Building a speech start-up

A startup is a source of excitement at any time, but we don't live at just "any time".  Right now, we are experiencing an era of particularly rapid evolution of computing technology, and a period of dramatic evolution of new business models and real-world applications. The blanket term, "AI", has captured the world's imagination, and it is tempting to dismiss much of the breathless enthusiasm (and doomsaying) as just so much hype.  While there is a large dose of hype circulating, we must not overlook the very real and very potent emergence of deep learning or neural network methods as a substantially fresh approach to computing. The pace of improvement of algorithms, applications, and computing platforms over just the past five years shows that this approach — more statistical, more parallel, and more suitable for complex, essentially ambiguous problems — really is a big deal. 

Not surprisingly, a great deal of today's deep learning work is being directed at problems in computer vision: locating, classifying, segmenting, tagging, and captioning images and videos. Roughly half of all deep learning start-up companies are focused on one sort of vision problem or another. Deep learning is a great fit for these problems. Other developers and researchers have fanned out across a broad range of complex, data-intensive tasks in modeling financial markets, network security, recruiting, drug discovery, and transportation logistics. One domain is showing particular promise: speech processing. Speech has all the key characteristics of big data and deep complexity that ...

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