We Asked Google Bard How It was Different from ChatGPT

Google Bard and ChatGPT are two of the most popular AI chatbots on the market. Both chatbots are capable of generating human-like text in response to a wide range of prompts and questions. And with the recent beta launch of Google Bard, it’s a great time to examine the basic difference between the two chatbots.

Google Bard is a large language model (LLM) chatbot that is trained on a massive dataset of text and code – this includes Google Search data. This allows Bard to generate more comprehensive and informative responses than the current ChatGPT which is a natural language processing (NLP). However, ChatGPT appears to give more breakdown in its responses. This is not to say Bard isn’t as detailed, it appears to be a more refined chatbot, providing summarised information, that you can decide to expand just like you would with ChatGPT using the right prompt.

Bard is also able to access and process information from the internet in real time, which gives it a significant advantage over ChatGPT. However ChatGPT-4 can access the internet in certain cases.

ChatGPT is trained on a smaller data set when compared to Google Bard. This limits ChatGPT’s ability to generate comprehensive and informative responses. But with the team at OpenAI constantly training the AI this gap is could be bridged.

Here is a table that summarizes the key differences between Google Bard and ChatGPT, we have added more context to the original response of Google Bard:

Ultimately, the best AI chatbot for you will depend on your specific needs and requirements. If you are looking for a chatbot that can generate comprehensive and informative responses, then Google Bard is the better choice. If you are looking for a chatbot that is more affordable and can provide factual information as guided by prompts, then ChatGPT may be a better option.

Value of AI Models in the Workplace and Business

AI models can be valuable in the workplace in a number of ways. They can be used to:

Automate tasks

AI models can automate tasks that are currently performed by humans. This can free up employees to focus on more creative and strategic work. For example, AI models can be used to automate customer service tasks, such as answering questions and resolving issues. This can free up customer service representatives to focus on more complex tasks, such as providing advice and support.

Improve customer service

AI models can improve customer service by providing 24/7 support and answering questions in a timely and informative manner. For example, AI models can be used to create chatbots that can answer customer questions about products or services. This can help businesses to provide better customer service, even when their customer service representatives are not available.

Personalize the customer experience

AI models can personalize the customer experience by understanding each customer’s unique needs and preferences. For example, AI models can be used to recommend products or services to customers based on their past purchases or browsing history. This can help businesses to provide a more relevant and engaging customer experience.

Make better decisions

AI models can provide employees with access to real-time data and insights. This can help employees to make better decisions about a variety of issues, such as product development, marketing, and sales. For example, AI models can be used to analyze customer data to identify trends and patterns. This information can then be used to make better decisions about product development, marketing, and sales.

Identify and mitigate risks

AI models can monitor for potential problems and take corrective action before they occur. This can help businesses to identify and mitigate risks, such as fraud, cyberattacks, and product defects. For example, AI models can be used to monitor financial transactions for signs of fraud. This can help businesses to identify and prevent fraud before it causes financial losses.

Challenges of AI models in the Workplace

While AI models can be valuable, they also pose a number of challenges. These challenges include:

The potential for bias

AI models are trained on data, and if that data is biased, the model will be biased as well. This can lead to discrimination and other negative outcomes. For example, if an AI model is trained on a dataset of resumes that is biased towards men, the model may be more likely to recommend men for jobs. This can lead to discrimination against women in the workplace.

The potential for job displacement

AI models can automate tasks that are currently performed by humans. This could lead to job losses, especially in low-skilled and repetitive jobs. For example, AI models can be used to automate tasks such as data entry and customer service. This could lead to job losses in these industries.

The potential for security risks

AI models can be hacked or manipulated, which could lead to data breaches or other security problems. For example, if an AI model is used to store sensitive customer data, it could be hacked and the data could be stolen. This could lead to financial losses and damage to the company’s reputation.