Let's talk about some recent events & how intelligent computing could have helped..

A lot of us might have heard the news of earthquakes which rattled the western regions in America's.

When we think about it & wonder what was the worst part of these events -
    1) the fact that they occurred.
    2) the fact that they didn't give a prior notice before occurring.
    3) the fact that they might have been prevented.

Being an earthquake prone region, an occurrence of earthquake shouldn't be surprising but what is surprising is that the prediction models have just been constrained to the occurrence and not the simulated reality around it. Difficult to understand?

Let's take an example here - you wake up or maybe you are in the midst of doing something and you get an 'alert'(you can call it 'notification' - not usual time wasters but something like this)
- titled - 'seismic activity prediction' - (when expanded reads something like this) -

'there is a 75% probability of a seismic activity measuring 6 - 7.5 on the richter scale today between 2 & 3 pm EST. You might want to consider moving the following items - X, Y & Z - to a safer place to avoid losses or consequential damage costing upto 20% of your monthly earnings for the next 12 months.'

This is what current technology can do - they can simulate learning with prediction models to evaluate the risk of an earthquake and then predict the asset losses which can occur when such an activity occurs.

How - ask the right questions to the earth & earth's core & subsequent questions for changes in climatic conditions of atmosphere of different places on the seismic or fault zones and then establish a relation between them, perform deep learning using each model to arrive at most probabilistic activity.

Ask the right questions to know a  persons key items which they would not prefer loosing. The trick here is not a direct question but a question which forms a part of an answer to the direct question. If done correctly - the system should be able to predict the key items the consumer would not want to loose.

Then the only other thing left to do is to establish a relation between activity A & activity B, A being a seismic event & B - the affect it can have on that person.

Above appears to be simple when put forward but needs a lot of deep learning & artificial intelligence  arriving at conclusion when estimating the potential of a factor hampering the user.

Let's take another example - a famous company took a decision one day to remove the charge light or indicator which indicates a lap is charged or not from their chords, they also took the chords back 2 steps by keeping them non-magnetic. In short - however they want to interpret it but they lost the battle of savings vs indegenuity (their product turning backwards from a quality focussed one to an ordinary one) which will not be easy to get back unless they do something creative and out of the world.

Would this decision have been taken by an intelligent system - it wouldn't have gone backwards, as that system would have been able to relate to the consumption pattern accurately.

This way an intelligent system could help to aid in design decisions for crucial design areas and non-crucial but significant quality areas.