A Nanyang Technological University, Singapore (NTU Singapore) study has found that some Singaporeans have reported that, despite being aware of the existence of ‘deepfakes’ in general, they believe they have circulated deepfake content on social media which they later found out was a hoax.
Deepfakes, a portmanteau of ‘deep learning’ and ‘fake’, are ultrarealistic fake videos made with artificial intelligence (AI) software to depict people doing things they have never done — not just slowing them down or changing the pitch of their voice, but also making them appear to say things that they have never said at all.
In a survey of 1,231 Singaporeans led by NTU Singapore’s Assistant Professor Saifuddin Ahmed, 54 per cent of the respondents said they were aware of deepfakes, of which one in three reported sharing content on social media that they subsequently learnt was a deepfake.
The study also found that more than
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A computer vision technology developed by University of Cambridge engineers has now been developed into a free mobile phone app for regular monitoring of glucose levels in people with diabetes.
The app uses computer vision techniques to read and record the glucose levels, time and date displayed on a typical glucose test via the camera on a mobile phone. The technology, which doesn’t require an internet or Bluetooth connection, works for any type of glucose meter, in any orientation and in a variety of light levels. It also reduces waste by eliminating the need to replace high-quality non-Bluetooth meters, making it a cost-effective solution to the NHS.
Working with UK glucose testing company GlucoRx, the Cambridge researchers have developed the technology into a free mobile phone app, called GlucoRx Vision, which is now available on the Apple App Store and Google Play Store.
To use the app, users simply take
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Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).
The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.
The research will be presented at next month’s Conference on Neural Information Processing Systems. The lead author is Ji Lin, a PhD student in Song Han’s lab in MIT’s Department of
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