We have many problems, few apparent solutions, and could use some novel ideas about what to do next. Among inventors, the flash of genius comes not from nowhere, but usually by analogy — one thing is so, so why not another? In the 1940s, this was how Italian microbiologist Salvador Luria, watching a slot machine work, conceived his Nobel Prize-winning extension of Darwinism to bacteria. In 1666, Isaac Newton saw an apple fall from a tree, and originated his theory of gravity.
The trouble with this approach to invention is the unpredictability of a good analogy — you simply have to wait for that spark. But in a new paper, researchers at Carnegie Mellon and Hebrew University say they've made an advance toward automating the process of finding and melding wholly unconnected things into big ideas.
To get started, says Carnegie Mellon's Aniket Kittur, researchers hired a bunch of people as a crowd-sourcing group. Their assignment: to attach analogy labels to hundreds of products.
- These descriptions were fed into a neuron network—a machine-learning system—which trained on them.
- The machine, after investigating far more material, spit out what, in its view, were related analogies.
- Those were handed back to the crowd-source group, which used them to suggest new products.
- The result: the human-AI team produced the most innovative ideas, the researchers said.
A first step: "Analogy has driven human progress," Kittur tells Axios. "This doesn't solve the whole thing. But it is the first step showing the practical benefits of finding analogies at scale."
Read the rest of the post.