Me chatGPT kiyanne matanan pissuwak. Mechchara hondata respond karanne kohomada man made AI ekak? Meka boruwak wenna barida? Kawruhari horen kattiya daagena karana wadak wenna barida data collect karala salli hamba karanna??
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ChatGPT aththakda?
- Thread starter Imgonnaletyoudown
- Start date
ඔව් ඔව් එහෙම තමා. මාත් මේ එකේ වැඩ කරලා ආවේ දැන්
ඔව් ඔව් එහෙම තමා. මාත් මේ එකේ වැඩ කරලා ආවේ දැන් 
Matat me tike mekata enna une natte math eke wada kala me tike campus wala assignment wadi nisa wada poddk wadi
I asked it from GPT , yet this was the answer 
" ChatGPT's efficiency and descriptiveness stem from its underlying architecture and training methodology. Here are a few reasons why it excels in these aspects:
and then from GEMINI
ChatGPT's efficiency and descriptiveness likely stem from a combination of factors:
Training Data: Large language models like ChatGPT are trained on massive datasets of text and code. This data includes books, articles, code repositories, and more. This vast amount of information allows ChatGPT to learn complex patterns in language use and generate text that is both comprehensive and informative.
Algorithmic Design: ChatGPT's underlying algorithms are designed to predict the next word in a sequence. These algorithms are constantly being refined to improve their accuracy and fluency. This allows ChatGPT to not only generate grammatically correct sentences but also choose words that effectively convey the intended meaning.
Focus on Statistical Patterns: Language models like ChatGPT don't have inherent understanding but identify statistical patterns in the training data. These patterns include how words are used together, the flow of ideas in different contexts, and the use of descriptive language. By recognizing these patterns, ChatGPT can generate text that is not only factual but also engaging and informative.
Here's a comparison to further illustrate this:
Imagine a chef who has access to a massive pantry filled with every imaginable ingredient (training data). The chef (algorithm) has studied countless recipes (patterns) and can predict which ingredients often go well together to create a delicious dish (descriptive text). However, the chef doesn't inherently understand the taste or the nutritional value of the ingredients, just the successful combinations observed in the past.
It's important to remember that while ChatGPT can be efficient and descriptive, it may not always capture the nuances of human language or understand the deeper meaning behind the text it generates.
" ChatGPT's efficiency and descriptiveness stem from its underlying architecture and training methodology. Here are a few reasons why it excels in these aspects:
- Transformer Architecture: ChatGPT is built on a Transformer architecture, which is highly efficient for processing sequences of data. Transformers allow for parallel computation, making them faster and more scalable compared to sequential models like RNNs or LSTMs.
- Large-scale Training Data: ChatGPT has been trained on vast amounts of text data from diverse sources, which allows it to understand and generate language with a high level of accuracy and richness. This extensive training data provides it with a broad understanding of various topics and contexts.
- Fine-tuning: ChatGPT can be fine-tuned on specific tasks or domains, which further enhances its efficiency and descriptiveness for particular applications. Fine-tuning adapts the model to the specific nuances and vocabulary of the target domain, making its responses even more accurate and detailed.
- Attention Mechanism: The attention mechanism in Transformers allows ChatGPT to focus on relevant parts of the input text when generating responses. This helps it produce more contextually appropriate and informative answers by attending to the most important aspects of the conversation.
- Continuous Learning: ChatGPT continually learns from interactions with users, incorporating new information and refining its responses over time. This adaptive learning process helps it stay up-to-date and improve its efficiency and descriptiveness in real-world conversations.
and then from GEMINI
ChatGPT's efficiency and descriptiveness likely stem from a combination of factors:
Training Data: Large language models like ChatGPT are trained on massive datasets of text and code. This data includes books, articles, code repositories, and more. This vast amount of information allows ChatGPT to learn complex patterns in language use and generate text that is both comprehensive and informative.
Algorithmic Design: ChatGPT's underlying algorithms are designed to predict the next word in a sequence. These algorithms are constantly being refined to improve their accuracy and fluency. This allows ChatGPT to not only generate grammatically correct sentences but also choose words that effectively convey the intended meaning.
Focus on Statistical Patterns: Language models like ChatGPT don't have inherent understanding but identify statistical patterns in the training data. These patterns include how words are used together, the flow of ideas in different contexts, and the use of descriptive language. By recognizing these patterns, ChatGPT can generate text that is not only factual but also engaging and informative.
Here's a comparison to further illustrate this:
Imagine a chef who has access to a massive pantry filled with every imaginable ingredient (training data). The chef (algorithm) has studied countless recipes (patterns) and can predict which ingredients often go well together to create a delicious dish (descriptive text). However, the chef doesn't inherently understand the taste or the nutritional value of the ingredients, just the successful combinations observed in the past.
It's important to remember that while ChatGPT can be efficient and descriptive, it may not always capture the nuances of human language or understand the deeper meaning behind the text it generates.
වචන මිලියන එකට ඩොලර් 1.5 gpt 3.5 desk එකේ. 4 නන් 15ක් දෙනවා. එකේ වැඩ අමාරුයි ඒ උනාට ගොඩක් හොදට උත්තර දෙන්න ඕනි.Padiya keeyada umbage?
Dalle 3 එකේ චිත්ර අදින උන්ට නන් සෙහෙන්න ගෙවනවා.
Still amazing and unbelievable but true..I asked it from GPT , yet this was the answer
" ChatGPT's efficiency and descriptiveness stem from its underlying architecture and training methodology. Here are a few reasons why it excels in these aspects:
Overall, ChatGPT's efficiency and descriptiveness are the result of a combination of advanced architecture, extensive training data, fine-tuning capabilities, attention mechanisms, and continuous learning processes.
- Transformer Architecture: ChatGPT is built on a Transformer architecture, which is highly efficient for processing sequences of data. Transformers allow for parallel computation, making them faster and more scalable compared to sequential models like RNNs or LSTMs.
- Large-scale Training Data: ChatGPT has been trained on vast amounts of text data from diverse sources, which allows it to understand and generate language with a high level of accuracy and richness. This extensive training data provides it with a broad understanding of various topics and contexts.
- Fine-tuning: ChatGPT can be fine-tuned on specific tasks or domains, which further enhances its efficiency and descriptiveness for particular applications. Fine-tuning adapts the model to the specific nuances and vocabulary of the target domain, making its responses even more accurate and detailed.
- Attention Mechanism: The attention mechanism in Transformers allows ChatGPT to focus on relevant parts of the input text when generating responses. This helps it produce more contextually appropriate and informative answers by attending to the most important aspects of the conversation.
- Continuous Learning: ChatGPT continually learns from interactions with users, incorporating new information and refining its responses over time. This adaptive learning process helps it stay up-to-date and improve its efficiency and descriptiveness in real-world conversations.
අයියා USද Europeද?
අපි නම් ඉතින් ලංකාවේ outsource එකේ වැඩ
පොඩ්ඩක් වැරදුණ ගමන් supervisor whip කරනවා, female කෙනෙක් නම් කටට ගන්න ඕන
10-hour shift එකක් දාලා ආයේ ගෙදර ගිහිල්ලත් testing කරන්න ඕන
මේ මාසේ අන්තිමට අපි labor department එකට complain කරන්න ඉන්නේ
අර පොල් ලෙලි ගහන මැෂින් එක වගේ නේද ?ලොකු පෙට්ටියක් හදල දෙපැත්තෙන් හිල් දෙකක් තියනව in , out කියලා.. In එකෙන් දාන පොල් ගෙඩිය ඇතුලේ එකෙක් උලක් තියන් ලෙලි ගහන out එකෙන් එලියට දානව.
Sorry කලබලේට වචන පැටලුනා. ආයේ හදන්නත් බෑ නේ ලොකු සීන් නේ එහෙමම හිටියා.
කටට ගන්න තියා වතුර උගුරක් බොන්න වෙලා නෑ අපේ අයට. වැරදුනා වගේ නන් අන්තිමට කෝකටත් මේ වගේත් බලන්න කියල වෙබ් ලින්ක් දෙක තුනක් යවන්න තියන්නේ.අයියා USද Europeද?
අපි නම් ඉතින් ලංකාවේ outsource එකේ වැඩ
පොඩ්ඩක් වැරදුණ ගමන් supervisor whip කරනවා, female කෙනෙක් නම් කටට ගන්න ඕන
10-hour shift එකක් දාලා ආයේ ගෙදර ගිහිල්ලත් testing කරන්න ඕන
මේ මාසේ අන්තිමට අපි labor department එකට complain කරන්න ඉන්නේ
------ Post added on Apr 24, 2024 at 9:15 PM
You're correct, More than 1000 people are working in an underground bunker near the Arizona desert. Some dude stepped up and wrote a book about it "sad side of Chatgpt workers". Unfortunately, the US government burned that book before it could be published.
