Google's Bard vs ChatGPT: A Battle of AI Poets

Table of Contents

1. Introduction
2. Initial Impressions of Google's Bard
3. Testing Bard's Writing Abilities
   • 3.1. Writing a Simple Paragraph
   • 3.2. Coaxing Bard to Generate Content
   • 3.3. Hit or Miss Results
4. Discoveries with Bard
   • 4.1. Bard's Content Detected as Fake
   • 4.2. Challenges with Sourcing Information
   • 4.3. Inconsistent Responses and Apologies
5. Bard's Comparison with GPT Models
   • 5.1. Bard's Inability to Assist with Requests
   • 5.2. GPT Models' Superiority in Writing
6. Single Shot Examples and Bard's Performance
   • 6.1. Bard's Inability to Follow Patterns
7. Testing Bard's Creativity and Knowledge
   • 7.1. Creating a Song with Bard
   • 7.2. Bard's Average Performance in Creativity
8. Bard's Schedule Creation and Productivity
   • 8.1. Bard's Generic Sample Schedule
   • 8.2. Impressive Schedule Creation by GPT-3
9. Bard's Translation and Google Services Integration
   • 9.1. Bard's Inability to Translate Text
   • 9.2. Discovering a Workaround with Google Translate
10. Bard's Performance in Different Tasks
    • 10.1. Bard's Summarization Abilities
    • 10.2. Bard's Limited Capacity in Answering Medical Queries
    • 10.3. Google Maps Integration and Distance Estimation
11. Conclusion
12. Final Thoughts and Future Expectations

Introduction

Google's Bard, an Advanced AI, has been a subject of speculation, hype, and rumors for quite some time. Finally, the opportunity to test and compare Bard with other AI models has arrived. In this article, we will Delve into our initial impressions of Bard and conduct several tests to gauge its writing abilities and performance. Our hope is to provide an unbiased and comprehensive analysis, highlighting both the strengths and weaknesses of this AI model.

Initial Impressions of Google's Bard

Upon our first experience with Bard, we were disappointed. Requesting it to write a simple paragraph on a generic topic, such as the benefits of vitamin C for men, resulted in Bard stating that it was unable to do so. The irony was not lost on us, as Bard proclaimed to be designed solely for processing and generating text. This lackluster first impression set the tone for further exploration and testing of Bard's capabilities.

Testing Bard's Writing Abilities

3.1 Writing a Simple Paragraph

Attempting to Elicit a response from Bard by slightly changing the phrasing, such as asking it to help understand the benefits of vitamin C for men instead, proved to be more fruitful. By slightly coaxing and massaging the AI, we were able to get it to generate text. However, the resulting content was often unimpressive, lacking in relevance or accuracy.

3.2 Coaxing Bard to Generate Content

To better understand Bard's performance, we conducted multiple tests in different scenarios. Despite varied attempts, Bard's responses remained hit or miss. It often declared its inability to fulfill requests or apologize for its shortcomings. This lack of consistency raised concerns about Bard's capabilities compared to other AI models currently available.

3.3 Hit or Miss Results

Throughout our testing, Bard's output was often detected as fake by a content detector Based on the Roberta model. Surprisingly, even though Bard was expected to use a new model to avoid detection, the content produced shared patterns with previous GPT models. Bard's sourcing of information was also questionable, with inconsistencies in citing sources and irrelevant or incorrect attributions. Apologies from Bard for its shortcomings were frequent and reinforced its underdeveloped state.

Discoveries with Bard

4.1 Bard's Content Detected as Fake

One notable discovery during our tests was that Bard's generated content was frequently flagged as 99% fake by the content detector. This raised concerns about the AI's ability to produce reliable and trustworthy information. It was puzzling to encounter such detection, considering Bard's supposed advancements over previous AI models.

4.2 Challenges with Sourcing Information

Sourcing information proved to be another stumbling block for Bard. When prompted for the source of information, Bard often provided unrelated or incorrect attributions. It struggled to understand the Context of the source and frequently apologized for its confusion. Bard's limited comprehension of sourcing and its reliance on predetermined patterns added to its overall shortcomings.

4.3 Inconsistent Responses and Apologies

Throughout our interactions with Bard, we noticed a common theme: frequent apologies for its lack of knowledge or inability to fulfill requests. Bard often apologized for not being able to assist or for not understanding instructions. While these apologies may indicate a Sense of accountability and self-awareness, they only served to highlight Bard's limitations and inability to provide accurate and comprehensive information.

Bard's Comparison with GPT Models

5.1 Bard's Inability to Assist with Requests

When comparing Bard with GPT models, it became apparent that Bard's ability to assist with various requests was severely lacking. Unlike earlier GPT models, Bard struggled to provide coherent and Relevant responses. Its shortcomings were particularly evident in generating content, where previous AI models excelled and outperformed Bard in terms of accuracy and relevance.

5.2 GPT Models' Superiority in Writing

In contrast to Bard's limitations, GPT models, such as GPT-3 and GPT-4, exhibited superior writing capabilities. GPT models consistently produced high-quality and relevant content, showcasing their advanced natural language processing abilities. This comparison further underscored Bard's shortcomings in the realm of AI-generated writing.

Single Shot Examples and Bard's Performance

6.1 Bard's Inability to Follow Patterns

One area where Bard consistently fell short was in following patterns or instructions for generating content. Even with repeated examples and clear instructions, Bard struggled to replicate the desired output. This limitation demonstrated the disparity between Bard and more proficient AI models, which could easily comprehend and follow instructions.

Testing Bard's Creativity and Knowledge

7.1 Creating a Song with Bard

Bard's creative abilities were put to the test by requesting the generation of a song. While Bard was able to produce a song, the quality and relevance of the lyrics were below average. Furthermore, Bard had difficulty incorporating all the given words into the song, highlighting its limitations in handling creative tasks.

7.2 Bard's Average Performance in Creativity

Throughout our exploration of Bard's creative abilities, it became evident that Bard's performance was average at best. It struggled to grasp the intricacies of creative tasks, often providing generic or nonsensical output. In comparison to other AI models, Bard's creative capabilities were lacking and left much to be desired.

Bard's Schedule Creation and Productivity

8.1 Bard's Generic Sample Schedule

One aspect where Bard was expected to excel was schedule creation and productivity assistance. However, when asked to Create a schedule for optimal productivity, Bard produced a generic sample schedule that failed to incorporate specific requirements and preferences. It missed the mark in understanding the nuances of the given tasks and goals, leaving much to be desired in terms of tailored and personalized scheduling.

8.2 Impressive Schedule Creation by GPT-3

In contrast, GPT-3 showcased its superior understanding of productivity scheduling. When presented with the same request, GPT-3 generated a detailed and comprehensive schedule that aligned with the specified goals and preferences. GPT-3's ability to provide timestamps and structure the day effectively surpassed Bard's generic and less insightful schedule.

Bard's Translation and Google Services Integration

9.1 Bard's Inability to Translate Text

Despite Bard's claims of being capable of translating text, our tests revealed a significant shortcoming in this aspect. Bard consistently failed to translate text accurately or provide reliable translations. The inability to fulfill translation requests left much to be desired and raised concerns about the overall reliability and usefulness of Bard in language-related tasks.

9.2 Discovering a Workaround with Google Translate

Upon further investigation and experimentation, we discovered a workaround to Bard's translation limitations. By asking Bard to use Google Translate for translation purposes, we were able to obtain more accurate and reliable translations. This reliance on external services showcased Bard's dependence on Google services to compensate for its deficiencies.

Bard's Performance in Different Tasks

10.1 Bard's Summarization Abilities

Bard demonstrated reasonable skills in summarizing content. When provided with a lengthy medical article, it successfully generated concise summaries that captured the essential information. While Bard's summarization abilities were commendable, they fell short compared to its GPT counterparts, often resulting in less coherent and comprehensive summaries.

10.2 Bard's Limited Capacity in Answering Medical Queries

When it came to answering medical queries, Bard's responses were limited in scope and often lacked detailed information. Its default response typically advised consulting with a doctor, similar to GPT models. However, Bard's performance in the medical domain was subpar compared to other AI models, failing to provide in-depth or accurate medical insights.

10.3 Google Maps Integration and Distance Estimation

One area where Bard showcased potential was in its integration with Google Maps. It could estimate distances and travel times accurately when asked about specific routes. However, its performance in this regard still lagged behind GPT-4, as GPT-4 provided more precise and consistent estimations without relying on Google Maps' integration, showcasing the widening gap between Bard and more advanced AI models.

Conclusion

After extensive testing and exploration, it is clear that Bard is not up to par with its AI counterparts, most notably GPT-3 and GPT-4. Bard's performance in generating content, following patterns, and understanding complex instructions is lacking compared to more advanced AI models. Its inconsistencies, struggle with accurate sourcing and citations, and frequent apologies for its limitations further highlight the gap between Bard and its competitors.

Final Thoughts and Future Expectations

While Bard's performance offers glimpses of potential, it falls short of expectations. As AI continues to evolve, we hope that Bard will undergo significant improvements to bridge the gap and offer a more competitive and reliable AI model. The need for more advanced and efficient AI writing solutions remains, and we eagerly await the next iteration of Bard or future AI models to meet these expectations.

FAQ

Q: Is Bard capable of generating accurate and reliable content?\ A: Bard's ability to generate accurate and reliable content is inconsistent. While it can sometimes produce satisfactory results, it often falls short, providing irrelevant or incorrect information.

Q: How does Bard differ from GPT models?\ A: Bard trails behind GPT models in terms of writing abilities and performance. GPT models demonstrate superior comprehension, coherency, and relevance in generating content compared to Bard.

Q: Can Bard effectively assist with productivity scheduling?\ A: Bard's performance in creating personalized and effective productivity schedules is lacking. It tends to provide generic sample schedules that fail to consider specific requirements and preferences.

Q: Is Bard proficient in translating text?\ A: Bard's proficiency in translating text is limited. It frequently fails to provide accurate translations, often necessitating the use of external services such as Google Translate.

Q: What are Bard's strengths and weaknesses compared to GPT models?\ A: Bard's strengths lie in its summarization abilities and integration with Google Maps for distance estimation. However, its weaknesses include inconsistent content generation, struggles in following patterns, and limited knowledge in certain domains.

Q: Can Bard provide accurate and detailed medical information?\ A: Bard's performance in providing medical information is subpar. It typically recommends consulting with a doctor and lacks the depth and accuracy exhibited by more advanced AI models.

Q: What can be expected from Bard in the future?\ A: There is hope that Bard will undergo significant improvements in the future to bridge the gap with more advanced AI models. However, the Current performance necessitates further development to provide more reliable and comprehensive AI-generated content.