New AI algorithm is able to differentiate creativity
The last two bastions holding out against the rise of the AI machines are creativity and care – anything in fact that has its foundations in human emotion.
Already AI is encroaching on these last human outposts – we recently reported that AI was being taught to simulate anger in order to teach humans (!) how to deal with other angry human beings. We also reported how AI learns emotion.
The next stage of course is AI becomes creative in itself – UK startup Yossarian Lives has created a metaphorical search engine that assists human creativity:
Now it’s learning to understand and differentiate human creativity . . . the article below from MIT provides some excellent commentary.
It’s looking like that last untouched bastion is human care. But perhaps that’s even on the AI Trust and Regret as well . . .
From Creative AI: Machine Vision Algorithm Chooses the Most Creative Paintings in History
Picking the most creative paintings is a network problem akin to finding super spreaders of disease. That’s allowed a machine to pick out the most creative paintings in history.
Creativity is one of humanity’s uniquely defining qualities. Numerous thinkers have explored the qualities that creativity must have, and most pick out two important factors: whatever the process of creativity produces, it must be novel and it must be influential.
The history of art is filled with good examples in the form of paintings that are unlike any that have appeared before and that have hugely influenced those that follow. Leonardo’s 1469 Madonna and child with a pomegranate, Goya’s 1780 Christ crucified or Monet’s 1865 Haystacks at Chailly at sunrise and so on. Others paintings are more derivative, showing many similarities with those that have gone before and so are thought of as less creative.
The job of distinguishing the most creative from the others falls to art historians. And it is no easy task. It requires, at the very least, an encyclopedic knowledge of the history of art. The historian must then spot novel features and be able to recognize similar features in future paintings to determine their influence.
Those are tricky tasks for a human and until recently, it would have been unimaginable that a computer could take them on. But today that changes thanks to the work of Ahmed Elgammal and Babak Saleh at Rutgers University in New Jersey, who say they have a machine that can do just this.