how can AI reduce the probability of human errors?
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The growing complexity of software has meant that human developers are increasingly powerless to manage enormous interconnected applications. There is now a need to focus on developing AI that talks not to humans but directly to computer programs, creating software that codes, edits and tests itself. This will ensure better software that is more secure and impervious to attack.
With technology becoming more complex, it’s now impossible for humans to keep software safe. As demonstrated by the Tesla autopilot car crash, human error is to blame more than “bad” AI. In this case, autopilot sensors failed to distinguish a white trailer against a bright sky, something we can now attribute to human error within the code.
The way to fix this is through AI itself. AI can test software for us, replacing the human element. AI can do what humans have done for years – but better – and also learn along the way, fixing issues that would otherwise plague our software. This should lower the risk of cyber attacks, and also make software safer to use.
This capability already exists. DiffBlue, for example, have created AI that automates all traditional coding tasks: bug fixing, test writing, finding and fixing exploits, refactoring code, translating from one language to another, and creating original code to fit specifications. It should mean that increasingly complex software is comprehensively tested by other software, eliminating human error.
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