Top-10 Reasons Why AI Fails and "Cures" via AIUserForum.com - <2 Minute Video

AI has almost mythical status as a cure for nearly everything, global climate change and global peace.?

Yet beyond all the hype and the billions spent the results so far are rather dismal.

Forbes reports 40% of organizations making “significant investments” in AI do not report any business gains from AI.

Here are 10 of the key AI failures not including malicious efforts.

1 - Too narrow - one SKU (stock keeping unit) or product not others.

2 - Too wide - breadth not depth - can analyze ocean water but not what's in the ocean.

3 - Too complex - helps with auto but not truck claims.

4 - Costs too much - analyzing data takes a lot of time and computing.

5 - Changing needs - AI systems work best with solving a large problem that doesn't change.

6 - Sequential failures - an example might include "after I "drive" down the street, what should I know to "walk" through the mall?".

7 - Escalation failures - the AI system may provide call routing to the "right" agent after a long series of questions.

8 - Data poisoning - you cannot assume that all the data is not maliciously damaged, distorted or worse.

9 - Cascading failures - too much interdependence on other systems aka APIs for solutions.

10 - Drug Contraindication - drug "side effects" facing nearly every patient.

11 - Future - AI is generally a machine programmed for now.

Yet AI?is often wrong nor capable without humans to change at all or fast enough..

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