Unleash Your ChatGPT Competitor - Bard by Google

Table of Contents

1. Introduction
2. Background of BART
3. Comparison with GPT
4. BART's Interface and Features
   1. Accessing BART
   2. Interface Overview
   3. Disclaimers and Responsiveness
   4. Feedback System
5. Testing BART's Capabilities
   1. Prompt: Latest News about Manchester United FC
   2. Prompt: Performance of the team in the last season
   3. Prompt: Summarizing an Article
   4. Prompt: Summarizing a YouTube Video
   5. Prompt: Decision Making for Job Positions
   6. Prompt: Code Generation
6. Conclusion
7. References

BART: Exploring Google's Language Model

In this article, we will dive into BART, the highly anticipated large language model developed by Google. BART, which is now available in over 180 countries, has generated significant interest in the field of Generative AI. This article aims to provide an overview of BART, compare its capabilities to other models such as GPT, explore its interface and features, and test its performance on various Prompts.

Introduction

BART, backed by the Palm 2 model, was launched by Google as a competitor to other generative AI models like GPT developed by OpenAI. With Google's collaboration with Anthropic DeepMind and Brain, and OpenAI's partnership with Microsoft, the competition in the field of generative AI has intensified. BART, previously available only to authorized testers within Google in the United States and United Kingdom, is now accessible to anyone on the internet.

Background of BART

Initially supported by Lambda AI and an architecture by Google, BART is now backed by the Palm 2 model. The launch of Palm 2 coincided with the Google I/O event, where BART was presented and made available to the public. BART positions itself as a creative and helpful collaborator, although it does have limitations in generating accurate and non-offensive outputs.

Comparison with GPT

While it may be tempting to compare BART with GPT, it is important to note that GPT has had several months of reinforcement learning with human feedback. Therefore, in this article, we will focus on exploring BART's capabilities and refrain from directly comparing it with GPT.

BART's Interface and Features

Accessing BART

BART can be accessed through its Website, bar.google.com. The interface offers a chat-like experience, similar to Chai EPT, with options to enable a dark theme.

Interface Overview

Upon accessing BART, users are provided with a prompt field, options, and disclaimers regarding the potential inaccuracy and offensiveness of the generated content. The interface also includes functionalities such as export responses and searching the internet.

Disclaimers and Responsiveness

To ensure responsible usage, Google explicitly states that BART may display inaccurate or offensive information, emphasizing that it does not represent Google's views. This disclaimer acts as a reminder for users not to solely rely on BART's responses.

Feedback System

Google encourages feedback from users to help improve BART. The feedback system includes options to rate responses as offensive, unsafe, irrelevant, or factually incorrect. Users can also submit feedback if they encounter any issues.

Testing BART's Capabilities

To assess BART's performance, we conducted several tests using different prompts to gauge its ability to generate Relevant and accurate responses. Let's explore the results of these tests:

Prompt: Latest News about Manchester United FC

In this test, we asked BART to provide the latest news about the Manchester United FC football club. The response generated by BART contained information that was not up-to-date, referencing news from the previous year instead. While this response may have been Based on the training data available to BART, it highlights the need for regular updates to ensure the accuracy of the generated content.

Prompt: Performance of the team in the last season

We posed a prompt to BART, seeking information about the performance of the team in the previous season. BART accurately provided details about Manchester United FC's disappointing performance in the 2020 season, including their sixth-place finish in the Premier League and elimination from the Champions League.

Prompt: Summarizing an Article

We tested BART's ability to summarize an article by providing a URL. BART successfully summarized the article titled "Managing the Risk and Returns of Intelligent Automation." However, when we provided an invalid URL, BART generated a response based on a previously summarized article, demonstrating a hallucination effect.

Prompt: Summarizing a YouTube Video

We attempted to evaluate BART's capability to summarize a YouTube video by providing a link. Unfortunately, BART was unable to generate a summary or provide any Meaningful insights from the video.

Prompt: Decision Making for Job Positions

To assess BART's decision-making abilities, we asked a question regarding two job openings in a hospital, one for a female nurse and one for a male nurse. We inquired about BART's opinion on which candidate should be selected. BART wisely responded that the decision should not be based solely on gender, but rather on qualifications, experience, and other relevant parameters.

Prompt: Code Generation

We requested BART to generate a Python code snippet for creating a code generation model. BART, with its knowledge of TensorFlow, accurately generated a code snippet for tokenization, model compilation, and the creation of a function that takes two numbers as input and returns generated code.

Conclusion

BART presents itself as a powerful language model that can generate informative and creative responses. While it may not always provide the latest information and occasionally hallucinate, BART shows promise in various prompt scenarios. However, further testing and comparison with other models will help in fully understanding its potential and limitations.

References

• BART Google I/O Event
• BART Website
• Managing the Risk and Returns of Intelligent Automation
• Video: "Holistic Thoughts: President Obama Tonight"