Code optimisation for better AI: TurinTech joins the IoT Insider podcast

After rounding off a year where AI made waves through business and society, our CEO Dr Leslie Kanthan spoke to IoT Insider’s Editor Kristian McCann on the IoT unplugged podcast  for the first episode of the new year. Leslie covers a whole range of topics on AI and code optimisation. From discussing AI’s recent surge and the drawbacks of current models, to training software developers and how code optimisation can impact the future of the industry. 

The unseen costs of AI 

While AI products have been very visible, the effects of their growth are less noticed. “You’re seeing the advent of automation and AI in the general market right now, with products like ChatGPT […]”, explains Leslie. “What you’re not seeing is the huge amount of resources that’s consuming, the models, how large they are, how much the compute costs, the cloud costs, and so forth.”

These drawbacks are creating significant inefficiencies for businesses using AI, as well as having a sizeable environmental impact – the issues that code optimisation sets out to tackle. But how does it work? 

Leslie outlines the process of AI optimisation, which involves identifying “inefficient elements and components” in the source code, generating new code using AI, and comparing the improvements made to the original code. For businesses, this is particularly effective given that “previously, optimisation techniques were a manual process […] it could take years for several developers”. 

Overcoming AI pain points

Companies integrating or using AI can suffer from common pain points. As Leslie states, “everything is now about compute power and, consequently, compute cost. […] The amount of energy consumption in this process is significant.” So, by implementing code optimisation, “you have a business impact straight away”. 

And the impact of code optimisation goes beyond the direct impact on AI models. Production teams can get bottlenecked and stuck in their process. So, as Leslie touches on, “if you can optimise your code so it’s faster, you’re getting it into production much quicker.” The subsequent benefits of this also means, ultimately, more profitability. 

Those who stand to benefit

What industries does Leslie see benefiting from code optimisation the most? Leslie picks out the financial and technology sectors – and everywhere where they are doing continuous integration. 

While it can deliver great business outcomes, Leslie is keen to flag how the process can also bring great benefits to software teams. He discusses feedback from a client about how developers are using their tool to “learn and improve themselves” by showing where the code can be better optimised. Rather than feeling AI is replacing them, it cultivates more acceptance, showing it can augment their capabilities. 

Machine learning in IoT and future trends

Looking ahead, Leslie outlines how being able to optimise operating code “can reduce the energy consumption of IoT devices” as well as helping to identify sensor data and monitor temperatures. The huge datasets in the IoT world make it perfect for AI optimisation. 

This optimisation can be used for hardware, models and potentially even devices. Referencing his own phone, Leslie notes how leaving apps on idle, for instance, consumes large chunks of battery power, and so there is great potential for optimising battery tech. 

There’s a shared buzz about AI. Everyone is looking at it, but there are huge costs attached to it, with millions spent on building and refining the LLMs out there. AI optimisation will help to maximise the benefits of these models for businesses while simultaneously reducing costs.

To find out more, listen to the full podcast episode here. To stay up to date and follow our progress, check out our LinkedIn and Twitter.

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