AI and Low/Non Code: What they can and can’t do together

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Artificial Intelligence (AI) is on the fast track and is moving towards mainstream enterprise acceptance, but in the meantime, there is another technology that makes its presence known: Low code and no code programming. While these two initiatives inhabit different areas within the data stack, they offer some exciting possibilities for working in tandem to greatly simplify and streamline data and product development processes.

Low code and no code It aims to make it easier to create new applications and services, to the point that even non-programmers – knowledge workers who already use these applications – can create the tools they need to complete their own tasks. It basically works by creating modular and interoperable functionality that can be mixed and matched to suit a variety of needs. If this technology can be combined with artificial intelligence to help guide development efforts, there is no telling how productive an organization’s workforce will be in a few short years.

smart programming

Venture capital is already beginning to flow in this direction. startup called Sway AI It recently launched a drag and drop platform that uses Open source models for artificial intelligence To enable low-code and non-code development for novice, intermediate, and expert users. The company claims that this will allow organizations to put new tools, including smart tools, into production faster, while at the same time fostering greater collaboration between users to expand and integrate these emerging data capabilities in efficient and highly productive ways. The company has already designed its generic platform for use cases specialized in healthcare, supply chain management and other sectors.

He says that the contribution of artificial intelligence to this process is basically the same as in other fields Jason Wong of Gartner i.e. doing repetitive tasks by heart, which in development processes include things like performance testing, quality assurance, and data analysis. Wong noted that while the use of AI in no-code and low-code development is still in its early stage, big hitters like Microsoft are keenly interested in applying it to areas such as platform analysis, data anonymization, and user interface development, which would significantly mitigate from the current situation. A skill shortage that prevents many initiatives from achieving production readiness.

Before we start dreaming of an improved, AI-powered development chain, however, we’ll need to address some practical concerns, according to the developer. Anouk Dhotri. For one thing, abstracting the code into configurable modules creates a lot of overhead, and this introduces process latency. AI is increasingly gravitating toward mobile and web apps, where delays of even 100 milliseconds can turn users off. For back office applications that tend to fall back on quietly for hours, this shouldn’t be a huge problem, but beyond that, this is unlikely to be a mature area for low or no code development either.

AI is restricted

Additionally, most low-code platforms are not very flexible, since they work with largely predefined modules. However, AI use cases are usually very specific and depend on what data is available and how it is stored, adapted, and processed. So, in all likelihood, you’ll need custom code to make the AI ​​model work correctly with other elements in the low/no-code template, and this could end up costing more than the platform itself. This same split affects functions like training and maintenance as well, where the resilience of the AI ​​is affected by low/no relative code rigidity.

However, adding a dose of machine learning to low-code and no-code platforms can help mitigate them, and add a much-needed dose of ethical behavior as well. Dataraj Raw For Fixed Systems He recently highlighted how ML could allow users to run pre-packaged patterns for processes such as feature engineering, data purification, model development, and statistical comparison, all of which should help create transparent, interpretable, and predictable models.

It’s probably an exaggeration to say that AI and low/low code are like chocolate and peanut butter, but there are strong reasons to expect that they can enhance each other’s strengths and reduce their weaknesses in a number of key applications. As an organization becomes increasingly dependent on developing new products and services, both technologies can remove many of the barriers currently impeding this process – and this is likely to remain the case regardless of whether they work together or independently.

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