28 Nov '17, 10am

#ArtificialIntelligence faces a power struggle if it is to be used more broadly

#ArtificialIntelligence faces a power struggle if it is to be used more broadly

If it is to break into a broader range of real-time applications, such as self-driving cars and robots, AI needs to be able to work without calling for help from remote servers. Even if the connections were entirely reliable, latency is incurred by data crossing the network before it can be interpreted. AI needs to be more local, which means cutting the power demand of its algorithms. One option is to move away from deep learning for these real-time systems, even though that has become practically synonymous with AI. Gradient descent links many of the techniques used in machine learning (ML) today. It extends from simple curve-fitting algorithms to highly complex loops and stacks of virtual neurons that underpin the video and audio recognition abilities of server-based deep-learning systems. While the current focus is hardware optimisation for deep learning, the same techn...

Full article: http://www.newelectronics.co.uk/electronics-technology/ai...

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