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Meta to Launch OpenAI’s Rival Advanced Open-Source AI

Meta’s AI revolution aims to challenge OpenAI with cutting-edge technology, expanding its AI services across platforms like Instagram and Facebook.

Jay Hicks

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Meta is going to revolutionize AI technology by introducing the most advanced form of AI technology to beat down OpenAI. The parent and modified company of social platforms i.e., Instagram and Facebook has been reportedly working on expanding its services in AI technology.  Meta challenges to introduce the most advanced and efficient Open-source AI rival of OpenAI.

Wall Street Journal’s Report

This latest news came into the market through the Wall Street Journal (WSJ) that Meta is developing a new open-source AI model to beat down OpenAI. WSJ reported that Meta claims to be working on the most advanced and efficient open-source AI model which will be available to users in a short time.

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In the first quarter of 2023, Meta also launched the LIama2 model and now aims to launch a new AI model which is going to be more advanced and unique of its kind. WSJ invites other firms to revolutionize the technological world with different ideas as Meta is doing to expand its technological and AI approach. WSJ disclosed that Meta is also employing H100 semiconductor chips of Nvidia to build high-level secure data centres.  H100 semiconductor chips are considered to be the most advanced and powerful chips available in the market.

Meta’s Growing Technology

Resources say that Llma has been trained above 70B parameters but OpenAI hasn’t released the exact numbers of parameters. It is estimated that roughly 1.5 trillion parameters have been utilized for OpenAI’s GPT-4 version. Moreover, Meta is going to start training for Large Language Model (LLM) in the 1st quarter of 2024 to make sure it’s released in the coming consecutive year.

Microsoft is on the backend of OpenAI and as assured of its collaboration with Llma2 the availability of its cloud-computing platform named Azure. However, it is more likely that Meta will train the model on its own platform.

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