Ambiguous Threats and AI?

This isn’t about politics. See what we may learn from this article in HBR about leadership in the COVID-19 crisis and think about how the paradigm applies to the growing trend towards utilising Artificial Intelligence to assist in better decision making.

When facing ambiguous threats, leaders have to commit to a decision based on the best understanding of the information available at the time. In this case, both imagined the worst case scenario of mass infection and the potential consequences which may be suffered if nothing is done. They made the decision to take action early and in the New Zealand example, small and narrow indicators of change were monitored in order to trigger the enactment of stronger measures. Their strategies have been proven to be effective with reports in the media suggesting the New Zealand “haven’t just flattened the curve, they’ve squashed it.”

Jumping to the use of IoT technology, the ambiguous threat is the most difficult to address when the consequences cannot be visualised. Cyber security attacks are probably one of the best examples. They can be detected by AI and addressed quickly as they happen or after the event. But often the damage is already done. What if the Cyber attack could be predicted and patches and IoT security overhauled before the event. AI could analyse all blogs and vulnerability reports and predict the threat of another BotNet attack from the online chatter about the subject and automatically raise the alarm enabling relevant action to be taken quickly.

Artificial Intelligence is often seen to be lending itself to the cause to solve very tightly defined problems. We will soon see more instances of “super AIs” being developed and applied to a set of specialised AIs, painting a picture from the pockets of analysis of seemingly unrelated data. This big picture intelligence will vastly improve the ability to detect an ambiguous threat and enable action before the threat becomes a consequence.

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