Google’s AI tool, Bard, has gained the ability to program. Bard is now equipped not only to create, debug and explain code but also to improve existing code and make it faster. Bard is currently only available via a waiting list open to people from the USA and Great Britain, but it is capable of using more than 20 programming languages, including Go, C++, JavaScript, Java, Python, and Typescript, according to Google’s blog post. With Bard, Python code can be exported to Google Colab, a platform used for writing and executing code.
Furthermore, Bard also assists in working with Google Sheets. Individual sections of code can be explained in natural language by Bard, making it a helpful tool for beginners. The AI tool can explain both connections and individual terms in code. Additionally, debugging is made more manageable with Bard. Users can inform the tool to identify and fix bugs.
However, it’s important to handle Bard’s output with caution. In an experimental stage, the AI tool may not provide the desired result, leading to mistakes. In the blog post, Google emphasizes the importance of double-checking Bard’s output and reviewing the code for errors, bugs, and vulnerabilities. Google assures that Bard will only take code from open-source projects, and sources will be given when more extended portions of the code are used.
Google’s Bard has already become quite popular for its programming assistance. Other similar services include Github’s copilot, which has been serving this purpose for years. Copilot X has been announced recently, which utilises the OpenAI GPT-4 large language model’s extended multimodal abilities to become a universal development assistant for programmers. Generative AI has also made its move into Google Workspace, where it helps in writing emails. Microsoft’s assistant, Copilot, is integrated into its Office products.
In conclusion, Bard is a promising AI tool that could be useful for programmers working in different languages. As Bard is still experimental, caution is advised when using its output. Nonetheless, its potential to improve existing code, help with programming, and explain code sections using natural language makes it a tool worth exploring.
