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𝗧𝗵𝗲 𝗪𝗼𝗿𝗹𝗱’𝘀 𝗙𝗶𝗿𝘀𝘁 𝗖𝗵𝗮𝘁𝗚𝗣𝗧𝟰-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗔𝗽𝗽 𝗧𝘂𝗿𝗻𝘀 𝗔𝗻𝘆 “𝗨𝗥𝗟, 𝗪𝗲𝗯𝘀𝗶𝘁𝗲, 𝗔𝗿𝘁𝗶𝗰𝗹𝗲, 𝗞𝗲𝘆𝘄𝗼𝗿𝗱, 𝗕𝗹𝗼𝗴, 𝗣𝗗𝗙” 𝗜𝗻𝘁𝗼 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗔𝘂𝗱𝗶𝗼𝗕𝗼𝗼𝗸𝘀 𝗢𝗿 𝗣𝗼𝗱𝗰𝗮𝘀𝘁𝘀 𝗜𝗻 𝟲𝟲𝟬 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗩𝗼𝗶𝗰𝗲𝘀 𝗔𝗻𝗱 𝟴𝟬 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀…
Vox AI lets you utilize Siri-like voice instructions to create audiobooks, podcasts, or voiceovers for VSL, TV Commercial, Webinars, Sales Videos, etc.
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Video review for Front End only VOX AI
VOX AI – Text From This Video
Okay. GPT-3 and GPT-4 struggle with this.
No code. This list affects nations globally. Before considering major language model concerns, let’s review basics.
their work. Famous ChatGPT. An app overlays a large language model. GPT version. ChatGPT interprets language.
Phone support and auto-completion use it. GPT resembles a used bookstore. Shop books taught it. Big language models.
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Language learners read a lot. Covering responses tests their process.
then verifying. Based on data, they may recognize sentiment, summarize, translate, and suggest.
Last line by ChatGPT. Videos need that. Reading honed its skills. ChatGPT can rewrite Shakespeare, but it reads all of it, wasting my paper. I’ll show.
Yeah? Good? Okay. 2008–present Common Crawl printing. Crawl and index all webpages.
They list every language they think they’ve indexed. English predominates. Crawls are almost 40% English. German: DEU. Indexes here.
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It’s 6% each time. It’s 2023. FIN: Finnish.
Many pages. 0.4% of scan. This bookstore needs stock. Languages are prioritized.
NLP research covers 20 of the world’s 7,000 languages, according to a report. Go back. Ruth-Ann Armstrong. She’s doing what many researchers want.
new datasets. These 20 languages are resource-rich. Low-resource languages aren’t online, hence they’re not language databases. AI cannot comprehend.
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Our used bookshop. James Patterson, Anne Tyler, and Dan Brown abound. English, German, Chinese. High-resource languages. Rare books.
Low-resource languages. Many models are unaware of them.
Jamaican here. Jamaican patois and English dominate.
Armstrong and her coauthors wanted to create a dataset to explain this largely spoken language without creating ChatGPT. Their model needed comprehension. Armstrong used Jamaican patois. 2 columns. She agreed, disagreed, or was neutral.
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Start here. A’s heated. Hot B. So entailment. They agree.
Try it: Or contradiction? Contradiction. One more. Neutral. They’re unconnected.
She finished 650 occurrences. Worked hard. Jamaican patois is not on my vast Common Crawl list. Catalan scholars were consulted.
Catalan language models are massively tested. Most Spaniards speak it. GPT-3 is 92% English. German 1.
4% words. Spain 0.7%. Catalan last. The training set lacks Catalan terms. VOX AI OTO Overview
01%. Works fine. Is this issue different? Catalan data. Catalan 0.
2335 %. A little. GPT-3 and GPT-4 in the large corporation fared well on little data. GPT-3 gave researchers three Catalan sentences. They inserted real sentences.
Three natives evaluated them. Our test. Well-performing machine. One catch. It’s fine.
It’s worthwhile to train and test a language-specific model. Not performance, but transparency and data. Common Crawl reports indexing…
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Catalan words. GPT-3 only read 140 Catalan pages. Think novella. Dependence on a few companies’ success or goodwill is a problem. One enterprise may drop Catalan.
Catalan News stated Google stopped searching Catalan sites. Common Crawl trained GPT-3. Unknown GPT-4. This language model’s further components are unknown. Meta, Microsoft, Baidu, Open AI, and Google run these bookshops.
They select novels without disclosing their writers. Some seek a bookshop-near library. Paris’ supercomputer rarely ran. I was discussing it.
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creator, “Nobody uses this GPU.” What’s available? Thomas Wolf co-founded Hugging Face, an online AI research community, and Big Science’s BLOOM.
open-source multilingual model. We realized that training it in several languages was preferable. Hugging Face might become a large collaboration if we involve many people. We tried to include everyone.
Top Wikipedia languages were covered. When possible, low-resource languages. Languages are resource-poor. African languages. We asked local communities what data was valuable and how we could get it.
The data’s origin is crucial. Open-source differences. Library books. I’ll find English. Honest English speakers.
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Major corporations’ models target me. The target audience needs all languages covered, even if over 40% of the Common Crawl is in English. English with a Jamaican accent.
Siri first struggled with my accent. Adding voice assistant training datasets.
Accents aid. Extend that. We’re expanding language technologies. Trust them to spread this model.
Trust Microsoft. Don’t trust them…
Our language. Catalan speakers. A minor language may have a…
many speakers with modest digital footprints. They’ll go.
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