Arjun Chandrasekaran from Virginia Tech and pals say they’ve trained a machine-learning algorithm to recognize humorous scenes and even to create them. They say their machine can accurately predict when a scene is funny and when it is not, even though it knows nothing of the social context of what it is seeing.
Apparently, Arjun Chandrasekaran at Virginia Tech and his research team has trained an algorithm to differentiate visual images that are funny from images that are not. The system uses blind analytics – it has no idea why they are funny or not. It just looks at a massive number of visual primitives – associated with those that have been rated as funny or not, and then builds the algorithm. It can then be launched at new images to make predictions.
As with any research study, they used a narrow scope of visual images. It remains to be seen if it could work on the full set we see on Google Images or television. But their results within this narrow scope were significant and promising.
At first, I was hesitant and skeptical. But then I got to thinking about all of the neuroscience findings that show much of the way we think is not too far different from this (as much as our conscious brains try to convince us otherwise). So maybe it is not so farfetched.
I guess we will have to wait and see.
What is your gut instinct on this? Human factors has a long history of deciding what machines can be better at and what humans can be better at.
Is humor about to switch sides?
Image Credit: Ciaran McGuiggan