What is GPT-4 ?
A generative pre-trained transformer (GPT) is a deep learning model trained on internet data for text generation.This tool is used for question and answer, text summarization, machine translation, classification, code generation, and conversation AI.By completing the Deep Learning in Python skill track, you will gain knowledge on building deep learning models.In this workshop, you will focus on the fundamentals of deep learning, gain an introduction to Tensorflow and Keras frameworks, and develop multiple input and output models using Keras.The possibilities for utilizing GPT models are endless, and you can adjust them to fit specific data for enhanced performance.By utilizing transformers, you will be saving on costs related to computing, time, and other resources.
During a question-and-answer session at the AC10 online meetup, Sam Altman, founder and CEO of OpenAI, confirmed rumors about the GPT-4 model being in development by OpenAI.By utilizing the information and drawing from current trends, we can predict the model size, optimal parameter, and compute, multimodality, sparsity, and performance in this section.
Based on Altman’s estimation, GPT-4 will likely have around 175B-280B parameters, similar to the language model Gopher developed by Deepmind.According to Altman, they are striving to achieve better performance with smaller-scale models.To create a large language model, a significant amount of data, a high-powered computer system, and a complicated software design were required.Despite being costly at times, using large models can be a waste of resources for some companies.
As of my training cutoff in September 2021, there is no official announcement or confirmation from OpenAI about the release of GPT-4. However, based on the improvements made in the previous versions of GPT, here are some potential features that GPT-4 might have:
- Increased model size: GPT-4 might have a larger number of parameters compared to GPT-3, which could lead to better performance on a wide range of natural language processing tasks.
- Better performance on complex tasks: GPT-4 might be able to perform well on more complex tasks such as machine translation, summarization, and question-answering.
- Multilingual support: GPT-4 could potentially support a wider range of languages compared to its predecessors.
Improved training techniques: GPT-4 might use new and improved training techniques such as unsupervised learning, which could lead to better accuracy and efficiency.
Domain-specific models: GPT-4 could potentially have domain-specific models, such as models trained specifically for medical or legal language.
Improved language understanding: GPT-4 could potentially have a deeper understanding of language and be able to generate more human-like responses.
Enhanced natural language generation: GPT-4 could potentially be able to generate text that is even more coherent and natural-sounding than its predecessors.
Better performance on complex tasks: GPT-4 could potentially perform well on complex natural language processing tasks such as machine translation, summarization, and question-answering.
Multilingual support: GPT-4 could potentially support a wider range of languages than its predecessors.
Increased efficiency: GPT-4 could potentially be more efficient in terms of training and inference time, allowing for faster development of natural language processing applications.
gpt-4 with an 8K context window (about 13 pages of text) will cost $0.03 per 1K prompt tokens, and $0.06 per 1K completion tokens.
gpt-4-32k with a 32K context window (about 52 pages of text) will cost $0.06 per 1K prompt tokens, and $0.12 per 1K completion tokens.
Please join us for a live demo of GPT-4 at 1pm PDT today, where Greg Brockman (co-founder & President of OpenAI) will showcase GPT-4’s capabilities and the future of building with the OpenAI API.