GPT-4 (Generative Pre-trained Transformer 4) is an advanced language model that has been gaining a lot of attention in the technology world.
Presented as more reliable and creative than GPT-3, it was released on March 14, 2023 and has the potential to revolutionize natural language processing (NLP) and other areas of artificial intelligence (AI). From processing more nuanced instructions to generating 25,000 words of complex text, GPT-4 has many exciting use cases.
In this blog post, we’ll analyze this new technology and the areas it can be useful in.
- Generative Pre-trained Transformer (GPT) is a type of artificial intelligence model that learns contextual relationships between words in a text and can generate new content with impressive results.
- GPT-4 is based on an advanced form of deep learning known as transformer networks and uses an unsupervised approach that doesn’t require labeled data.
- GPT-4 has a larger data set, a bigger model and is significantly faster than GPT-3.
- GPT-4 comes with a new pricing model in which the price of the prompt tokens is reduced.
- GPT-4 can be used for written content generation, answering FAQs on your website or social media channels, and efficiently translating content.
- GPT-4 can accept images as inputs and generate captions, classifications, and analyses.
Generative Pre-trained Transformer
Generative Pre-trained Transformer (GPT) is a type of artificial intelligence model that has revolutionized natural language processing (NLP).
What is it?
The GPT model was developed by OpenAI and released in 2018, and since then it has become increasingly popular.
GPT is based on the transformer architecture, which uses attention mechanisms to learn contextual relationships between words in a text. By using pre-training techniques on large amounts of publicly available text data, GPT can generate new content with impressive results.
How does it work?
At its core, GPT is a deep learning system that uses natural language processing (NLP) and machine learning (ML) to generate text from a given prompt.
The system works by taking the input prompt and using it to create an internal representation of the input context. It then uses this representation to create a response that follows the same context as the original input.
Where is it used?
The most impressive feature of the GPT technology is its ability to generate human-like writing without any prior training or understanding of language structure. This makes it possible for users to quickly produce high quality content tailored specifically for their needs without having to invest time in understanding complicated NLP models or manually coding them into their applications.
The technology is being used more frequently in various applications including automated customer service chatbots and summarization services.
Overview of GPT-4
Let’s take a closer look at GPT-4, the latest machine learning model developed by OpenAI.
What is it?
It has been described as a “natural language processing powerhouse” due to its ability to generate human-like text when given a prompt.
GPT-4 is based on an advanced form of deep learning known as transformer networks and uses an unsupervised approach that doesn’t require labeled data.
How does it compare to GPT-3?
First of all, GPT-4 comes with a larger data set, around 45 gigabytes, which means that it is able to generate more accurate results.
GPT-3 also has a smaller model compared to GPT-4: the former is a 175 billion parameter model while the latter is around 1.6 trillion parameters model. The bigger the model, the easier solving complex tasks gets. Other performance improvement is that GPT-4 is significantly faster than GPT-3, as it runs on more powerful GPUs and TPUs.
And finally, GPT-4 can accept images as inputs and generate captions, classifications, and analyses. That means this new model can comprehend what an image contains and base its answers on the details of the visual.
Specifically, the model generates text outputs given inputs consisting of arbitrarily interlaced text and images. Over a range of domains—including documents with text and photographs, diagrams, or screenshots—GPT-4 exhibits similar capabilities as it does on text-only inputs.
GPT-4 comes with a new pricing model in which the price of the prompt tokens is reduced.
For models with 8k context lengths, the price is:
- $0.03/1k prompt tokens
- $0.06/1k sampled tokens
For models with 32k context lengths, the price is:
- $0.06/1k prompt tokens
- $0.12/1k sampled tokens
Use Cases of GPT-4
GPT-4 is an incredibly powerful tool for creating content such as articles and stories without needing to write any code.
GPT-4 has many applications including writing emails, generating summaries from documents, answering questions and creating conversational agents that can interact with users in natural language.
It can also be used for automated customer service bots, helping to reduce the need for manual customer support staff.
Written content generation
GPT4 leverages machine learning and artificial intelligence to generate text based on given prompts or topics, which means you can quickly create high-quality written content without having to manually write each sentence or piece of information.
If you’d like to try out an AI tool that doesn’t rely on the GPT-3 or GPT-4 technology, you can always give TextCortex AI a go to automate 70% of your daily work and join thousands of creators who already use our browser extension to streamline their workflow on over 1000 platforms.
One of the biggest advantages of using GPT-4 for answering FAQs and other customer inquiries is its ability to generate accurate, personalized responses quickly and efficiently.
This means that instead of having to manually answer each inquiry from scratch, businesses can now use GPT-4 to provide detailed and accurate answers in a fraction of the time it would normally take.
With the rise of global communication, it is becoming increasingly important to be able to translate content quickly and accurately.
One of the key advantages of using GPT-4 for translation compared to traditional machine translators is its ability to retain nuances within different languages that would otherwise be lost with traditional methods such as word replacement or dictionary lookups alone.
For example, when translating between English and Spanish where there are multiple meanings for certain words depending on context, GPT-4 can use contextual clues such as verb conjugations or sentence structure in order to determine which meaning best fits the context before generating an accurate result.