OpenAI Introduces New Tools for Developers to Accelerate AI Model Creation
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OpenAI Introduces New Tools for Developers to Accelerate AI Model Creation

A OpenAI unveiled a series of new tools on Tuesday (1st) that make it easierariam for developers to create models based on their technology artificial intelligence (AI), while the creator of the ChatGPT struggle with technology giants to keep up with the race for Generative AI.

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The startup supported by Microsoft said a new real-time tool, being released immediately for testing, would allow developers to build AI voice applications using a single set of instructions.

The process previously required developers to go through at least three steps: first transcribing the audio, then running the generated text model to arrive at an answer to the query, and finally using a separate text-to-speech model.

A large part of the revenue from OpenAI comes from companies using its services to build their own AI applications, making the launch of advanced capabilities a major selling point.

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Competition has also been heating up as tech giants including A, controller of Google, integrate AI models capable of processing different forms of information, such as video, audio and text, into their businesses.

A OpenAI expects revenue to jump to $11,6 billion next year from an estimated $3,7 billion in 2024. The company is also in the midst of a $6,5 billion fundraising that could value it at $150 billion.

As part of Tuesday's launch, the OpenAI presented a fine-tuning tool for models after training that would allow developers to improve the responses generated by their models using images and text.

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This fine-tuning process can include feedback from humans who feed the model examples of good and bad responses based on their answers.

Using images to adjust models gives themaria stronger image understanding capabilities, enabling applications such as enhanced visual search and improved object detection for autonomous vehicles, the OpenAI.

The startup also unveiled a tool that would allow smaller models to learn from larger, established models, along with “Prompt Caching” which cuts some development costs in half by reusing chunks of text that the AI ​​has previously processed.

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