaluated on a host of language modelling and NLP datasets.<ref name="ref_4308f63e" />
# 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.<ref name="ref_4308f63e" />
# Based on the relatively few samples of text available for examination, GPT-3 is capable of producing excellent syntax.<ref name="ref_0a957981">[https://theconversation.com/gpt-3-new-ai-can-write-like-a-human-but-dont-mistake-that-for-thinking-neuroscientist-146082 GPT-3: new AI can write like a human but don't mistake that for thinking – neuroscientist]</ref>
# 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.<ref name="ref_0a957981" />
# GPT-3 shows AI will certainly lead to better experiences than what has been available until now.<ref name="ref_0a957981" />
# 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.<ref name="ref_0a957981" />
# GPT-3 challenges Google’s natural language processing (NLP) and the massive computing power of machine learning from all directions.<ref name="ref_55691189">[https://www.merkleinc.com/in/blog/ai-search-what-openais-gpt-3-means-google-and-seo-0 AI in Search: What OpenAI’s GPT-3 means for Google and SEO?]</ref>
# OpenAI is planning to turn GPT-3 into a commercial product by next year.<ref name="ref_55691189" />
# But GPT-3 can make a big difference.<ref name="ref_55691189" />
# The students that created the film used a tool derived from GPT-3 called Shortly Read to write the screenplay.<ref name="ref_4239da50">[https://singularityhub.com/2020/10/23/an-ai-wrote-this-short-film-and-its-sort-of-fascinating/ OpenAI’s GPT-3 Wrote This Short Film—Even the Twist at the End]</ref>
# And if GPT-3 can write a reasonably convincing screenplay, what can’t it write?<ref name="ref_4239da50" />
# It seems there are algorithms for everything these days, and GPT-3 is among the most impressive of them.<ref name="ref_4239da50" />
# GPT-3 created eight different essays.<ref name="ref_b4abc6bd">[https://blog.marketmuse.com/gpt-3-exposed/ GPT-3 Exposed: Behind the Smoke and Mirrors]</ref>
# Those who took a closer look at GPT-3 found the smooth narrative was lacking in substance.<ref name="ref_b4abc6bd" />
# The GPT-3 hype exemplifies the sort of personification of which we need to be careful.<ref name="ref_b4abc6bd" />
# 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.<ref name="ref_b4abc6bd" />
# 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.<ref name="ref_164f7288">[https://venturebeat.com/2020/07/24/ai-weekly-the-promise-and-shortcomings-of-openais-gpt-3/ AI Weekly: The promise and shortcomings of OpenAI’s GPT-3]</ref>
# 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.<ref name="ref_164f7288" />
# 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.<ref name="ref_164f7288" />
# 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.<ref name="ref_164f7288" />
# GPT-3 was created by OpenAI in May 2020 and published here.<ref name="ref_73a8f1d1">[https://www.sparkcognition.com/gpt-3-natural-language-processing/ What Is GPT-3, and What Does It Mean for Natural Language Processing?]</ref>
# More remarkably, GPT-3 also provides a much simpler way of applying the model to NLP tasks.<ref name="ref_73a8f1d1" />
# GPT-3 removes the need for traditional fine-tuning of models for each NLP task.<ref name="ref_73a8f1d1" />
# For example, GPT-3 has been used for the NLP task of machine translation between English and French.<ref name="ref_73a8f1d1" />
# 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.<ref name="ref_cd7dd2a3">[https://bdtechtalks.com/2020/09/21/gpt-3-economy-business-model/ The GPT-3 economy]</ref>
# 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.<ref name="ref_cd7dd2a3" />
# Ideally, OpenAI would have made GPT-3 available to the public.<ref name="ref_cd7dd2a3" />
# Beta testers vetted and approved by OpenAI got free early access to GPT-3.<ref name="ref_cd7dd2a3" />
# OpenAI stunned the world with the release of Generative Pre-trained Transformer 3 (GPT-3), the world’s most impressive language-generating AI.<ref name="ref_3ae6e6b0">[https://hbr.org/podcast/2020/10/how-gpt-3-is-shaping-our-ai-future How GPT-3 Is Shaping Our AI Future]</ref>
# When startup OpenAI, based in San Francisco, released GPT-3, the whole research community stood up and took notice.<ref name="ref_e9e79fe9">[https://analyticsindiamag.com/hits-misses-of-gpt-3-the-most-talked-about-innovation-of-2020/ Hits & Misses Of GPT-3: The Most Talked About Innovation Of 2020]</ref>
# 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.<ref name="ref_e9e79fe9" />
# A bot powered by GPT-3 was found to be interacting with people in a Reddit thread.<ref name="ref_e9e79fe9" />
# This gave GPT-3 model the headline and an introduction from which it churned several completed versions.<ref name="ref_e9e79fe9" />
# Developers testing GPT-3 have provided many intriguing use cases.<ref name="ref_9de4087e">[https://www.datacamp.com/community/blog/gpt3 GPT-3 and the Next Generation of AI-Powered Services]</ref>
# it’s useful to consider what the commercialization of GPT-3 means for the future.<ref name="ref_9de4087e" />
# So where does GPT-3 intersect with artificial intelligence, machine learning, and deep learning?<ref name="ref_9de4087e" />
# This makes GPT-3 the most complex language model ever conceived, with 175 billion parameters in its network architecture.<ref name="ref_9de4087e" />
# Microsoft recently received an exclusive license to use OpenAI’s GPT-3 (Generative Pre-trained Transformer) language model in its own products and services.<ref name="ref_591126c5">[https://www.thehindu.com/sci-tech/technology/microsoft-gets-exclusive-license-to-use-gpt-3-language-model-what-does-the-model-mean/article32782848.ece Microsoft gets exclusive license to use GPT-3 language model. What does the model mean?]</ref>
# There are several variations of GPT-3, which range from 125 to 175 billion parameters.<ref name="ref_6689de28">[https://www.fullstackpython.com/gpt-3.html GPT-3]</ref>
# The GPT-3 model can generate texts of up to 50,000 characters, with no supervision.<ref name="ref_6689de28" />
# To generate output, GPT-3 has a very large vocabulary, which it can combine to generate sentences.<ref name="ref_6689de28" />
# This is done by feeding GPT-3 with books.<ref name="ref_6689de28" />
# The latest development in this decoupling process is the GPT-3 language model.<ref name="ref_aaaedbe9">[https://link.springer.com/article/10.1007/s11023-020-09548-1 GPT-3: Its Nature, Scope, Limits, and Consequences]</ref>
# 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).<ref name="ref_9874da85">[https://www.hyro.ai/post/gpt-3-vs-existing-conversational-ai-solutions GPT-3 vs. Existing Conversational AI Solutions]</ref>
# To understand what GPT-3 is, we must first explain what a neural network is.<ref name="ref_9874da85" />
# What makes GPT-3 so unique as a language model powered by neural networks is its sheer size.<ref name="ref_9874da85" />
# 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.<ref name="ref_9874da85" />
# Commenters noted that although PET produced better results for NLP benchmarks, GPT-3 appeared more flexible.<ref name="ref_0f8ac98b">[https://www.infoq.com/news/2020/10/training-exceeds-gpt3/ AI Training Method Exceeds GPT-3 Performance with 99.9% Fewer Parameters]</ref>
# 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.<ref name="ref_5f5912aa">[https://www.devopsonline.co.uk/is-ai-gpt-3-gaining-consciousness/ Is AI GPT-3 gaining consciousness?]</ref>
# 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?<ref name="ref_5f5912aa" />
# 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.<ref name="ref_fa4f1da6">[https://www.lexology.com/library/detail.aspx?g=042f85c1-f263-4b9d-a0bf-a5f855aa128d New Artificial Intelligence Instrument: GPT 3 and Legal Evaluation]</ref>
# 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.<ref name="ref_fa4f1da6" />
# 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.<ref name="ref_fa4f1da6" />
# It is evaluated that the risks of GPT-3 may violation personal rights and the right to obtain information.<ref name="ref_fa4f1da6" />
# You may have heard about GPT-3 this summer, the new cool kid on the AI block.<ref name="ref_a1537299">[https://www.nabla.com/blog/gpt-3/ Doctor GPT-3: hype or reality?]</ref>
# 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.<ref name="ref_a1537299" />
# 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.<ref name="ref_a1537299" />
# 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.<ref name="ref_a1537299" />
# In this implementation, GPT-3 generates text from prompts (the tool can do more, such as generating computer code).<ref name="ref_cd59927a">[https://algorithmwatch.org/en/story/gpt-3/ GPT-3 is a lot of fun, but no game-changer]</ref>
# Like most things labeled Artificial Intelligence, GPT-3 was fed a lot of data and tasked with finding patterns.<ref name="ref_cd59927a" />
# Some reported that GPT-3 produced content that was racist, misogynistic or anti-Muslim, but I could not replicate their findings.<ref name="ref_cd59927a" />
# The hype around GPT-3 seems largely due to OpenAI’s excellent publicity skills.<ref name="ref_cd59927a" />
# Microsoft will exclusively license the powerful GPT-3 language model from artificial intelligence developer startup OpenAI.<ref name="ref_8763c80c">[https://voicebot.ai/2020/09/22/microsoft-scores-exclusive-license-to-the-much-hyped-gpt-3-language-model/ Microsoft Scores Exclusive License to the Much-Hyped GPT-3 Language Model]</ref>
# 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.<ref name="ref_8763c80c" />
# “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.<ref name="ref_8763c80c" />
# GPT-3 drew a lot of attention from AI experts when it arrived.<ref name="ref_8763c80c" />
# 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.<ref name="ref_ebd63fab">[https://blog.exxactcorp.com/what-can-you-do-with-the-openai-gpt-3-language-model/ What Can You Do with the OpenAI GPT-3 Language Model?]</ref>
# Comparing GPT-3 to GPT-2 is like comparing apples to, well, raisins, because the model is about that much larger.<ref name="ref_ebd63fab" />
# 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.<ref name="ref_ebd63fab" />
# Approximate size comparison of GPT-2, represented by a human skeleton, and GPT-3 approximated by the bones of a Tyrannosaurus rex.<ref name="ref_ebd63fab" />
# 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.<ref name="ref_902555e4">[https://slator.com/ma-and-funding/early-adopter-of-worlds-largest-language-model-othersideai-points-to-gpt-3-potential/ Early Adopter of World’s Largest Language Model, OthersideAI, Points to GPT-3 Potential]</ref>
# Contrary to earlier reports that it was “built entirely on GPT-3,” OthersideAI is actually built on a three-part system.<ref name="ref_902555e4" />
# CEO Matt Shumer told Slator, “Using GPT-3 would lead to extremely inconsistent results, and a product that wouldn’t be very beneficial.<ref name="ref_902555e4" />
# More broadly, the launching of OthersideAI highlights that all the hype around GPT-3 may not have been misplaced.<ref name="ref_902555e4" />
# Therefore, GPT-3 is extremely powerful without understanding a single word it produces.<ref name="ref_5a07b0e3">[https://www.byteant.com/blog/openai-gpt-3-how-it-works-why-it-matters/ OpenAI GPT-3: how it works & why it matters]</ref>
# Designer Jordan Singer used GPT-3 to build a Figma plugin and called it “Designer”.<ref name="ref_5a07b0e3" />
# GPT-3 can code in Python, CSS, JSX.<ref name="ref_5a07b0e3" />
# Serving as a universal tool for programmers, GPT-3 is another step forward to this simple interaction with software systems.<ref name="ref_5a07b0e3" />
# GPT-3 is an AI language generation model created by OpenAI that automatically produces human-sounding language at scale.<ref name="ref_ad3473ef">[https://www.marketingaiinstitute.com/blog/how-to-cut-through-the-hype-of-gpt-3 How to Cut Through the Hype of GPT-3]</ref>
# In giving GPT-3 a Turing Test, Kevin Lacker reveals that GPT-3 possesses no expertise and is “still clearly subhuman” in some areas.<ref name="ref_ad3473ef" />
# GPT-3 can also, unfortunately, create more insidious results than nonsensical sentences.<ref name="ref_ad3473ef" />
# 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.<ref name="ref_7cf114c3">[https://www.flowrite.com/blog/how-we-got-access-to-gpt-3-in-5-days How we got access to GPT-3 in 5 days]</ref>
# Not everyone can access the GPT-3 API, though – at least just yet.<ref name="ref_7cf114c3" />
===소스===
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GPT-3 - 편집 역사

2021년 2월 17일 (수) 08:19에 Pythagoras0님의 편집

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← 이전 판		2020년 12월 26일 (토) 12:23 판
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