If AI starts to generate intelligence by itself, there’s no guarantee that it will be human-like. Rather than humans teaching machines to think like humans, machines might teach humans new ways of thinking.

Clune also stresses the importance of thinking about the ethics of the new technology from the start. There is a good chance that AI-designed neural networks and algorithms will be even harder to understand than today’s already opaque black-box systems.

Rui Wang thinks human-designed challenges are going to be a bottleneck and that real progress in AI will require AI to come up with its own. “No matter how good algorithms are today, they are always tested on some hand-designed benchmark,” he says. “It’s very hard to imagine artificial general intelligence coming from this, because it is bound by f... See more

model-agnostic meta-learning, or MAML, it trains a model using two machine-learning processes, one nested inside the other.[...] setting his AI the challenge of learning more than one task led it to come up with its own version of a sparse model that outperformed human-designed ones.

The new system, called AutoML Zero, tries to build an AI from the ground up using nothing but the most basic mathematical concepts that govern machine learning.Amazingly, not only did AutoML Zero spontaneously build a neural network, but it came up with gradient descent, the most common mathematical technique that human designers use to train a net... See more

The neural networks coded by human scientists are often the result of trial and error. There is little theory to what does and doesn’t work, and no guarantee that the best designs have been found.

His point is that if intelligence as we know it resulted from the mindless mutation of genes over countless generations, why not seek to replicate the intelligence-producing process—which is arguably simpler—rather than intelligence itself?

AlphaGo (and its successor AlphaZero), which beat the best humanity has to offer at the ancient, beguiling game of Go by employing seemingly alien strategies. After hundreds of years of study by human masters, AI found solutions no one had ever thought of.

I may be an avid consumer of ac-tion movies and yet consider arthouse films aesthetically superior, eventhough I watch them more rarely. Artificial systems that gather data abouthuman aesthetic consumption should take these issues into consideration ifwe want to avoid overly simplistic models of human aesthetic experienceand judgment