Recently, a popular work principle has emerged: when in doubt, consult ChatGPT.
Thus, the editor, who was struggling with topic selection, decided to chat with ChatGPT about what an AI IDE (Artificial Intelligence Integrated Development Environment) is.
From the basic concept of AI IDE to future development trends, let’s explore the collision of perspectives between ChatGPT and the editor!
ChatGPT’s response essentially summarizes the core functions that an AI IDE should possess.
The editor and the team believe that AI IDE is an integrated development environment specifically designed for AI and data science developers (such as data scientists and algorithm engineers), serving the entire AI development process and helping them improve efficiency.
Firstly, in terms of functionality completeness, we believe AI IDE needs tohave built-in plugins required for the entire AI development process, primarily including:
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Data preparation: convenient data access, data exploration, data annotation
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Coding: providing both interactive programming and scripted programming environments
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Model training: providing tools and plugins for training visualization, hyperparameter optimization, etc., to efficiently optimize models
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Model management: offering a one-stop model management service, including model version management, model testing, model evaluation, and model deployment.
IDE inherently has integrated characteristics, and AI IDE is no exception; it needs to have an open ecosystem that conveniently integrates various plugins required for AI development, and provides good compatibility.
At the same time, in terms of functionality usage, these plugins should be organized in an orderly manner according to the AI development process, rather than being a collection of discrete plugins. Therefore, workflow construction is an essential feature of AI IDE.
Additionally, considering the usage habits of data scientists and algorithm engineers, as well as the characteristics of AI development, it should also have “efficiency plugins”, such as version management, environment management and cloning, variable management, preset code snippets, and intelligent code assistance, to reduce the time data scientists and algorithm engineers spend on development preparation and auxiliary work.
In summary, the core features that an AI IDE should possess include: built-in plugins for the entire AI development process, orderly organization of workflows, good compatibility of plugins, and alignment with the usage habits of AI developers.
ChatGPT says:
ChatGPT’s response lists the main development tools used by AI and data science personnel. However, strictly speaking, these cannot be defined as AI IDEs.
The first two are interactive programming environments, mainly supporting model building and data exploration. The latter two are more used in engineering fields.
Since 2021, several development tools focusing on serving the data science field have emerged, such as the collaborative interactive programming tool Hex (https://hex.tech/) and Deepnote (https://deepnote.com/), as well as IDP that serves the entire AI development process. The standalone version of IDP has also been open-sourced, and interested friends are welcome to follow https://github.com/BaihaiAI/IDP.
With the development of AI applications, AI development tools are continuously iterating and expanding their functionalities. However, there is still no unified standard for AI IDEs or a strictly mature AI IDE tool.
Like ChatGPT, the editor also looks forward to a more mature and prosperous AI IDE!
ChatGPT believes that integration, intelligence, data visualization, and cloud computing are the future development trends.
At this point, the editor has fully grasped ChatGPT’s response style, which is reminiscent of the “Pyramid Principle”: a divide-and-conquer structure with a summary leading each point.
Regarding its predictions for trends, the four major points have certain reference and enlightening significance, such as cloud computing and data visualization. Among them, the relationship between data visualization BI and AI has been discussed in a previous article, where we believe that these two complement each other and may merge and develop collaboratively in the future.
Diving into the details, there are also some ambiguous points. For example, in the integration aspect, “not only supporting artificial intelligence but also data science, machine learning, etc.” The relationship between artificial intelligence, data science, and machine learning is something that ChatGPT should ponder over.
Aside from the aforementioned conceptual definition issues, ChatGPT here vaguely proposes a “general” concept of AI, which aligns with our definition of “AI” in AI IDE. When we built IDP, we considered the “AI” in AI IDE as a general AI concept, encompassing not only deep learning but also machine learning and traditional data analysis.
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