Perhaps the trick is to start small. At the EmTech Digital conference on AI in San Francisco this week, Microsoft Research Postdoctoral Researcher Timnit Gebru suggested taking cues from the field of hardware design. To understand what problems this would solve, it helps to have a clear idea of exactly how bias in AI works. A typical AI is trained on data from the past, looks for patterns in that data, then makes predictions about the future. So if your dataset is racist or sexist–and much data is–the AI will generalize from that bias and exacerbate it.

At the MIT EmTech Digital conference, startup Nervana announced plans to design and build a custom ASIC processor for neural networks and machine learning applications that the company's CEO, Naveen Rao, claims will run 10 times faster than graphic processor units (GPU). The news comes after Google last week announced it had secretly deployed its neural network and machine-learning-tailored processors in its data centers about a year ago. The company reported that its custom processor had improved performance by an order of magnitude. GPUs have become synonymous with machine learning. Interest in machine learning exploded a few years ago when Alex Krizhevsky, a student of artificial intelligence (AI) luminary Geoff Hinton at the University of Toronto, proved that machine learning systems could be trained on economically priced GPU hardware.