Artificial intelligence platforms like ChatGPT can be put to work generating a mountain of repetitive boilerplate coding tasks, says Elenjical Solutions’ Siju Mammen.
A boon to every errant schoolboy hoping for undeservedly good marks for his homework, it turns out that ChatGPT may also become software developers and fintech firms’ new best friend.
Although still in their infancy, Large Language models (LLMs) such as ChatGPT and artificial intelligence (AI) in general, have already shown their potential to transform business operations and shape the future of work. We’ve never had a system as easy and natural to interact with as ChatGPT and its rivals Google Bard, Microsoft Bing Chat et al. No platform is perfect yet, but its huge potential is already clear – and businesses that learn to harness its benefits early on will have first mover advantage.
In a recent pilot study, Elenjical Solutions, a leading South African fintech company, tested the capabilities of various Language Learning Models (LLMs) in multiple scenarios and measured an increase in productivity while using these tools. In fact, in certain cases, software developers using these tools were able to write complex computer programs in about half the time. Siju Mammen, technology lead at Elenjical Solutions has been closely following AI’s development since 2017, comments: “There was a feeling within the tech community that it would take decades for AI to reach human level natural language communication. Incredibly, however, it has only taken five years.”
Force behind the future
Much of the excitement surrounding AI can be attributed to its ability to facilitate natural language communication with technology. As humans, the most intuitive way we communicate is by talking to one another. And so, the prospect of machines being able to ‘talk’ with us intelligibly and display cognitive behaviors is the holy grail of human/machine interaction.
“AI is not one piece of tech, but rather a collection of tools, techniques, algorithms and data working together to achieve a certain goal,” explains Mammen. “ChatGPT, for example, is built on all the knowledge of the world we can find on the Internet. All of this information has, in a way, been internalised by these tools,” he says.
Machine learning is one of the most common types of AI in development for business purposes today. It excels in processing vast amounts of data rapidly. These AI algorithms appear to ‘learn’ and improve over time. Integrating AI into any organisation is a substantial undertaking, especially within niche businesses. It takes in-depth knowledge and a lot of time and effort to train the system and iron out inaccuracies.
Liberation from repetitive tasks
“Elenjical Solutions is famous for its deep knowledge of the Murex financial technology solution, and tools such as Chat GPT will over time be able to handle many tasks efficiently and accurately. However, it is essential the AI is securely underpinned by deep human expertise,” explains Bereket Demeke, ES’s Executive Manager.
Precision in progress
“AI tools have become surprisingly good at generating boilerplate code — not necessarily complex code, but the repetitive code that every programmer needs to write, such as frameworks for various tasks. These tools can churn out such code efficiently,” says Siju Mammen.
Programmers at Elenjical Solutions envision a global goal for AI development — a reality where AI can perform virtually all human tasks without distinction. However, Siju Mammen, unlike much of the industry, believes that we are still a few decades away from achieving this. ”And even if this is achieved sooner,” he emphasises, “hard problems are hard, not because people are unintelligent, but because they are inherently challenging. They will still be hard for AI – but the combination of humans and AI empowered machines offers great potential for generating positive outcomes.”
Working with companies that deal with significant financial transactions naturally raises concerns about data security and privacy when integrating AI. The reality is that the system does not have unrestricted access to such information itself. “It’s crucial to be careful regarding the use of private, proprietary information when using these systems,” adds Demeke. Fortunately, privacy and security concerns can be alleviated by making certain architectural decisions when creating systems that use these tools.
Accountancy firm PwC predicts that AI will increase global GDP by up to 15% by 2030, with the same research suggesting that AI could contribute as much as $15.7 trillion to the global economy by that time.
A bit like cheating schoolboys, the rise of artificial intelligence in business is unstoppable. AI holds immense promise for business growth, efficiency and innovation. However, it’s essential to remember that AI and humans are not competitors in this brave new world, but important collaborators.
