Nvidia, Intel and others are piling innovation resources into AI-specialised chip architectures amid heightened risk of competition from Chinese vendors
Specialist computer processing chips are poised to entrench the effects of deep artificial intelligence, two decades on from the rise of machine learning.
Deep learning algorithms already outperform the human eye when classifying pictures, and the costs of this and other image recognition-based tasks have plummeted.
The latest annual report from Stanford University’s AI Index pegs the cost of benchmark-accuracy image recognition using the cloud-hosted ImageNet repository at $12, against $2,323 two years ago.
The timeframe for training large image classification systems was estimated at just 88 seconds, down from about three hours in October 2017.
The impact has seen image recognition powering more use-cases, everything from agricultural problem-solving to computer vision-assisted driving systems.
On social media, regulators scramble to stop more iniquitous implications, with “deepfaked” photos vying to hoodwink users alongside computer-generated news stories.
More encouraging is the progress of AI drug developers such as Insilico Medicine, which revealed…
