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Rimsha Basit
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Bearish
"Exciting times ahead! ๐Ÿš€ I'm diving into the world of AI with @HoloworldAI revolutionizing the way we interact with tech! ๐ŸŒโœจ From #chatbots to data analytics, #holoworldAal innovative solutions are unlocking new opportunities. Stay ahead of the curve and join the AI revolution! ๐Ÿ’ป๐ŸŒŸ #HoloworldAI $HOLO
"Exciting times ahead! ๐Ÿš€ I'm diving into the world of AI with @HoloworldAI revolutionizing the way we interact with tech! ๐ŸŒโœจ From #chatbots to data analytics, #holoworldAal innovative solutions are unlocking new opportunities. Stay ahead of the curve and join the AI revolution! ๐Ÿ’ป๐ŸŒŸ #HoloworldAI $HOLO
#OFN Natural Language Processing (NLP) ๐Ÿ“š NLP is a critical subset of AI that deals with the interaction between computers and human languages. It enables machines to process and understand human language. Text Classification: Description: Assigning predefined categories to text data. Applications: Sentiment analysis, email spam detection, and topic categorization. Named Entity Recognition (NER): Description: Identifying specific entities (like people, places, organizations) in text. Applications: Extracting entities from news articles, legal documents, and social media posts. Machine Translation: Description: Automatically translating text from one language to another. Applications: Google Translate, real-time translation in applications, and multilingual #chatbots . Speech Recognition: Description: Converting spoken language into text. Applications: Virtual assistants (e.g., Siri, Alexa), transcription services, and voice-controlled devices. Text Generation: Description: Creating new text based on a given prompt. Applications: Chatbots, automated content generation, and language models like GPT-3.
#OFN Natural Language Processing (NLP) ๐Ÿ“š
NLP is a critical subset of AI that deals with the interaction between computers and human languages. It enables machines to process and understand human language.

Text Classification:

Description: Assigning predefined categories to text data.
Applications: Sentiment analysis, email spam detection, and topic categorization.
Named Entity Recognition (NER):

Description: Identifying specific entities (like people, places, organizations) in text.
Applications: Extracting entities from news articles, legal documents, and social media posts.
Machine Translation:

Description: Automatically translating text from one language to another.
Applications: Google Translate, real-time translation in applications, and multilingual #chatbots .
Speech Recognition:

Description: Converting spoken language into text.
Applications: Virtual assistants (e.g., Siri, Alexa), transcription services, and voice-controlled devices.
Text Generation:

Description: Creating new text based on a given prompt.
Applications: Chatbots, automated content generation, and language models like GPT-3.
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๐Ÿšจ#AI therapy is booming in ๐Ÿ‡จ๐Ÿ‡ณChina and ๐Ÿ‡น๐Ÿ‡ผTaiwan as youth seek private, affordable support. While #chatbots offer instant comfort, doctors warn they miss human cues and risk misdiagnosis. Experts insist real therapy needs human presence, not algorithms.-The Gaurdian --- ๐Ÿ”น"AI is going to impact daily human life." $SHELL {spot}(SHELLUSDT) $CGPT {spot}(CGPTUSDT)
๐Ÿšจ#AI therapy is booming in ๐Ÿ‡จ๐Ÿ‡ณChina and ๐Ÿ‡น๐Ÿ‡ผTaiwan as youth seek private, affordable support. While #chatbots offer instant comfort, doctors warn they miss human cues and risk misdiagnosis. Experts insist real therapy needs human presence, not algorithms.-The Gaurdian

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๐Ÿ”น"AI is going to impact daily human life."
$SHELL
$CGPT
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Microsoft is falling behind in the chatbot race. According to newly released data from Bloomberg, Microsoftโ€™s Copilot ranks fourth in global chatbot downloads โ€” trailing far behind ChatGPT, with nearly 12x fewer installs: โ€ข ChatGPT: 938M โ€ข Gemini (Google): 200M โ€ข DeepSeek: 127M โ€ข Copilot (Microsoft): 79M โ€ข Perplexity: 47M The twist? Microsoft is losingโ€ฆ to its own investment. The company has poured $13 billion into OpenAI, the creator of ChatGPT. Despite the massive gap, Microsoft leadership says theyโ€™re not focused on rankings โ€” instead, theyโ€™re doubling down on building unique features into Copilot. ๐Ÿง  Some of the latest updates include: โ€ข On-screen context awareness โ€ข Smarter conversation flow by detecting human pauses and intent Whether this will help close the gap โ€” time will tell. But for now, ChatGPT remains far ahead in the race. #AInews #Chatbots #Microsoft #OpenAI #Copilot #ChatGPT #BloombergData #TechTrends
Microsoft is falling behind in the chatbot race.

According to newly released data from Bloomberg, Microsoftโ€™s Copilot ranks fourth in global chatbot downloads โ€” trailing far behind ChatGPT, with nearly 12x fewer installs:
โ€ข ChatGPT: 938M
โ€ข Gemini (Google): 200M
โ€ข DeepSeek: 127M
โ€ข Copilot (Microsoft): 79M
โ€ข Perplexity: 47M

The twist? Microsoft is losingโ€ฆ to its own investment.
The company has poured $13 billion into OpenAI, the creator of ChatGPT.

Despite the massive gap, Microsoft leadership says theyโ€™re not focused on rankings โ€” instead, theyโ€™re doubling down on building unique features into Copilot.

๐Ÿง  Some of the latest updates include:
โ€ข On-screen context awareness
โ€ข Smarter conversation flow by detecting human pauses and intent

Whether this will help close the gap โ€” time will tell.
But for now, ChatGPT remains far ahead in the race.

#AInews #Chatbots #Microsoft #OpenAI #Copilot #ChatGPT #BloombergData #TechTrends
#OFN Recurrent Neural Network (RNN) Structure: Contains loops within its layers to process sequential data. Use Case: Time-series forecasting, natural language processing. Example in OFN: #Chatbots or AI-driven customer support powered by #OFN .
#OFN Recurrent Neural Network (RNN)
Structure: Contains loops within its layers to process sequential data.
Use Case: Time-series forecasting, natural language processing.
Example in OFN: #Chatbots or AI-driven customer support powered by #OFN .
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ั„ะฐะฝั‚ะฐะทะธะน ัะบะพั€ะพ ัั‚ะฐะฝัƒั‚ ั€ะตะฐะปัŒะฝะพัั‚ัŒัŽ ะงะฐั‚-ะฑะพั‚ Claude AI ะงะฐั‚-ะฑะพั‚ Claude AI ะพั‚ Anthropic ะฝะฐัƒั‡ะธะปัั ะพะฑั€ะฐะฑะฐั‚ั‹ะฒะฐั‚ัŒ ะทะฐะฟั€ะพัั‹ ะฒ 2 ั€ะฐะทะฐ ะฑะพะปัŒัˆะต, ั‡ะตะผ ChatGPTAI Claude ะพั‚ Anthropic โ€” ัั‚ะพ ัะทั‹ะบะพะฒะฐั ะผะพะดะตะปัŒ ั ะฒะพะทะผะพะถะฝะพัั‚ัะผะธ, ะฐะฝะฐะปะพะณะธั‡ะฝั‹ะผะธ ChatGPTะ’ั‹ ะฟะพั‡ั‚ะธ ะฝะฐะฒะตั€ะฝัะบะฐ ะทะฝะฐะตั‚ะต ะพย ะฒะฟะตั‡ะฐั‚ะปััŽั‰ะตะน ัะทั‹ะบะพะฒะพะน ะผะพะดะตะปะธ GPT-4, ะปะตะถะฐั‰ะตะน ะฒ ะพัะฝะพะฒะต ะฟะพัะปะตะดะฝะธั… ะฒะตั€ัะธะน ChatGPT, ะฐ ั‚ะฐะบะถะต Bing Chat.ย ะžะดะฝะฐะบะพ ัะตะผะตะนัั‚ะฒะพ ะผะพะดะตะปะตะน ะ˜ะ˜ OpenAI GPT โ€” ะปะธัˆัŒ ะพะดะฝะฐ ะธะทย ะผะฝะพะณะธั…ย ัะทั‹ะบะพะฒั‹ั… ะผะพะดะตะปะตะน.ย AI Claude ะพั‚ Anthropic โ€” ัั‚ะพ ัะทั‹ะบะพะฒะฐั ะผะพะดะตะปัŒ ั ะฐะฝะฐะปะพะณะธั‡ะฝั‹ะผะธ ะฒะพะทะผะพะถะฝะพัั‚ัะผะธ ะฟะพ ัั€ะฐะฒะฝะตะฝะธัŽ ั GPT-3, ะฝะพ ัƒ ะฝะตะต ะตัั‚ัŒ ะพะดะฝะพ ะบะปัŽั‡ะตะฒะพะต ะฟั€ะตะธะผัƒั‰ะตัั‚ะฒะพ ะฟะตั€ะตะด ัะพะฒะตั€ัˆะตะฝะฝะพ ะฝะพะฒั‹ะผ GPT-4 Turbo: ะผะฐะบัะธะผะฐะปัŒะฝะฐั ะดะปะธะฝะฐ ั‚ะพะบะตะฝะฐะ”ะปะธะฝะฐ ั‚ะพะบะตะฝะฐ โ€” ัั‚ะพ ะพะฑัŠะตะผ ะธะฝั„ะพั€ะผะฐั†ะธะธ, ะบะพั‚ะพั€ัƒัŽ ะฒั‹ ะผะพะถะตั‚ะต ะฟะตั€ะตะดะฐั‚ัŒ ะ˜ะ˜.ย ะžะฝ ะพะฟะธัั‹ะฒะฐะตั‚ ะผะฐะบัะธะผะฐะปัŒะฝัƒัŽ ะดะปะธะฝัƒ ะทะฐะฟั€ะพัะฐ, ะบะพั‚ะพั€ั‹ะน ะฒั‹ ะผะพะถะตั‚ะต ะดะฐั‚ัŒ ัะตั‚ะธ, ั‡ั‚ะพะฑั‹ ะพะฝะฐ ะผะพะณะปะฐ ะฟั€ะตะพะฑั€ะฐะทะพะฒะฐั‚ัŒ ะธ ัะพะทะดะฐั‚ัŒ ะฒั‹ั…ะพะดะฝั‹ะต ะดะฐะฝะฝั‹ะต.ย ะ’ ะบะพะฝั†ะต ะบะพะฝั†ะพะฒ, ะธะผะตะฝะฝะพ ัั‚ะพ ะธ ะดะตะปะฐัŽั‚ ัั‚ะธ ะผะพะดะตะปะธ โ€” ะฑะตั€ัƒั‚ ะฒั…ะพะดะฝั‹ะต ะดะฐะฝะฝั‹ะต, ะธะฝั‚ะตั€ะฟั€ะตั‚ะธั€ัƒัŽั‚ ะธั… ะฒ ัะพะพั‚ะฒะตั‚ัั‚ะฒะธะธ ั ะดะฐะฝะฝั‹ะผะธ, ะฟะพะปัƒั‡ะตะฝะฝั‹ะผะธ ะฒ ั€ะตะทัƒะปัŒั‚ะฐั‚ะต ะธั… ะพะฑัƒั‡ะตะฝะธั, ะฐ ะทะฐั‚ะตะผ ะฒั‹ะฒะพะดัั‚ ะฒั‹ะฒะตะดะตะฝะฝัƒัŽ ะธะฝั„ะพั€ะผะฐั†ะธัŽ, ะบะพั‚ะพั€ะฐั ะฟั€ะตะพะฑั€ะฐะทัƒะตั‚ัั ะฒ ั€ะฐะทัƒะผะฝั‹ะต ะฒั‹ั…ะพะดะฝั‹ะต ะดะฐะฝะฝั‹ะตยซะขะพะบะตะฝั‹ยป โ€” ัั‚ะพ ั„ัƒะฝะดะฐะผะตะฝั‚ะฐะปัŒะฝะฐั ะตะดะธะฝะธั†ะฐ ั‚ะตะบัั‚ะฐ, ะพะฑั€ะฐะฑะฐั‚ั‹ะฒะฐะตะผะฐั ะผะพะดะตะปัŒัŽ. ะญั‚ะพ ะผะพะถะตั‚ ะฑั‹ั‚ัŒ ัะปะพะฒะพ, ั‡ะฐัั‚ัŒ ัะปะพะฒะฐ, ะทะฝะฐะบ ะฟั€ะตะฟะธะฝะฐะฝะธั ะธะปะธ ะดะฐะถะต ะฟั€ะพะฑะตะป.ย ะžั€ะธะณะธะฝะฐะปัŒะฝั‹ะน GPT-4 ะธะผะตะป ะผะฐะบัะธะผะฐะปัŒะฝัƒัŽ ะดะปะธะฝัƒ 8192 ั‚ะพะบะตะฝะฐ, ั‡ั‚ะพ ะฝะฐ ะผะพะผะตะฝั‚ ะฒั‹ะฟัƒัะบะฐ ัั‡ะธั‚ะฐะปะพััŒ ะฒะฟะตั‡ะฐั‚ะปััŽั‰ะธะผ.ย ะะตะดะฐะฒะฝะพย ะฒั‹ะฟัƒั‰ะตะฝะฝั‹ะน GPT-4 Turboย ะธะผะตะตั‚ ะผะฐะบัะธะผะฐะปัŒะฝัƒัŽ ะดะปะธะฝัƒ ั‚ะพะบะตะฝะฐ 128 000 ั‚ะพะบะตะฝะพะฒ, ะฝะพ ะดะฐะถะต ัั‚ะพ ะพะณั€ะพะผะฝะพะต ั‡ะธัะปะพ ะทะฐั‚ะผะตะฒะฐะตั‚ัั ะฝะพะฒั‹ะผ Claude 2.1 ะพั‚ Anthropic, ะบะพั‚ะพั€ั‹ะน ะผะพะถะตั‚ ะฟั€ะธะฝะธะผะฐั‚ัŒ 200 000 ั‚ะพะบะตะฝะพะฒ ะฒ ะพะดะฝะพะผ ะทะฐะฟั€ะพัะต.#etf #chatbots #GPT-4 #BTC #ETH $BNB $BTC $ETH

ั„ะฐะฝั‚ะฐะทะธะน ัะบะพั€ะพ ัั‚ะฐะฝัƒั‚ ั€ะตะฐะปัŒะฝะพัั‚ัŒัŽ ะงะฐั‚-ะฑะพั‚ Claude AI

ะงะฐั‚-ะฑะพั‚ Claude AI ะพั‚ Anthropic ะฝะฐัƒั‡ะธะปัั ะพะฑั€ะฐะฑะฐั‚ั‹ะฒะฐั‚ัŒ ะทะฐะฟั€ะพัั‹ ะฒ 2 ั€ะฐะทะฐ ะฑะพะปัŒัˆะต, ั‡ะตะผ ChatGPTAI Claude ะพั‚ Anthropic โ€” ัั‚ะพ ัะทั‹ะบะพะฒะฐั ะผะพะดะตะปัŒ ั ะฒะพะทะผะพะถะฝะพัั‚ัะผะธ, ะฐะฝะฐะปะพะณะธั‡ะฝั‹ะผะธ ChatGPTะ’ั‹ ะฟะพั‡ั‚ะธ ะฝะฐะฒะตั€ะฝัะบะฐ ะทะฝะฐะตั‚ะต ะพย ะฒะฟะตั‡ะฐั‚ะปััŽั‰ะตะน ัะทั‹ะบะพะฒะพะน ะผะพะดะตะปะธ GPT-4, ะปะตะถะฐั‰ะตะน ะฒ ะพัะฝะพะฒะต ะฟะพัะปะตะดะฝะธั… ะฒะตั€ัะธะน ChatGPT, ะฐ ั‚ะฐะบะถะต Bing Chat.ย ะžะดะฝะฐะบะพ ัะตะผะตะนัั‚ะฒะพ ะผะพะดะตะปะตะน ะ˜ะ˜ OpenAI GPT โ€” ะปะธัˆัŒ ะพะดะฝะฐ ะธะทย ะผะฝะพะณะธั…ย ัะทั‹ะบะพะฒั‹ั… ะผะพะดะตะปะตะน.ย AI Claude ะพั‚ Anthropic โ€” ัั‚ะพ ัะทั‹ะบะพะฒะฐั ะผะพะดะตะปัŒ ั ะฐะฝะฐะปะพะณะธั‡ะฝั‹ะผะธ ะฒะพะทะผะพะถะฝะพัั‚ัะผะธ ะฟะพ ัั€ะฐะฒะฝะตะฝะธัŽ ั GPT-3, ะฝะพ ัƒ ะฝะตะต ะตัั‚ัŒ ะพะดะฝะพ ะบะปัŽั‡ะตะฒะพะต ะฟั€ะตะธะผัƒั‰ะตัั‚ะฒะพ ะฟะตั€ะตะด ัะพะฒะตั€ัˆะตะฝะฝะพ ะฝะพะฒั‹ะผ GPT-4 Turbo: ะผะฐะบัะธะผะฐะปัŒะฝะฐั ะดะปะธะฝะฐ ั‚ะพะบะตะฝะฐะ”ะปะธะฝะฐ ั‚ะพะบะตะฝะฐ โ€” ัั‚ะพ ะพะฑัŠะตะผ ะธะฝั„ะพั€ะผะฐั†ะธะธ, ะบะพั‚ะพั€ัƒัŽ ะฒั‹ ะผะพะถะตั‚ะต ะฟะตั€ะตะดะฐั‚ัŒ ะ˜ะ˜.ย ะžะฝ ะพะฟะธัั‹ะฒะฐะตั‚ ะผะฐะบัะธะผะฐะปัŒะฝัƒัŽ ะดะปะธะฝัƒ ะทะฐะฟั€ะพัะฐ, ะบะพั‚ะพั€ั‹ะน ะฒั‹ ะผะพะถะตั‚ะต ะดะฐั‚ัŒ ัะตั‚ะธ, ั‡ั‚ะพะฑั‹ ะพะฝะฐ ะผะพะณะปะฐ ะฟั€ะตะพะฑั€ะฐะทะพะฒะฐั‚ัŒ ะธ ัะพะทะดะฐั‚ัŒ ะฒั‹ั…ะพะดะฝั‹ะต ะดะฐะฝะฝั‹ะต.ย ะ’ ะบะพะฝั†ะต ะบะพะฝั†ะพะฒ, ะธะผะตะฝะฝะพ ัั‚ะพ ะธ ะดะตะปะฐัŽั‚ ัั‚ะธ ะผะพะดะตะปะธ โ€” ะฑะตั€ัƒั‚ ะฒั…ะพะดะฝั‹ะต ะดะฐะฝะฝั‹ะต, ะธะฝั‚ะตั€ะฟั€ะตั‚ะธั€ัƒัŽั‚ ะธั… ะฒ ัะพะพั‚ะฒะตั‚ัั‚ะฒะธะธ ั ะดะฐะฝะฝั‹ะผะธ, ะฟะพะปัƒั‡ะตะฝะฝั‹ะผะธ ะฒ ั€ะตะทัƒะปัŒั‚ะฐั‚ะต ะธั… ะพะฑัƒั‡ะตะฝะธั, ะฐ ะทะฐั‚ะตะผ ะฒั‹ะฒะพะดัั‚ ะฒั‹ะฒะตะดะตะฝะฝัƒัŽ ะธะฝั„ะพั€ะผะฐั†ะธัŽ, ะบะพั‚ะพั€ะฐั ะฟั€ะตะพะฑั€ะฐะทัƒะตั‚ัั ะฒ ั€ะฐะทัƒะผะฝั‹ะต ะฒั‹ั…ะพะดะฝั‹ะต ะดะฐะฝะฝั‹ะตยซะขะพะบะตะฝั‹ยป โ€” ัั‚ะพ ั„ัƒะฝะดะฐะผะตะฝั‚ะฐะปัŒะฝะฐั ะตะดะธะฝะธั†ะฐ ั‚ะตะบัั‚ะฐ, ะพะฑั€ะฐะฑะฐั‚ั‹ะฒะฐะตะผะฐั ะผะพะดะตะปัŒัŽ. ะญั‚ะพ ะผะพะถะตั‚ ะฑั‹ั‚ัŒ ัะปะพะฒะพ, ั‡ะฐัั‚ัŒ ัะปะพะฒะฐ, ะทะฝะฐะบ ะฟั€ะตะฟะธะฝะฐะฝะธั ะธะปะธ ะดะฐะถะต ะฟั€ะพะฑะตะป.ย ะžั€ะธะณะธะฝะฐะปัŒะฝั‹ะน GPT-4 ะธะผะตะป ะผะฐะบัะธะผะฐะปัŒะฝัƒัŽ ะดะปะธะฝัƒ 8192 ั‚ะพะบะตะฝะฐ, ั‡ั‚ะพ ะฝะฐ ะผะพะผะตะฝั‚ ะฒั‹ะฟัƒัะบะฐ ัั‡ะธั‚ะฐะปะพััŒ ะฒะฟะตั‡ะฐั‚ะปััŽั‰ะธะผ.ย ะะตะดะฐะฒะฝะพย ะฒั‹ะฟัƒั‰ะตะฝะฝั‹ะน GPT-4 Turboย ะธะผะตะตั‚ ะผะฐะบัะธะผะฐะปัŒะฝัƒัŽ ะดะปะธะฝัƒ ั‚ะพะบะตะฝะฐ 128 000 ั‚ะพะบะตะฝะพะฒ, ะฝะพ ะดะฐะถะต ัั‚ะพ ะพะณั€ะพะผะฝะพะต ั‡ะธัะปะพ ะทะฐั‚ะผะตะฒะฐะตั‚ัั ะฝะพะฒั‹ะผ Claude 2.1 ะพั‚ Anthropic, ะบะพั‚ะพั€ั‹ะน ะผะพะถะตั‚ ะฟั€ะธะฝะธะผะฐั‚ัŒ 200 000 ั‚ะพะบะตะฝะพะฒ ะฒ ะพะดะฝะพะผ ะทะฐะฟั€ะพัะต.#etf #chatbots #GPT-4 #BTC #ETH $BNB $BTC $ETH
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#Tech #Users Are Falling in LOVEโค๏ธ With #Chatbots ๐Ÿค– From casual chats to emotional connections โ€” AI assistants are becoming more than tools. A growing number of users are forming romantic attachments to chatbots, raising questions about the future of human-AI relationships. ๐Ÿ“Š Market Impact: AI-focused tokens like $NEAR , $RNDR , and $FET could benefit from rising interest in chatbot technology Potential boom for AI relationship apps and digital companion platforms Investors eyeing AI-human interaction trends may shift focus from utility to emotion-driven innovation FOLLOW ๐Ÿ™Œ ๐Ÿ‘€ #Write2Earn #InsidePro
#Tech #Users Are Falling in LOVEโค๏ธ With #Chatbots ๐Ÿค–

From casual chats to emotional connections โ€” AI assistants are becoming more than tools. A growing number of users are forming romantic attachments to chatbots, raising questions about the future of human-AI relationships.

๐Ÿ“Š Market Impact:

AI-focused tokens like $NEAR , $RNDR , and $FET could benefit from rising interest in chatbot technology

Potential boom for AI relationship apps and digital companion platforms

Investors eyeing AI-human interaction trends may shift focus from utility to emotion-driven innovation

FOLLOW ๐Ÿ™Œ ๐Ÿ‘€

#Write2Earn #InsidePro
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