Image by Author
The Pathways Language Model (PaLM) has been updated with improved multilingual, reasoning, and coding capabilities. This new model is more capable of understanding and generating text in multiple languages, as well as reasoning and coding.
PaLM 2 was trained on a massive dataset of text and code in over 100 languages. To improve its reasoning capabilities, the developers included scientific papers and web pages with mathematical expressions. PaLM 2 was also pre-trained on publicly available source code in various programming languages. As a result, it is a top-of-the-line, next-generation language model that is powering various Google services.
According to Google Keynote (Google I/O ‘23), Bard is now running on the PaLM 2 model. It is far better at coding, reasoning, and creative writing problems than LaMDA.
Image from Google Keynote (Google I/O ‘23)
I have been using the old Bard (LaMDA) for 30 days and the new Bard (PaLM 2) for 7 days. I have seen drastic changes in the way Bard handles coding problems. Bard is not perfect, but I think Google is on the right track.
For example, when I asked Bard to create a snake game using Pygame, the old Bard was able to create the game, but it had several bugs and reduced functionality. The new Bard was able to create a working snake game with all of the expected features.
I am still seeing some bugs with the new Bard, but overall I am impressed with the progress that Google has made.
Image from Bard
I asked both ChatGPT and HuggingChat to generate code to solve a similar problem. ChatGPT generated bug-free code with additional functionality, while HuggingChat generated code with several errors, missing libraries, and security vulnerabilities.
Image by Author | Using ChatGPT
How Bard is different from ChatGPT?
Whenever you write a prompt, it will provide you three drafts to choses from. It is fast in producing the results, and comes with Google services integrations.
To access the drafts, you need to click on “view other drafts”.
Image from Bard
To access Google integrations, click on the up arrow at the bottom left. It is a code response. You will get the option to run your code on Google Colab.
Image from Bard
I have been using Bard for all kinds of data science taks, from understanding the project to producing high quality data reports. I believe that Bard is the best large language model available for the following reasons:
- Grammar and Writing: Bard is good at improving grammar and coming up with realistic text that can be used to improve your writing overall. It is better at this than ChatGPT, which can be overly dramatic.
- Machine Learning Research: Bard is good at researching machine learning topics. It can provide you with accurate information on a wide range of topics, even the latest research.
- Translation: Bard is good at translation. It can translate between many different languages, including Python code to JavaScript or English to Japanese.
- Brainstorming, Project Planning, and Understanding Context: Bard is good at brainstorming, project planning, and understanding context. It will evaluate chat history to provide appropriate answers, instead of giving random responses.
- Generating DALL-E 2, Midjourney, and Stable Diffusion Prompts: Bard is good at generating DALL-E 2, Midjourney, and Stable Diffusion prompts. It can help you create realistic images and art from text descriptions.
- Providing Links to External Sources: Bard is good at providing links to external sources. This can be helpful if you want to learn more about a topic or see an example of something that Bard has generated.
Image from Bard
“Apart from code generation, I am using Bard for everything.”
Now, let’s talk about the Super Bard that can do all. In the upcoming month, Google has announced Google service and third-party integrations. It means you can prompt in Bard and move the final response to Google Docs, Colab, Email, or any third-party software that you use for work.
Till now, we know you can use Bard to perform research, convert it into a table, modify the table, and export the response to Google Sheets. Moreover, you can use Google Lens service to interact with the image. For example, “Can you describe the image in detail?” Similar to GPT-4.
But it is better than GPT-4.
In the future, you will be able to use Adobe Firefly to generate Images directly from the Bard. You will be able to automate most of your tasks just by typing prompts.
Image by Author from Google I/O ‘23
In conclusion, I believe that Bard has the potential to be a one-stop solution for all of your work-related tasks. The team is constantly working to improve the model and add new features, and they are on the right track to overtake GPT-4. However, there are still a few areas where Bard could improve, such as its ability to handle code-related problems and its integration with Google Search. If Bard can address these issues, I believe it will be a truly revolutionary tool that can change the way we work.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
