AICTE, IIT Madras-incubated Enterprise Bags World Record For Holding Online Program For Maximum Users

AICTE, IIT Madras-incubated Enterprise Bags World Record For Holding Online Program For Maximum Users

All India Council For Technical Education (AICTE) along with ‘Guvi Geek Network’ – an IIT Madras incubated company got recognition from the Guinness World Records for holding an online computer programme in 24 hours for maximum number of users. Guinness World Records has presented a certificate to both AICTE and Guvi Geek Network.

“The most users to take an online computer programme lesson in 24 hours was achieved by Guvi Geek Network Private Limited and AICTE (both India) online from April 24 to 25,” reads the certificate presented by Guinness World Records.

Through a tweet, Union Education Minister Ramesh Pokhriyal ‘Nishank’ congratulated AICTE and Guvi Geek Network for the achievement.

GUVI was started by a group of IIT Madras students as a YouTube channel in 2012. It was introduced as a two-sided platform, where students can learn to do programming and companies can come and recruit. Currently, the channel has over 39,000 subscribers.

Last year, CBSE and Intel India also set a Guinness World Record for maximum users to take an online artificial intelligence (AI) lesson in 24 hours. The record was set after delivering a virtual lesson focused on demystifying the impact of AI and key considerations around its use to 13,000 students from class 8 and above between October 13-14.

Meanwhile, IIT Madras was recently also in news for devising a technology which uses the power of artificial neural networks to bring degraded images back to life.

Dr A. N. Rajagopalan, the chair professor of Sterlite Technologies in the Electrical Engineering department at IIT Madras came up with this novel technology of cleaning old, damaged photographs or videos marred by weather conditions such as rain, haze, hail, and more. He worked in collaboration with his assistants Maitreya Suin and Kuldeep Purohit.

This new technology uses a network of artificial neural groups as restoring the degraded portions and cleansing the image was nearly impossible with a single neural network.

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