AI evolution - in order to understand this first let us consider what is AI?
Now let's move ahead to understand where AI originated from and where it stands today -
Neural networks and the basis of the AI calculation and logic have been around from a long time but AI was initially limited to the decisioning based on available computing power and resources.
Hence the true decisioning with close to human capability was never achieved. With the evolution of connected computing and cloud resources along with quantum computing - AI got a bigger engine to incorporate decisioning logic.
Imagine it to be a Saturn V rocket thruster as compared to a standard economy class car with few horse powers - that is the level of boost that was achieved with the kind of compute power that was available for a network to arrive at a decision.
Along with this we had the storage capacity restraint earlier which went away with virtually unlimited storage availability.
Because of above two factors AI grew from an infant which doesn't understand anything and talks only few words to a full blown adult with any decisioning capability.
Now since we covered the journey - let's look at the situation today - what AI is capable of as of today and near future?
As of today - computers have been able to deduce and defeat humans in some of the brain challenging games like AlphaGo but it doesn't end here. Currently big firms are working on developing software which can work on the central brain and take advantage of the AI capabilities - these come as close to humans and sometimes more accurate than humans to identify and decision.
They follow the simple cycle -
Train --> Deduce --> Decision --> Feedback --> Retrain --> ...
..and this cycle continues. Thereby they evolve as entities able to decision better and better, at the expense of their experiences with each decision. This means three things -
1. They think faster.
2. They improve their decision capabilities.
3. They don't have emotional qualms which can draw the decision back at times.
What this means is accuracy and precision coupled with focussed results - if used correctly - giving a big gain or profit to human kind, but then it comes at the expense of humans following what the computer predicts which is a bit tricky because once the system understands the 'good' and the 'bad' and then it starts to understand the dependency curve --
-- system needs to be good to decision and serve humans better
where does the thin line of taking a decision for it's betterment to serve humans better goes over a decision to not serve some humans in the best way -- this is like a human thinking about itself - that is where it starts to get a bit muddy.
What should be done to make sure that above doesn't happen?
Currently humans have an oversight of what is the result of a decision by AI or neural network but don't have any vision of how this decision was achieved - in order to have this - humans should also become by any way possible a part of that decisioning and feedback process.
This can be achievable by many means - one is to plant a chip to provide that feedback to humans - another can be to install the human thought decisioning on the AI as an end-result.. this fine line is necessary so that the balance is still maintained...
Currently with the speed at which neural processing is changing - it's going to be very quickly exceeding the human decisioning capability, hence above measures should be good as a stop cork on anything which might go in harms way.
Artificial intelligence is the intelligence demonstrated via a machine in contrast to the natural intelligence demonstrated via humans.
I am sure everyone is aware of the experiment where human and computer are judged on their responses to test a true artificial intelligent machine.
I am sure everyone is aware of the experiment where human and computer are judged on their responses to test a true artificial intelligent machine.
Now let's move ahead to understand where AI originated from and where it stands today -
Neural networks and the basis of the AI calculation and logic have been around from a long time but AI was initially limited to the decisioning based on available computing power and resources.
Hence the true decisioning with close to human capability was never achieved. With the evolution of connected computing and cloud resources along with quantum computing - AI got a bigger engine to incorporate decisioning logic.
Imagine it to be a Saturn V rocket thruster as compared to a standard economy class car with few horse powers - that is the level of boost that was achieved with the kind of compute power that was available for a network to arrive at a decision.
Along with this we had the storage capacity restraint earlier which went away with virtually unlimited storage availability.
Because of above two factors AI grew from an infant which doesn't understand anything and talks only few words to a full blown adult with any decisioning capability.
Now since we covered the journey - let's look at the situation today - what AI is capable of as of today and near future?
As of today - computers have been able to deduce and defeat humans in some of the brain challenging games like AlphaGo but it doesn't end here. Currently big firms are working on developing software which can work on the central brain and take advantage of the AI capabilities - these come as close to humans and sometimes more accurate than humans to identify and decision.
They follow the simple cycle -
Train --> Deduce --> Decision --> Feedback --> Retrain --> ...
..and this cycle continues. Thereby they evolve as entities able to decision better and better, at the expense of their experiences with each decision. This means three things -
1. They think faster.
2. They improve their decision capabilities.
3. They don't have emotional qualms which can draw the decision back at times.
What this means is accuracy and precision coupled with focussed results - if used correctly - giving a big gain or profit to human kind, but then it comes at the expense of humans following what the computer predicts which is a bit tricky because once the system understands the 'good' and the 'bad' and then it starts to understand the dependency curve --
-- system needs to be good to decision and serve humans better
where does the thin line of taking a decision for it's betterment to serve humans better goes over a decision to not serve some humans in the best way -- this is like a human thinking about itself - that is where it starts to get a bit muddy.
What should be done to make sure that above doesn't happen?
Currently humans have an oversight of what is the result of a decision by AI or neural network but don't have any vision of how this decision was achieved - in order to have this - humans should also become by any way possible a part of that decisioning and feedback process.
This can be achievable by many means - one is to plant a chip to provide that feedback to humans - another can be to install the human thought decisioning on the AI as an end-result.. this fine line is necessary so that the balance is still maintained...
Currently with the speed at which neural processing is changing - it's going to be very quickly exceeding the human decisioning capability, hence above measures should be good as a stop cork on anything which might go in harms way.
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