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Deep learning's big technical break finally arrived in the mid-2000s, when leading researcher Geoffrey Hinton discovered a way to efficiently train those new layers in neural networks. The result was like giving steroids to the old neural networks, multiplying their power to perform tasks such as speech and object recognition.

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While Geoff was not my Ph.D. advisor, his impact on my Ph.D. thesis was tremendous. His student Peter Brown (co-inventor of statistical machine translation, now CEO of Renaissance Technologies) was my mentor, and taught me how to apply various types of machine learning algorithms to speech recognition. This was a primary reason that helped my Ph.D. thesis to become the best-performing speech recognizer in 1988, which helped shift the speech recognition field from expert-systems approach to machine-learning approach. If I have benefited so much from Geoff and Peter, there must be thousands of other beneficiaries, given Geoff's brilliance, persistence, and generosity.

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