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( votes)Speech recognition had an error rate of 16% around the time Apple’s Siri was launched early last decade. Which means it wouldn’t understand many of the words/sentences we spoke to her, and so it provided no answers, or wrong answers. But as we spoke to her more, she learnt from it. Today, speech recognition systems have significantly lower error rates, they can even understand accents. But it has taken years to get there.
If you need to build great AI systems quickly, you need to throw a lot of data and compute power into it. More and more use cases are emerging where the AI system needs to instantaneously understand what’s going on to be able to respond to it. Braking by autonomous cars is a classic one.
Chips with AI acceleration, and chips that are designed for AI are coming in to deal with this. Semiconductor companies, startups, and even those like Google, Apple, Amazon and Facebook, for whom AI is central to what they do, are all developing such chips. A lot of this work is happening in India, one of the world’s foremost chip design hubs.
Fractal is also looking at these chips for a solution they call Customer Genomics, which mines massive customer data, like in banks, and recommends the next best action for the customer.
Ruchir Dixit, India country manager at semiconductor tools maker Siemens EDA, says such analytics is possible to do in software, but it won’t be fast enough. Many have used GPUs, because they are designed for heavy-duty graphics processing, but even those fall short for emerging requirements. “A machine learning algorithm implemented on hardware is always orders of magnitude faster. When I launch a software programme on my laptop, it has to find time from the CPU, even as the CPU deals with other computations it may be involved in, like an on-going video call. But if you put it in hardware, it doesn’t care what else you are doing, it will do it immediately because that’s what it is designed to do,” he says.
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