As a cybersecurity executive, I’ve been knee-deep in generative AI for many years, back when models like ChatGPT couldn’t answer a word problem correctly, much less challenge search engines for supremacy on the internet.
As a buzzword for more than a decade across industries, the labels of artificial intelligence and machine learning were often slapped onto algorithms or tools such as chatbots that failed to live up to the hype. What AI promised to be—an asset that translates raw data or questions into complex, accurate full-fledged reports with pattern recognition greater than the ability of any human—hadn’t yet materialized, and skepticism among business leaders was founded.
My firsthand experience has been in the cybersecurity field, in which advances in AI have caused the arms race between attackers and security practitioners to go nuclear. Bad actors with access to AI as advanced as GPT-4 can exploit it to find vulnerabilities and entry points.
And that’s just cybersecurity. Within the medical field, for example, AI can be used to analyze a patient's history to create more accurate diagnoses or assist doctors during surgery, and that’s not even mentioning the administrative tasks that could be automated with AI tools, like maintaining records, following up with patients and accelerating data entry.
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