ChatGPT vs Google Bard: In recent years, artificial intelligence (AI) has made tremendous advances, giving rise to astounding language models capable of engaging in human-like conversations. Both Chat GPT and Google Bard, built by significant technological corporations, are important AI models in this arena. In this post, we will evaluate various models and investigate their strengths to determine which one reigns as the greatest AI model.
On one side, we have Chat GPT, which is based on OpenAI’s GPT-3.5 architecture and is a tremendously strong language model. With its massive knowledge base from training on a wide spectrum of internet literature, Chat GPT provides coherent and contextually relevant responses. Leveraging deep learning techniques, it mimics human speech patterns, resulting in genuine and lively conversations.
On the other hand, Google Bard, an AI model developed by Google, also utilizes cutting-edge approaches with a core architecture designed to understand and generate text. Trained on a large dataset, Google Bard excels at comprehending complex queries and delivering meaningful responses. Its notable strength lies in its ability to generate creative and poetic writing.
Overall, both Chat GPT and Google Bard showcase remarkable capabilities, each with its unique strengths in language understanding and generation.
ChatGPT vs Google Bard
ChatGPT and Google Bard are two of the most popular AI models on the market right now. Both models can generate text, translate languages, write various types of creative content, and provide helpful answers to your questions. There are, however, some significant changes between the two models. Let’s see what they are.
The data source is critical in training a GPT model because it determines the model’s quality and accuracy. A excellent data source is one that is large, diverse, and of high quality. It should also be relevant to the domain in which the model will be employed.
In contrast, the data sources used by ChatGPT and Google Bard are one of the main differences between the two. ChatGPT is taught using a corpus of text and code that will be updated through the year 2021. However, Google Bard is trained on an infinite and ever-changing data set (called an “infiniset“). Bard, being favored by Google and renowned for its extensive data reserves, has the advantage of accessing the latest information. In comparison, ChatGPT may be limited to older information.
Google Bard was taught on a massive dataset consisting of over 1.56 trillion words of text and code. This is equivalent to approximately 175 terabytes of text data. Books, journals, websites, GitHub repositories, and even some code are all included in this collection.
As of the 2021 data cutoff, ChatGPT (GPT-3.5) was trained on approximately 45 terabytes of data. The data is collected from a wide range of publicly available text sources on the internet. ChatGPT’s training dataset contain books, articles, webpages, forums, Wikipedia, and other publically available text. ChatGPT can also create code-related responses, therefore GitHub may be included in the dataset.
The parameter count is one of the factors that contribute to the model’s capacity and performance because it represents the number of learnable weights in the model. Having huge number of parameters allows it to capture complicated patterns and produce sophisticated responses.
ChatGPT (GPT-3.5) has around 175 billion parameters as of the September 2021 knowledge cutoff. The official parameter count for Bard, on the other hand, was not revealed. However, if the claims are genuine, Google Bard was fed around 195-210 billion parameters.
Model & Architecture
Bard is built on Google’s LaMDA (Language Model for Dialogue Applications) model, while ChatGPT uses the GPT-3.5 model.
GPT-3.5 is a big language model created by OpenAI. It is trained on a vast dataset of text and code and can generate text, translate languages, write various types of creative content, and answer your questions in an informed manner.
Google AI created LaMDA, a massive language model. It, too, is trained on a vast text and code dataset, but it is specifically developed for dialogue applications. This means it is more capable of comprehending and responding to natural language interactions. LaMDA aims to overcome the limitations of traditional language models by enabling more fluid and context-aware conversations.
The approach, techniques, and processes utilized to train a machine learning model are referred to as the training methodology. It includes the actions taken to optimize the parameters of the model and enable it to accomplish a certain task.
In Chat GPT, the training methodology involves a series of procedures such as Pre-training, Fine tuning, Evaluation and Iteration. Unsupervised learning is the foundation of Chat GPT. Unsupervised learning is the process of training a model using unlabeled data so that the model can learn to detect patterns, structures, and representations in the data without explicit supervision or labeled instances. In addition to that, Reinforcement learning was also used in Chat GPT.
On the other hand, Supervised learning trains Bard. The model is trained using labeled data in supervised learning. Input-output pairings make up the labeled data. The input is text, and the output is the required answer. The model learns to predict output by comparing input to labeled data. In addition to supervised learning, Bard is trained using a process known as self-supervised learning. Self-supervised learning is a sort of unsupervised learning that does not require labeled data. In self-supervised learning, the model is trained to anticipate a missing portion of a piece of text. For example, the model could be trained to anticipate the next word in a sentence or the meaning of a word.
Creative/Poetic vs Versatile & Diverse
Additionally, ChatGPT uses its extensive knowledge base to deliver accurate and insightful responses to a wide range of questions. It makes an effort to comprehend the context and intent of the discourse, resulting in fulfilling relationships.
On the other hand, Bard is more poetic than ChatGPT because it is trained on a dataset that includes a large amount of poetry. This means that Bard has a better understanding of the structure and rhythm of poetry, and it is able to generate more creative and original poems.
As a result, Chat GPT is a good solution if you’re searching for a dynamic language model capable of engaging in diverse conversations over a wide range of subjects. It excels at responding in an informed and contextually suitable manner. If, on the other hand, you prefer a more poetic and imaginative exchange, Google Bard’s emphasis in literature and arts might be a good fit. Its text generating capabilities allow it to weave beautiful storylines and elicit emotions.
ChatGPT vs Google Bard: Summary
Here is a table that summarizes the key differences between ChatGPT and Bard:
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