Scientists of AI at Google’s Google Brain and DeepMind units acknowledge machine learning is falling short of human cognition and propose that using models of networks might be a way to find relations between things that allow computers to generalize more broadly about the world.
Critics of the current mode of artificial intelligence technology have grown louder in the last couple of years, and this week, Google, one of the biggest commercial beneficiaries of the current vogue, offered a response, if, perhaps, not an answer, to the critics.
In a paper published by the Google Brain and the Deep Mind units of Google, researchers address shortcomings of the field and offer some techniques they hope will bring machine learning farther along the path to what would be “artificial general intelligence,” something more like human reasoning.
The research acknowledges that current “deep learning” approaches to AI have failed to achieve the ability to even approach human cognitive skills. Without dumping all that’s been achieved with things such as “convolutional neural networks,” or CNNs, the shining success of machine learning, they propose ways to impart broader reasoning skills.