![]() It’s an area of ongoing research and its applications are still not clear. It would be able to interlink data streams from different modalities to create an embedding space. It may also deal with text, audio, images, videos, depth data, and temperature. However, with GPT-5, OpenAI may take a big leap in making it truly multimodal. Sure, the capability has not been added to GPT-4 yet, but OpenAI may possibly release the feature in a few months. While GPT-4 has been announced as a multimodal AI model, it deals with only two types of data i.e. It must be on OpenAI’s wishlist to improve performance in the upcoming GPT-5 model, especially after the launch of Google’s much-faster PaLM 2 model, which you can try right now. Developers are already berating the fact that GPT-4 API calls frequently stop responding and they are forced to use the GPT-3.5 model in production. Tested average response on a fresh context for "Can you show me a basic scatter matplotlib example?"Ī huge chunk of OpenAI revenue comes from enterprises and businesses, so yeah, GPT-5 must not only be cheaper but also faster to return output. In our recent explainer on Google’s PaLM 2 model, we found that PaLM 2 is quite smaller in size and that results in quick performance.Ĭame back to a project I was working on with OpenAI GPT-4 API, noticed the API response times were pretty slow. With such a huge infrastructure, it becomes very costly to run and maintain the GPT-4 model. It means that GPT-4 uses 16 different models for different tasks and has 1.8 trillion parameters. According to a recent SemiAnalysis report, GPT-4 is not one dense model, but based on the “ Mixture of Experts” architecture. Whereas, the older GPT-3.5-turbo model is 15x cheaper ($0.002 per 1K tokens) than GPT-4. Next, we already know that GPT-4 is expensive to run ($0.03 per 1K tokens) and the inference time is also higher. So it’s highly likely that GPT-5 will hallucinate even less than GPT-4. I have been using the GPT-4 model for a lot of tasks lately, and it has so far given factual responses only. Now, it’s expected that OpenAI would reduce hallucination to less than 10% in GPT-5, which would be huge for making LLM models trustworthy. That’s a huge leap in combating hallucination. It’s very close to touching the 80% mark in accuracy tests across categories. Now, GPT-4 is 82% less likely to respond to inaccurate and disallowed content. According to OpenAI, GPT-4 scored 40% higher than GPT-3.5 in internal adversarially-designed factual evaluations under all nine categories.
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