GPT-4 is a tremendously useful tool for businesses and software developers due to its ability to improve natural language processing and code generation.
We analyze its benefits, use cases, latest updates and give you advice on how to get the most out of your business.
What is GPT-4?
GPT-4 is the latest model in the GPT (Generative Pre-trained Transformer) family. And what is a GPT? It is an LLM (Large Language Model), that is, a type of artificial intelligence that uses Deep Learning to try to imitate human language. The goal is to be able to generate meaningful data. As? Predicting the next word that will follow the previous one in a text.
They are a new way to create a customized version of ChatGPT to make your users' lives easier. No coding required to easily create your own GPT. In fact, creating one is as easy as starting a conversation, giving it additional instructions and knowledge, and choosing what it can do.
It presents a greater learning capacity thanks to the inclusion of more volume of data for training and an architecture with a greater number of parameters. The model can compose songs, write scripts, develop software or learn the user's writing style with much more precision and quality than previous versions. Additionally, thanks to its multimodal nature, it also accepts images as input, which greatly expands its capabilities.
How does GPT-4 work?
GPT-4 is an LLM that processes images and texts as input and generates texts as output. It uses an architecture based on Transformer, a model that consists of stacked decoder blocks that use different neural networks and incorporate the attention mechanism.
The model training and alignment process consists of two steps:
The model is trained with a large amount of multimodal data including images and texts from different domains and sources. This data is obtained from various public repositories and the goal is to predict the next token in a document given a sequence of previous tokens and optional images.
After training, the model is aligned with a manually labeled data set containing verifiable facts and desired behaviors. This data is obtained from reliable sources, such as encyclopedias, textbooks, and professional guides. The goal of this alignment is to adjust the model parameters so that its outputs are more factual and adherent to the desired behaviors.
GPT-3 vs. GPT-4 | Differences
The main difference between one version and another is that GPT-4 is a model that processes images and texts as input, something that previous versions could only do with text.
Also, with the new version, 4,096 tokens have gone from being sent to the API to 32,000 tokens. This represents a great advance, as it facilitates the creation of increasingly complex and specialized texts and conversations.
GPT-4 has a larger training set volume than GPT-3, going from training with 17 GB of data to 45GB.
Additionally, problem-solving capabilities have been improved by offering greater responsiveness with solutions and text generation that mimics the style and tone of the context.
GPT-4 Updates
GPT-4V
It is an LVM (Large-scale Visual linguistic Model) that allows the user to upload an image as input and engage in a conversation with the model. Instructions or questions can be given to direct the model to perform tasks based on the information provided in the form of an image.
It builds on existing GPT-4 capabilities and offers visual analysis in addition to existing text interaction features.
Its main capabilities are:
Visual input: Accepts visual content such as photos, screenshots, and documents.
Object Detection and Analysis: Can identify and provide information about objects within images.
Data Analysis: Master the interpretation and analysis of data presented in visual formats such as graphs, tables, and other data visualizations.
Text Decryption: Able to read and interpret handwritten notes and text within images.
It is a model that can be applied to numerous use cases, such as in academic research in which historical manuscripts come into play, where a lot of time is required to decipher them by expert paleographers and historians.
It is also very useful when writing code for a website based on an image with the required design. It can even be applied to interpret data through graph images, from which you can extract the underlying data and provide key information.
GPT-4 Turbo
With GPT-4 Turbo, a further step is taken in generative AI for several reasons:
New limite of knowledge: the message that the information collected by ChatGPT has a deadline of September 2021 comes to an end. The new model includes information until April 2023, which represents a much more current context for consultations.
Longer Prompts: Long, detailed prompts will no longer be an issue as it now supports up to 128,000 context tokens. This would correspond to about 300 pages of a book, which opens the paradigm even further.
Better following instructions: This model works better than previous ones on tasks that require careful following of instructions, such as generating specific formats.
Multiple tools in one chat: The updated GPT-4 chatbot chooses the appropriate tools from the dropdown menu.
How to get the most out of GPT-4?
One of the main advantages of GPT-4 and ChatGPT is that the model is already trained, so it helps you search for information in business documents and systems in an agile and efficient way. This translates into cost reduction, less time spent searching for information from different documents or improved process efficiency and employee productivity.
However, there are certain more complex use cases that will require fine tuning of the model that involves training, where a specialized partner must come into action. At Plain Concepts we offer you a unique OpenAI adoption solution, where you will access a program that will help you incorporate and take advantage of the benefits of generative AI in your organization.
No comments:
Post a Comment