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However, at a time when the full-screen display is a massive trend in the smartphone industry, these huge bezels will certainly not be appealing to many potential buyers. "STEP AWAY FROM THE AI FANFARE AND FOCUS ON SOFTWARE RELIABILITY" Step away from the AI fanfare and focus on software reliability Here is a challenge for you: go to any popular technology trade show and attempt to escape the talk tracks that focus on AI. AI is still very much en vogue, the zeitgeist that promises to be the cornerstone of the technical defining moments of this decade. However, while AI is undoubtedly going to change everything, in some ways it is simply the poster child for code that executes more intelligently than it did before. My favourite (tongue-in-cheek) definition of AI is anything that computers can do well today that they couldn’t do ten years ago. Decades ago that was playing chess; today it’s playing Go or driving a car. The user scenarios demonstrated at the tech shows are still a pipe dream for many developers.

Examples of AI systems in Tesla’s automated cars and Google’s Deep Mind AlphaGo which beat the world champion of Go, are complex intelligent systems far from the everyday ones that typical engineering teams deal with. It’s a bit like teenage sex – everyone is talking about it but few are doing it. AI threat to jobs When AI is not heralded as the next imperative, it is vilified as being the grim reaper set to destroy humanity, or at least to steal our jobs. Since the Industrial Revolution, automation has progressively replaced workforces, from the Jacquard Loom and agricultural mechanisation to automated supermarket checkouts and online accounting software.

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In reality, nearly all business applications created today are still based on traditional software. When it comes to jobs, humans will continue to work alongside automated systems, roles will evolve to account for the management of , complex systems and human decision-making will go hand and hand with their development. While many senior software engineers within tech companies are charged by the executive team to “implement AI”, they are all still dealing with business applications based on conventional software. In a world where about 95 percent of ATM transactions use COBOL, and at a time when a range of products remain built on decades-old code, AI headlines distract from the real software reliability issues at play. (Image: © Image Credit: Geralt / Pixabay) Laying the foundation for a successful AI deployment AI, like any traditional business system or application, needs a robust foundation.

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Before being able to come to grips with creating complex applications, engineers need to address immediate software reliability issues, first. Nearly all software ships with undiagnosed bugs that may turn into serious production incidents, resulting in client churn and burning hours of engineering resource later down the line. It would be foolish to ignore the promise – and the threats – heralded by AI; it is equally foolish to ignore the problems and risks from the unreliability of today’s “regular” software. Commercial pressures mean that software development managers and their teams have to make trade-offs between code quality and the pressure to ship product features.