Google Colab is a free online Jupyter notebook environment that allows you to write and run Python code. It is a popular tool for machine learning and data science, and it is also used by students and researchers. Google has announced that it is adding new AI coding features to Colab. These features are powered by Google’s PaLM 2 language model, and they can help you to write code more quickly and easily.
Google has recently unveiled its latest large language model (LLM), PaLM 2, which boasts impressive capabilities in advanced reasoning, multilingual translation, and code generation. PaLM 2 is the successor of PaLM, which was already a state-of-the-art LLM that could perform a variety of natural language tasks. PaLM 2 improves on PaLM by using compute-optimal scaling, an improved dataset mixture, and an updated model architecture and objective.
PaLM 2 is not only a powerful research tool, but also a practical one. Google has made PaLM 2 accessible to developers and researchers through its Colab platform, which allows users to run code and experiments in the cloud using Google’s computing resources. Colab users can now access PaLM 2 through the PaLM API, which gives them the ability to use PaLM 2 for dialog-focused use cases, like chatbots.
One of the new features is called “Codey.” Codey is a family of code models that can generate code from natural language descriptions. For example, you can type “write a function that takes two numbers as input and returns their sum” and Codey will generate Python code. Another new feature is called “Code completion.” Code completion helps you to write code more quickly by suggesting possible completions for the code that you are typing. I asked Google Bard to show an example of how Code Completion works and this is what the company’s ChatGPT rival AI chatbot said:
PaLM 2 to be at the Center of Google’s AI Push
The PaLM 2-driven Codey is easy to use and requires only a few lines of code. Users can create a new conversation with PaLM 2 by calling the palm.chat function with a message as an argument. The function returns a response object that contains the model’s response in the last field.
One of the advantages of using PaLM 2 is that users can get alternate model responses by setting the candidate_count argument to a number greater than one.
This allows users to see different possible responses that PaLM 2 can generate for the same input. Users can then choose the best response for their use case or combine multiple responses to create a more diverse and engaging conversation.
PaLM 2 is not only good at generating natural language, but also code and math. PaLM 2 was pre-trained on a large quantity of webpage, source code and other datasets, which means that it can generate code in popular programming languages like Python and JavaScript, as well as specialized languages like Prolog, Fortran, and Verilog. PaLM 2 can also generate mathematical equations and solve complex problems. This makes PaLM 2 a valuable tool for developers and researchers who want to leverage LLMs for coding and math tasks.
Programming is Being Transformed be the Mainstreaming of AI
AI is transforming the software development industry. Tech giants like Google, Microsoft, and Amazon are competing to offer the best AI-powered tools for code generation and debugging. Here are some of the recent developments in this field:
- GitHub Copilot is a joint project between Microsoft and OpenAI. It uses GPT-4 to suggest code snippets based on users’ inputs. GitHub Copilot is powered by OpenAI Codex, a generative pretrained language model created by OpenAI. Earlier this year, GitHub showcased its future vision with GitHub Copilot X, which includes an integration with OpenAI’s GPT-4.
- Builder.ai is an AI software firm that received an equity investment from Microsoft. The deal will allow users to access Builder.ai’s Natasha AI product manager through Microsoft Teams. Builder.ai and GitHub Copilot are two different types of AI-powered tools for software development. Builder.ai is a no-code platform that enables users to create apps by choosing from various templates and features, without writing any code. The service is ideal for non-technical users who want to build simple or standard apps.
- Google teamed up with Replit to offer Ghostwriter, an AI tool that helps developers write code. The partnership also gives Replit developers access to Google Cloud and vice versa. Additionally, Google brought code generation and debugging to its Bard AI chatbot. Users can simply type their coding questions or requests in natural language, and Bard will generate multiple drafts of possible responses for them to choose from. Users can also ask follow-up questions or have Bard try again if they are not satisfied with the results.
- Amazon launched CodeWhisperer, a free AI programming tool. It works with Python, JavaScript, and Java languages and integrates with popular IDEs like PyCharm and Visual Studio Code. It helps users write code faster and easier. CodeWhisperer is integrated with AWS services and tools, such as Lambda, CloudFormation, and Amplify. This allows users to easily deploy and manage their code on the cloud with minimal configuration.
- Earlier this month, Google introduced Studio Bot for the Android Studio service, bringing AI programming tools to developers on its mobile platform. Studio Bot supports Kotlin, the main language used for Android development, while Java support is planned for a future release. Developers can ask Studio Bot questions about Android APIs, libraries, and best practices, and get quick answers or code examples.
