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Discover how the generative network of Singularity, ChatGPT-4, with 100 trillion parameters, revolutionizes artificial intelligence.

ChatGPT-4: The Singularity’s Generative Network

ChatGPT-4: The Singularity's Generative Network | Arkkosoft
Discover how the generative network of Singularity, ChatGPT-4, with 100 trillion parameters, revolutionizes artificial intelligence.

The Singularity is a concept in artificial intelligence that refers to a point in the future at which artificial intelligence will surpass human intelligence. Some experts believe that the Singularity could be a positive event, as artificial intelligence could help us solve global problems like climate change and poverty. However, others fear that the Singularity could be dangerous if artificial intelligence becomes hostile or out of control, which is why many countries already have advanced legislation on this topic.

ChatGPT is a neural network trained with much of the information available on the internet that allows generating unique and instant answers to specific questions. Unlike a search engine, ChatGPT understands context and can remember previous conversations, making it ideal for creating personalized content in areas like education, medicine, art, and customer service.

Generative networks like ChatGPT have a wide variety of applications in different fields. For instance, in medicine, it can be used to generate medical reports and progress notes. In arts, it can be used to create music, poetry, and visual art. It can also be used in education to create personalized learning materials and in customer service to provide quick and accurate answers to customer questions. All this generation capacity saves a lot of resources in content creation.

Unlike traditional search engines such as Google, that presents us with a list of sites according to a popularity ranking, adverse generative neural networks such as GPT chat, analyze the context and search for the next best word and it is what is known as generation of pretrained content. This technology uses language models based on transformers architecture that have been previously trained on large amounts of text data. These models learn to generate high-quality content by understanding and predicting the structure and relationships within the language.

Transformers architecture was introduced in 2017 by Vaswani et al. in an article titled “Attention is All You Need.” Since then, it has been widely adopted in the field of natural language processing (NLP) and has given rise to a number of high-performance language models, such as GPT (Generative Pre-trained Transformer) developed by OpenAI.

These pretrained language models are trained in two main phases:

  1. a) Pre-training: where the model is trained on large amounts of unlabeled text data (e.g. web pages, articles, books, etc.)
  2. b) Fine tuning: where the pretrained model is fitted to a specific natural language processing task using “labeled” data. This allows the model to adapt to the particular needs of the task at hand, such as text classification, machine translation, summary generation, etc.

The current version of ChatGPT has 175 billion machine learning parameters, while version 4, which was released on 3-15-2023 has 100 billion parameters. GPT-4 is expected to be able to process multiple types of data, including video, images, sounds, and numbers, and can be used to write, for example, an entire book or entire movie scripts, create actors, produce the movie, and publish it, without the need to hire a single actor.

There are some who say that: AI can bring about a radical change as office automation in the 80s or the internet at the end of the 90s. AI with technologies like Chat GPT will become: An essential tool for all professionals and it will be immersed in all of our actions and our interactions as a society, which will undoubtedly bring enormous benefit, and where workers are ultimately expected to focus on tasks with greater added value”…. Unquestionably, this technology will put us as a society before a series of opportunities and challenges.

This article was written by Dr. Juan Barrios Arce who is a medical informatics specialist, Master in Big Data and Data Science and professor of biomedical engineering at the University of Barcelona.

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