First, a brief off-topic introduction. A quick search returns the following definition for creativity: “the use of imagination or original ideas to create something; inventiveness”. Not surprisingly, the definition refers to two other words which are similarly awkward if you’re thinking like a machine… Still, we know the process exists, everyone can name creative persons they know directly, so the subject definitely exists. Have we however reached a point where creativity has been distilled down enough to allow coding it into a machine?
Answer to that might depend the understanding of “thinking as a machine”. I wrote that intentionally, because it seems the terms are generally misused. I am used to getting a few sales pitches every month about how their new AI or even machine learning solution will revolutionize my business. I’d usually ask how it works and the answer puts the solutions in one of these pots:
a) simple logic
b) actual AI / machine learning
c) deep learning
As you can guess, most answers end up being a), sometimes a mixture of a) and b).
What’s the difference? Software allowed conditional responses for a very long time. Conditional clauses can be smartly designed and nested to a point where it seems the software (usually the frontend…) is “very smart”, or even “can predict what the user needs”. That’s not AI though.
AI kicks in where an actual algorithm, based on a model, can make assumptions or scoring while no direct reaction has been designed into the software. We have AI everywhere already: every spam filter, every antivirus engine follows uses this approach. For anything more complex though, like aiding in decision making, the computing capacity was so high that only now AI for wider use is becoming popular.
And finally there’s deep learning, which brings AI into a new tier: not only the algorithm goes over data to build rules, it can further based on previous runs build further rules, and rules for such rules, and so on. From computation, that’s usually very expensive, which is why there’s a new blooming market for chips and FPGA solutions that optimize this specific type of load to achieve good timing responses in the learning and decision making process.
I hope this becomes a good introduction to what I actually wanted to share. There’s an excellent paper on Arxiv detailing how tasks submitted to AI/ML solutions were “resolved”, sometimes in the most surprising way. The paper covers 27 anegdotes about how AI found the most surprising answers (to the surprise of the researchers).
From this perspective, wouldn’t you call it pure creativity? And if yes, I’d like to propose a new definition of Creativity: “in the huge lake of data (facts and clues), find new connections that are valuable”? I guess it works for the term “intelligence” as well, if you cross out the word “new”.