Technology Just because we can't trust generative AI (yet), doesn't...

Just because we can’t trust generative AI (yet), doesn’t mean we should fear it

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While the release of ChatGPT brought a lot of chatter about generative AI’s revolutionary impact on technology, there was an equal amount of focus on some of the technology’s shortcomings. Indeed, there have been some heated debates about the potentially dangerous impact of generative AI on society, its conceivable negative applications, and the significant ethical concerns surrounding its development.

But from the point of view of IT and software development – ​​where many predict generative AI will have the most telling impact in the future – one question in particular continues to arise: to what extent can enterprises actually trust this technology to perform their critical and creative tasks ?

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The answer, at least at the moment, is not very much. The technology is too riddled with inaccuracies, has serious reliability issues, and lacks real-world context for businesses to rely on it completely. There are also some very valid concerns about the security vulnerabilities, namely how adversaries are using the technology to produce and distribute deceptive deepfake content.

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With all these concerns, companies should certainly ask themselves whether they can ensure the responsible use of generative AI. But they shouldn’t frighten them either. Of course, companies always have to strike a balance between caution and the endless possibilities of the technology. But business decision makers — and tech professionals in particular — should already be accustomed to acting responsibly when they get new innovations that promise to revolutionize their entire industry.

Let’s break down why.

Learning from previous innovations

Generative AI isn’t the first technology to meet fear and skepticism. Even cloud computing, which has been nothing short of a salvation since the start of the remote work revolution, has raised alarms among business leaders over concerns about data security, privacy, and reliability. In fact, many organizations were hesitant to use cloud solutions for fear of unauthorized access, data leaks and potential service outages.

However, as cloud providers improved security measures over time, implemented robust data protection protocols, and demonstrated high reliability, organizations gradually embraced it.

Open source software (OSS) is another example. Initially, there were concerns that it would lack quality, safety, and support compared to proprietary alternatives. Skepticism persisted due to fears of unregulated code changes and a perceived lack of accountability. But the open source movement gained momentum, leading to the development of highly reliable and widely accepted projects such as Linux, Apache, and MySQL. Today, open source software is ubiquitous in all IT domains, providing cost-effective solutions, rapid innovation, and community-driven support.

In other words, after some initial caution, companies have adopted and embraced these technologies.

Addressing the unique challenges of generative AI

This is not to minimize people’s concerns about generative AI. After all, there’s a long list of unique — and valid — concerns about the technology. For example, there are fairness and bias issues that need to be addressed before companies can really rely on them. Generative AI models learn from existing data, which means they can inadvertently perpetuate biases and unfair practices in the training dataset. These biases can in turn lead to discriminatory or skewed results.

In fact, when our recent questionnaire out of 400 CIOs and CTOs on their acceptance of and views on generative AI asked these leaders about their ethical concerns, “ensuring fairness and avoiding bias” was the top ethical consideration they cited.

Inaccuracies or subtle “hallucinations” pose another threat. These are not colossal errors, but they are errors nonetheless. For example, when I recently asked ChatGPT to tell me more about my company, it incorrectly listed three specific companies as previous clients.

These are certainly concerns that need to be addressed. But if you dig deeper, you’ll also find some that may be exaggerated, such as those speculating that these AI-powered innovations will replace human talent. All you need to do is do a quick Google search to see headlines about the top 10 jobs at risk or why workers’ AI fears are warranted. Usually its impact on software development is a particularly hot topic.

But if you ask IT pros, this really isn’t a problem. In fact, job losses ranked last among the ethical considerations of CIOs and CTOs in the aforementioned survey. Further, an overwhelming 88% said they believe generative AI cannot replace software developers, and half said they believe it will actually increase the strategic importance of IT leaders.

Cracking the code for the future of generative AI

Companies should recognize the need to approach generative AI with caution, just as they have had to do with other emerging technologies. But they can do so while also celebrating the transformative potential it has to advance the IT industry and beyond. The reality is that technology is already reshaping IT and software development spaces, and companies will never be able to stop it.

And they wouldn’t want to stop, given the promise of strengthening the capabilities of their best technical talents and improving the quality of software. These are possibilities that they should not be afraid of. At the same time, they’re capabilities they can’t fully appreciate until they address the demise of generative AI. Only when they do this can they maximize the power of generative AI to support IT and software development, improve efficiency and build more advanced software solutions.

Natalie Kaminski is co-founder and CEO of IT development company JetRockets.

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