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Training of large-scale machine-learning models is extremely challenging because the training data is much more than the memory capacity. However, scientists at IBM and EPFL have collaborated to develop a novel scheme that enables the use of accelerators such as GPUs and FPGAs to speed up the training of machine learning models. They presented their findings at the 31st Annual Conference on Neural Information Processing Systems (NIPS) in Long Beach, California.

Machine learning and artificial intelligence (AI) are finding new applications across industries. Many tasks that were performed by humans are now being handled by machines, adding efficiency to the output. But what would happen if AI crosses the threshold of human control and makes unilateral decisions? It is a frightening, but highly probable, scenario. In 2014, it prompted Google to consider the idea of a “big red button” to stop dangerous AI in an emergency. However, the challenge is not in being able to stop or interrupt an AI process but in preventing AI from biased learning due to such frequent interruptions. The biased learning can be extremely dangerous in multi-agent systems, where several machines are involved in an AI task.

The program in French can be found here.