What actually is Artificial Intelligence?

I recall studying models of reasoning a few years ago Lotus and perhaps what I believe I learnt from that might not answer the question but might be worth "bringing to the party" as it were.

I learnt about hypothetico-deductive reasoning - "it could be this, so lets see if the facts fit..."

Pattern recognition - "I've seen this before"

Abstract reasoning - "I could use a coat rack instead"

I have concluded that only rarely do I personally feel safe entirely relying on just one of those models and types for serious decisions - they each have their time and place as far as I have discerned and blending them can be especially useful.

Since pattern recognition seems to be an intrinsic part of the AI models that I have encountered (and as owl_enthusiast explains well) those perhaps are less likely to "think outside of the box" in the fashion you describe.  

I would add tho' that for pretty much all of the uses they are currently applied to they do already "think outside the box" but not in the fashion you describe.  They present a version of thought that may not already be inside the "box" of the users head perhaps?  I suggest this because from a personal perspective I have found this especially useful in "decoding and revealing" some of the social communication issues that I find especially problematic with my own "spiky profile" of autism.  And of course the mundane "turning the handle" tasks where the speed of access and analysis of vast amounts of data

Herein lies a challenge as other contributions to this thread have already made clear - this does not entirely replace the need for the individual user to think themselves before employing that knowledge.

I have no pleasure in a particular business use of the term "meta", however "meta-cognition" is a useful term to also consider.

Once artists had to grind and blend their own pigments and binder media before they could paint (at least at the start of their career).  I am inclined to believe that this process enabled both greater insight into the nature and potential means of applying them for the people concerned and maybe helped them be better artists.

To pick up on the points that other contributors to this thread have made, the further we are separated from understanding how something is done perhaps the more likely we are inclined to make a mistakes.

Best wishes

PS to reframe and old joke: Q: how many AI's does it take to change a lightbulb?  A:  A fish :-)

I recall studying models of reasoning a few years ago Lotus and perhaps what I believe I learnt from that might not answer the question but might be worth "bringing to the party" as it were.

I learnt about hypothetico-deductive reasoning - "it could be this, so lets see if the facts fit..."

Pattern recognition - "I've seen this before"

Abstract reasoning - "I could use a coat rack instead"

I have concluded that only rarely do I personally feel safe entirely relying on just one of those models and types for serious decisions - they each have their time and place as far as I have discerned and blending them can be especially useful.

Since pattern recognition seems to be an intrinsic part of the AI models that I have encountered (and as owl_enthusiast explains well) those perhaps are less likely to "think outside of the box" in the fashion you describe.  

I would add tho' that for pretty much all of the uses they are currently applied to they do already "think outside the box" but not in the fashion you describe.  They present a version of thought that may not already be inside the "box" of the users head perhaps?  I suggest this because from a personal perspective I have found this especially useful in "decoding and revealing" some of the social communication issues that I find especially problematic with my own "spiky profile" of autism.  And of course the mundane "turning the handle" tasks where the speed of access and analysis of vast amounts of data

Herein lies a challenge as other contributions to this thread have already made clear - this does not entirely replace the need for the individual user to think themselves before employing that knowledge.

I have no pleasure in a particular business use of the term "meta", however "meta-cognition" is a useful term to also consider.

Once artists had to grind and blend their own pigments and binder media before they could paint (at least at the start of their career).  I am inclined to believe that this process enabled both greater insight into the nature and potential means of applying them for the people concerned and maybe helped them be better artists.

To pick up on the points that other contributors to this thread have made, the further we are separated from understanding how something is done perhaps the more likely we are inclined to make a mistakes.

Best wishes

PS to reframe and old joke: Q: how many AI's does it take to change a lightbulb?  A:  A fish :-)