The challenge with AI is that it’s not there to replace everything, it’s there to augment what we already do, but do it quicker and hopefully with…

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The challenge with AI is that it’s not there to replace everything, it’s there to augment what we already do, but do it quicker and hopefully with improved efficiency.

Rachel V. Rose, JD, MBA, medical AI, should in theory be domain specific. The domain LLMs should be taught using domain specific data for domain specific reasoning. The FDA has been clear about its expectations of the use of AI through it draft guidance.

https://lnkd.in/dbezEhuC

As an engineer one remembers 30 years ago (I’m getting old), when we adopted Six Sigma (god bless you Jack Welch). The simple reality was, if you automated a crap process all you achieved was the creation of more scrap, just quicker. The challenge with AI is we’ve now given everyone a Ferrari, when they were driving a Ford (nothing wrong with Ford), and expect them to be able to handle it straight out the gate.

The same is true for AI. If you use AI to improve a process, coz that’s what you’re doing. Then you can expect unconventional results. The speed and power of Ai is not in doubt, but its speed and power and ability to make decisions mean its imperative to ensure that that decisions, based on its reasoning are continually evaluated.

@Thaddeus Dziekanowski Brian D. McCarthy Faisal Khan

Veritas GRC

#cybersecurity #cyberriskmanagement

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