Artificial intelligence • Google • Machine learning - CHATGPT VS BERT AI

In this modern era ,there is various modern tech and science is been developing day by day ,to make the humans works are done easily and automated by using the AI Artificial Intelligence. While the chat GBT became their success for their company, so the google wanted themselves a chat bot which complete with chat GBT, so in this story we gonna find it out what is it in.

Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are designed to think and act like humans. These machines are capable of performing tasks that would normally require human intelligence, such as recognizing speech, understanding natural language, making decisions, and solving problems

APPLICATION

AI has the potential to transform many industries and has already been adopted by companies in a variety of sectors, including manufacturing, retail, and transportation. In manufacturing, AI is being used to optimize production processes, improve quality control, and reduce waste

Chatbot

A chatbot is a computer program designed to simulate conversation with human users, especially over the Internet. Chatbots can be integrated into various platforms, such as websites, messaging apps, and virtual assistants, to provide users with quick and convenient access to information and support.

ChatGPT

ChatGPT is an AI-powered chatbot developed by OpenAI. It's a language model trained on a large corpus of text data and designed to generate human-like responses to text input. ChatGPT is based on the Transformer architecture, which is a deep learning approach that's well-suited for natural language processing tasks. The model has been trained to generate text in a variety of styles and on a range of topics, allowing it to engage in conversations and answer questions on a wide range of subjects.

What is BERT in AI?

BERT is a method of pre-training language representations. Pre-training refers to how BERT is first trained on a large source of text, such as Wikipedia. You can then apply the training results to other Natural Language Processing (NLP) tasks, such as question answering and sentiment analysis.

What is Google BERT used for?

In Google, BERT is used to understand the users' search intentions and the contents that are indexed by the search engine. Unlike RankBrain, it does not need to analyze past queries to understand what users mean. BERT understands words, phrases, and entire content just as we do

Google announced that they had started applying BERT models for English language search queries within the US. On December 9, 2019, it was reported that BERT had been adopted by Google Search for over 70 languages.

Is BERT a large language model?

BERT is an extremely powerful and high-performance large language model (LLM) that is pretrained from Google on a large corpus

How to use BERT for chatbot?

BERT uses bidirectional training i.e it reads the sentence from both directions to understand the context of the sentence. Note that BERT is just an encoder. It does not have a decoder. Parameter counts of several recently released pre-trained language models.

Difference between CHATGPT and BERT

Natural language processing (NLP) has come a long way over the past few years. With the development of powerful new models such as GPT-3 and BERT, it's now possible to create sophisticated applications that can understand and interact with human language.

GPT-3 (Generative Pre-trained Transformer 3) is an autoregressive language model developed by OpenAI. It was trained on a dataset of 45TB of text data from sources such as Wikipedia, books, and webpages. The model is capable of generating human-like text when given a prompt. It can also be used for tasks such as question answering, summarization, translation, and more.

What is BERT?

BERT (Bidirectional Encoder Representations from Transformers) is another popular language model developed by Google AI. Unlike GPT-3, BERT is a bidirectional transformer model, which considers both left and right context when making predictions. This makes it better suited for sentiment analysis or natural language understanding (NLU) tasks.

Similarities between GPT-3 and BERT

• Despite their differences in architecture and training datasets size, there are also some similarities between GPT-3 and BERT:
• They use the Transformer architecture to learn context from textual-based datasets using attention mechanisms.
• They are unsupervised learning models (they don’t require labeled data for training).
• They can perform various NLP tasks such as question answering, summarization, or translation with varying degrees of accuracy, depending on the task.