Google recently launched a new AI language model, Bard, to compete with OpenAI's latest language model, Chat GPT. As someone who has extensively used Chat GPT for coding and other tasks, I was excited to try out Google's new offering and see how it stacks up. In this post, I'll share my experience with using Bard, comparing it to my previous experiences with Chat GPT-4 and exploring what it has to offer. While I was hoping for a smooth and seamless experience, my results with Bard were not quite what I expected from a company as experienced and reputable as Google.
My first test question was to write code to create a minimal website for a bike rental business. Within a few minutes, Chat GPT 4 actually generated HTML and CSS code for a bare minimum website! I was blown away by how cool and helpful this was.
The generated code included a header with a navigation bar, a main section with a welcome message and a call-to-action button, and a footer with a copyright notice. The styling was simple and clean, but professional-looking. Of course, I had to tweak some of the code to fit my specific needs, but overall it was a great starting point.
The generated code included a header with a navigation bar, a main section with a welcome message and a call-to-action button, and a footer with a copyright notice. The styling was simple and clean, but professional-looking. Of course, I had to tweak some of the code to fit my specific needs, but overall it was a great starting point.
Now Bard was to generate code for a minimal website for a bike rental business, just like I did with Chat GPT 4. Unfortunately, the results I got from Bard were a bit disappointing. While the generated HTML code included a basic structure with a heading and forms. Styling was plain and lacked any color or visual interest.
The generated CSS code also included some basic rules for the layout and form elements, but there was no custom design or attention to detail. Overall, the website looked very generic and lacked any personality or brand identity.
The generated CSS code also included some basic rules for the layout and form elements, but there was no custom design or attention to detail. Overall, the website looked very generic and lacked any personality or brand identity.
To further test the two AI language models, I decided to use a simple coding problem from LeetCode that required a dynamic programming approach. The problem was called "Climbing Stairs", and the task was to determine the number of distinct ways to climb to the top of a staircase with n steps, if you can only climb one or two steps at a time.
I first tested Chat GPT 4 by asking it to write the code for the problem. To my surprise, the model generated correct and clean code in just a few minutes. It was quite impressive to see the model automatically write code for such a problem.
Next, I tested Google Bard with the same question, and I got the same result. However, when I tried to directly copy-paste the code generated by Bard, it did not work because It had initialized a variable incorrectly. After fixing the error, the code worked as expected.
I was impressed with both models' performance, but I wanted to understand the code better. So, I asked both models to explain their code, and to my surprise, both were able to explain the code quite well.
I first tested Chat GPT 4 by asking it to write the code for the problem. To my surprise, the model generated correct and clean code in just a few minutes. It was quite impressive to see the model automatically write code for such a problem.
Next, I tested Google Bard with the same question, and I got the same result. However, when I tried to directly copy-paste the code generated by Bard, it did not work because It had initialized a variable incorrectly. After fixing the error, the code worked as expected.
I was impressed with both models' performance, but I wanted to understand the code better. So, I asked both models to explain their code, and to my surprise, both were able to explain the code quite well.
Source:Twitter
I also tested Bing's AI with same question and results were quite similar to Chat GPT's as it is based on Chat GPT -4. In conclusion, testing the three AI language models, Chat GPT 4, Google Bard, and Bing AI, was a fascinating experience. Chat GPT 4 and Google Bard were quite impressive in their ability to generate code and explain it. I was able to generate clean and efficient code for the given tasks in just a few minutes, which would have taken me hours to code manually.
Bing AI, on the other hand, went beyond just generating code. It was able to generate images and fetch data from the internet. It was able to answer my questions more efficiently than the other models.
On the other hand, Google Bard AI appeared to be unremarkable and uninteresting in comparison.
However, it's worth noting that Google Bard is a relatively new AI language model, and we cannot judge it by its early appearance. As the model continues to learn and improve, it's possible that it could surpass Chat GPT 4 and even Bing AI in the future.
Overall, it was interesting to see how far AI language models have come and how they can be useful in various fields. I look forward to seeing what the future holds for AI language models and how they will continue to improve our lives.
Bing AI, on the other hand, went beyond just generating code. It was able to generate images and fetch data from the internet. It was able to answer my questions more efficiently than the other models.
On the other hand, Google Bard AI appeared to be unremarkable and uninteresting in comparison.
However, it's worth noting that Google Bard is a relatively new AI language model, and we cannot judge it by its early appearance. As the model continues to learn and improve, it's possible that it could surpass Chat GPT 4 and even Bing AI in the future.
Overall, it was interesting to see how far AI language models have come and how they can be useful in various fields. I look forward to seeing what the future holds for AI language models and how they will continue to improve our lives.
Author
-Anurag
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