In the last couple of years, low-code solutions have turned out to be of integral help to organizations who wish to widen their programming accessibility to a larger base of users along with helping to fill development gaps.
Nevertheless, AI-based code generation might level up the war field with advanced auto-completion and AI coding assistants. For example, GitHub’s Copilot is a helpful AI pair programmer or OpenAI’s ChatGPT can generate complex functions when given normal language prompts. Other AI tools that are trained on UIs, such as Galileo, can procure frontends for any form of project within seconds.
So, the question is; are low-code development platforms still feasible in this paradigm of AI-driven software development?
If so, then how will low-code solutions and AI coexist altogether?
Before we dive into these questions, let’s understand what low-code development is.
A Low-code software development approach is when developers develop applications in a form of graphical interface.
Instead of writing numerous lines of complex code and syntax; low-code developers tend to drag and drop visual models to generate complete applications with modern user interfaces, data, integrations, and logic.
What is a Low-Code Platform like?
A low-code platform tends to remove repetitive and tedious tasks like code validation, dependency management, and complicated builds by automating everything.
With the rise in low-code development, developers can now focus on the creative aspects that make a true difference in their applications and to the business.
Generic algorithms can only create innovative and unique experiences that solve real-world business problems. Since most developers might not have the skills to train deep learning models from their own datasets, in this case, low-code solutions might help in lowering the barriers and enabling users to tag unstructured data, run simulations, generate models, and share reusable AI across.
Use Cases For Artificial Intelligence
AI has a plethora of use cases which started with computer image and processing. Currently, voice recognition, voice-to-text and text-to-voice are very common, and AI tools are being used to create sophisticated applications. The use of AI is reported to be also used in battery life management; predicting the lifespan of batteries for consumer vehicles and electric vehicles.
Low-code platforms, in the near future, might be a good tool for developers in order to incorporate AI into their business systems. For example, integrating a low-code method might be of good help to generate code for machine learning algorithms.
More and more people are getting interested in integrating AI in their applications. Businesses and organizations might start with simple needs, such as identifying text from handwritten letters or face identification. These rely on simple datasets and algorithms that already exist in order to accomplish most of these tasks.
Low-code is a near to perfect solution to avoid the point-and-click aspects of cropping, cleaning, and structuring data. “This can introduce automation into the process of labeling data while still involving some human intervention to fix mistakes. Machine learning models can take a while to train and simulate, underlining the need for a fast way of iterating on models. A common platform could help companies develop custom machine learning algorithms and models and keep track of experiments. This helps to ensure that the most effective model is used for the final application.” as reported by Reuter in an interview with an engineer from Google..
All in all, democratized AI is integral for businesses in order to take advantage of AI-based solutions that are coming to the market to replace old tools and processes.
However, there are arguments to be made for low-code AI tools, mostly in empowering non-technical users along with lowering the barrier to generate custom AI-driven functionality.