Transformative AI, no-code, or low-code? The best approaches to deploying AI in your business

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The coronavirus pandemic has clearly accelerated our dependency on technology, online activities, and artificial intelligence. AI is particularly important for businesses as it enables personalized services on a massive scale, and customers are increasingly demanding it.

However, not every company has the knowledge or the tools to implement AI, nor do they know what is required from them to become AI-driven. In this post, I will discuss what options these companies have.

It is important to note that while many of the methods described below assist no-coders, they are also suitable for developers, who can enjoy the extra development speed they bring in.

Transformative AI

Ever since I was learning to program, the idea of developing a tool that could create applications with plain English commands was floating around. Many years later, OpenAI’s GPT-3 managed to get quite close to this idea as we saw demonstrations of code and HTML markup being written by the text generator.

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GPT-3 stands for Generative Pre-trained Transformer 3, which demonstrates the idea of training an AI on colossal amounts of data, then using that built-in knowledge to get stunning results for new tasks with little or no training. GPT-3 was trained using huge amounts of data including, among others, Common Crawl and Wikipedia. But more importantly, it was on supercomputers which enabled it to amass 175 billion parameter values, making it the largest AI model developed to date.

This has enabled the AI to use its current learnings and transform it to apply to other tasks. Transformative AI has many advantages, as it takes much less time to train and gives a head start compared to developing from scratch. It also makes AI much more accessible: companies only need to share their specific data with the model to make it their own. For instance, Anyline’s no-code AI trainer helps companies build their own text reader solutions (such as ID scanners or license plate readers). Customers simply upload their data into the trainer, which automatically tunes the neural networks for them to produce a customized OCR scanner.

Users do not need to learn how the system works or what the source code and architecture of the application look like—all they need to do is to feed the data they want intelligence on, and the AI adjusts accordingly.

Of course, some degree of AI knowledge is still necessary. According to Drew Conway’s Data Science Venn Diagram, effective development and implementation of AI requires two important skills: hacking skills, and math and statistics knowledge. Without these components in place, companies risk developing an AI that works well in lab settings but fails in when faced with real-world problems.

No-code or low-code

Another popular approach has been no-code and low-code platforms, which enable companies to develop their applications through simple drag-and-drop interfaces. No-code and low-code tools are the next battle frontier of the tech giants, as proven by Amazon’s late entry with its Honeycode platform. We are looking at a $13.2 billion market, which is projected to reach $45.5 billion by 2025.

According to Raj Koneru, the CEO and founder of conversational AI platform Kore.ai, no-code has many benefits. “No-code platform can be easily customized for developing an application. The effort that usually took a few weeks or months before can now be completed in a few hours or days,” Koneru says. This results not only in reduced time-to-market, but also reduced cost and dependency on IT and expensive development teams.

Another benefit is that no-code platforms are easily customizable. According to Koneru, no-code platforms enable you to “implement new logic and can have the changes ready in a matter of hours.” More importantly, it gives power to the people who use the platform most. They can now implement what they need on the fly without the need to explain things to another IT developer.

But no-code platforms also have their drawbacks. Many such platforms are cloud-based, and they tend to lock in clients in the long run. This makes changing platforms down the road problematic and time-consuming. Also, no-code applications tend to work well within their defined boundaries, but the struggle as soon as users need extra features that go beyond the built-in capabilities of the system.

Of course, there are ways to overcome these problems. For instance, while Kore.ai offers a drag-and-drop interface for virtual assistant builders, it also grants API connections to developers which gives them much more freedom to develop extra features. The same goes for Radial, an AI platform that helps e-commerce businesses analyze their customers. They offer a plug-and-play solution for regular users and API tools for more advanced clients.

The optimal approach

The importance of AI cannot be underestimated. Without extracting value and information from data, companies will be at a competitive disadvantage. What approach you take depends on your business needs and technical capabilities. Between transformer learning, no-code and low-code platforms, the optimal approach would be one that would enable you to reach your business goals and offer a moderate interface to develop applications without prohibiting you to move beyond the platform’s offerings.

This article was originally published by Paul McNeil on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.