The next update from OpenAI, the industry leader in generative AI chatbots, promises more nuanced, human-like conversations for more precise applications.

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Sam Altman moderated OpenAI’s inaugural developer conference on November 6 in San Francisco.

Image source: OpenAI/YouTube

OpenAI launched GPT-4 Turbo today at its first developer conference, describing it as a more powerful and cost-effective successor to GPT-4. The update provides enhanced context handling and the flexibility to fine-tune to meet user needs.

GPT-4 Turbo comes in two versions: one centered around text and another that also processes images. According to OpenAI, GPT-4 Turbo is “optimized for performance” and is priced as low as $0.01 per 1,000 text tokens and $0.03 per 1,000 image tokens, nearly a third of the price of GPT-4.

ChatGPT is tailor-made for you

What makes GPT-4 Turbo so special?

“Fine-tuning improves few-shot learning by training on more examples beyond the prompt, allowing you to achieve better results on a wide range of tasks,” OpenAI explains. Essentially, fine-tuning bridges the gap between general-purpose AI models and custom solutions tailored for specific applications. It promises “higher quality results than prompts, token savings through shorter prompts, and faster request responses.”

Fine-tuning involves feeding a model large amounts of custom data to learn specific behaviors, turning a large, general-purpose model like GPT-4 into a specialized tool for a niche task without having to build an entirely new model. For example, a model tuned to medical information will provide more accurate results and will “speak” more like a doctor.

A good analogy can be seen in the world of image generators: fine-tuned StableDiffusion models tend to produce better images than the original StableDiffusion XL or 1.5 because they are learned from specialized data.

Prior to this innovation, OpenAI allowed limited modification of the behavior of its LLMs through custom instructions. This is already a significant leap in quality for those seeking customization of OpenAI models. Fine-tuning improves on this by introducing new data, tone, context, and voice to the model dataset.

The value of fine-tuning is significant. As AI becomes more integrated into our daily lives, the need for models that are adapted to specific needs grows.

"Fine-tuning OpenAI text generation models can make them better suited for specific applications, but this requires careful investment of time and effort," OpenAI states in its official guide.

The company has been continually enhancing the context, multimodal capabilities, and accuracy of its models. With today’s announcement, this capability is unmatched among mainstream closed-source LLMs like Claude or Google’s Bard.

While open-source LLMs like LlaMA or Mistral can be fine-tuned, they fall short in terms of functionality and professional usability.

The launch of GPT-4 Turbo and its emphasis on fine-tuning marks a major shift in AI technology. Users can expect more personalized and efficient interactions, with potential impacts ranging from customer support to content creation. #OpenAI  #GPT4