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OpenAI said developers with a “successful payment history” can access GPT-4. The company plans to open access to new developers later this month, after which it will begin increasing availability limits “depending on computing availability.”

GPT-4 can generate text (including code) and accept image and text input. It is an improvement over his text-only predecessor GPT-3.5, and performs “human-level” on several academic benchmarks and experts. Like his previous OpenAI GPT model, GPT-4 was trained using public data such as public web pages and data licensed from OpenAI.

Image compression functionality is not yet available in all his OpenAI clients. OpenAI is first testing with one of her partners, Be My Eyes. But it has not said when it will open up to a wider customer base.

OpenAI is one of the other modern but less capable text generation models (and one of the original models powering ChatGPT), GPT- 4 and GPT-3.5 Turbo can be adjusted. This has long been possible with some of OpenAI’s other text generation models. OpenAI says the feature is expected to arrive later this year.

In a related announcement today, OpenAI announced the general availability of its DALL-E 2 and Whisper APIs: DALL-E 2 is OpenAI’s imaging model and “Whisper” refers to the company’s speech-to-text model. increase. The company also said it plans to deprecate older models available through APIs in order to “optimize computing power.” (Over the past few months, OpenAI has struggled to keep up with the demand for generative models, largely thanks to the growing popularity of ChatGPT.)

As of January 4, 2024, certain older OpenAI models of him, specifically his GPT-3 and its derivatives, will no longer be available in favor of newer “GPT-3-based” models that are considered more computationally efficient. be replaced. Developers using older models will need to manually update their integrations by January 4th, and if they want to continue using the old tuned model after January 4th, they will need to update to the new GPT-3 base You need to adjust the replacement in addition to the model.

You can read the blog post from OpenAI here.

Yuuma

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今回は感情分析(Sentiment Analysis)のプロンプトを紹介します。
AIは文章を解析して、その文章が、ポジティブ、ニュートラル、またはネガティブかの
感情分析の判断をおこなうことができます。

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以前に記事にしたAutoGPTを使用した感想です。

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Nowadays, Artificial Intelligence (AI) has witnessed remarkable advancements and one technology that has taken center stage is Large Language Models (LLMs). These models have revolutionized natural language processing and have far-reaching implications across various domains. Today, we will explore the capabilities and significance of LLMs in the AI field.

Large Language Models are sophisticated AI models trained on massive amounts of text data. They leverage deep learning techniques, particularly transformers, to process and understand natural language. These models possess an exceptional ability to generate human-like text, comprehend context and answer questions.

Training Process

Training LLMs involves exposing the model to vast quantities of text from various sources such as books, articles and websites. With the help of this extensive training data, the models learn grammar, semantics and contextual relationships, enabling them to generate coherent and contextually relevant responses.

Applications and Benefits

1. Natural Language Understanding: LLMs excel at understanding and interpreting natural language, enabling them to perform tasks like sentiment analysis, language translation, and text summarization. They can comprehend nuances, context and even generate human-like responses.

2. Chatbots and Virtual Assistants: LLMs play a vital role in the development of intelligent chatbots and virtual assistants. By leveraging their language comprehension and generation capabilities, these models enhance user interactions, providing personalized and context-aware responses.

3. Content Generation: LLMs can generate high-quality content, including articles, stories and poems. They assist content creators by providing suggestions, auto-completion, and ensuring grammatical correctness. These models save time and improve content quality.

4. Research and Knowledge Discovery: LLMs act as powerful research tools, capable of analyzing vast amounts of text and extracting insights. Researchers can utilize these models to explore scientific literature, identify patterns and generate hypotheses, which accelerate the pace of discovery.

5. Language Learning and Accessibility: Today, LLMs can learn language and additional data. They can provide interactive language tutoring, generate practice exercises, and offer real-time feedback.

Challenges and Ethical Considerations

While LLMs offer incredible abilities, there are challenges and ethical considerations to address. Some challenges include biases in training data, potential misinformation propagation, and the need for responsible AI development to ensure fair and ethical usage.

Conclusion

Large Language Models have emerged as a groundbreaking technology in the AI field, with applications spanning natural language understanding, content generation, research, and language learning. Their ability to process and generate human-like text has significantly impacted industries and transformed user experiences.

This is all for now. Hope you enjoy that.

By Asahi

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本記事は前回の「OpenAI GPT API(6) プロンプトデザイン」の続きです。
前回は以下のプロンプトで旅行の感想文の中から地名だけを抜き出す処理をおこないました。

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