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When the application detects audio snippets with speech commands requesting help or saying OEM Filling and sealing line Manufacturers "stop" in distressed tones, it generates SOS alerts and the location of the user, and sends them to emergency contacts specified by the user. Video demo here.  The app is now available on the Google Play store.The winning team of Piyush Agrawal, Subham Banga, Aniket Sharma and Ujjwal Upadhyay tackled the troubling issue of womens safety in India, where incidents of women under attack have become all too common. This enabled them to detect emotion, background noise, and Indian accents in the audio with improved precision.In addition to pursuing technical careers, Celestini Program India participants are building their careers by having their applications showcased in prominent forums such as the TensorFlow Developer Summit and Google I/O 2019. The winning team receives a cash prize of USD 1500 and the second-place team received USD 500. The Celestini Program India partners with IIT Delhi and is anchored by Dr Aakanksha Chowdhery, a researcher in Google Brain, and a 2012 Marconi Young Scholar.

They started with publicly available speech command datasets, such as the Google Speech command dataset, then added speech commands specific to the scenario of women’s safety.  The Program, run by the Societys Young Scholars,  is a flagship effort to  inspire and connect individuals building tomorrow’s technologies in service of a digitally inclusive world. This builds on the work done by last years Celestini Prize winners which showed that a machine learning model can be built to estimate air quality from an image by extracting image features such as transmission index or haziness and combining them with meteorological data and historical air quality data. The innovative aspect of this year’s application is to leverage federated learning to train the machine learning model in a privacy-aware manner instead of uploading photos from each user as shown in this tutorial. They crowd-sourced additional data and open-sourced it as the Indian EmoSpeech Command dataset.Details of the Celestini Program can be found here.A team from Bharti Vidyapeeth College of Engineering, Delhi, has developed an Android application, Rakshak, that detects speech patterns via the audio microphone of the user’s smartphone.The team winning the second prize --also from Bharti Vidyapeeth College of Engineering, Delhi – addressed air quality since India has 14 of the 15 most polluted cities in the world according to the World Health Organization’s Global Ambient Air Quality Database.The solution took the top prize today in a contest organized in India by the Marconi Society’s Celestini Program. The Marconi Society and its Young Scholars select universities with promising telecommunications and engineering undergrads and provide them with support and mentorship to help tap their students’ true potential.The VisionAir team also enables other developers to create new machine learning models by open-sourcing a diverse set of smartphone images taken across several locations in Delhi, from different phones with ground truth air quality data from Central Pollution Control Board monitors and Airveda sensors. 

Team members Harshita Diddee, Shivam Grover, Shivani Jindal and Divyanshu Sharma created a privacy-aware smartphone application called VisionAir which uses photos of the horizon taken from a smartphone to estimate  air quality.The Marconi Society, dedicated to celebrating, inspiring and connecting the innovators building tomorrow’s digitally inclusive world, has awarded top prizes to students in India who crafted innovative solutions that address the growing challenges of womens safety and air pollution. Federated learning only uploads the features extracted from the images without uploading the smartphone images to train the machine learning model.The winning teams showcased their innovation at a function in IIT Delhi, today, where Padmasree Warrior, IIT Delhi alumnus and former CTO of Motorola and Cisco Systems, gave the keynote speech.

And since IBM’s "Jeopardy" victory in 2011, the tech industry has shifted its efforts to data-intensive methods that seek to not just find factoids, but better comprehend the meaning of multi-sentence pasغير مجاز مي باشدes.Human v/s MachineIt’s not uncommon for machine-learning competitions to pit the cognitive abilities of computers against humans.Seven years ago, a computer beat two human quizmasters on a "Jeopardy" challenge. "All these companies and institutions are trying to establish themselves as the leader in AI. "You need some very simple reasoning here, but the machine cannot get it," said Jianfeng Gao, of Microsoft’s AI research division. But computers are still "far off" from truly understanding what they’re reading, said Michael Littman, a Brown University computer science professor who has tasked computers to solve crossword puzzles.Like the other tests, the Stanford Question Answering Dataset, nicknamed Squad, attracted a rivalry among research institutions and tech firms — with Google, Facebook, Tencent, Samsung and Salesforce also giving it a try.

Machines first bested people in an image-recognition competition in 2015 and a speech recognition competition last year, although they’re still easily tricked. A person’s careful reading of the Wikipedia pasغير مجاز مي باشدe would have discovered the right answer, but the computer tripped up on the word "most" and didn’t understand that seven is bigger than four.Research teams at Microsoft and Chinese tech company Alibaba reached what they described as a milestone earlier this month when their AI systems outperformed the estimated human score on a reading comprehension test. Computers have also vanquished humans at chess, Pac-Man and the strategy game Go."Limits of understandingThe tech industry’s collection and digitization of huge troves of data, combined with new sets of algorithms and more powerful computing, has helped inject new energy into a machine-learning field that’s been around for more than half a century.Computers are getting better at the statistical intuition that allows them to scan text and find what seems relevant, but they still struggle with the logical reasoning that comes naturally to people.Microsoft, for instance, fumbled an easy football question about which member of the NFL’s Carolina Panthers got the most interceptions in the 2015 season (the correct answer was Kurt Coleman, not Josh Norman). The answers they got wrong — and the test itself — also highlight the limitations of computer intelligence and the difficulty of comparing it directly to human intelligence. The computers, however, also made mistakes that many people wouldn’t have.". (And they are often hopeless when it comes to deciphering the subtle wink-and-nod trickery of a clever puzzle. The test developed at Stanford University demonstrated that, in at least some circumstances, computers can beat humans at quickly "reading" hundreds of Wikipedia entries and coming up with accurate answers to questions about Genghis Khan’s reign or the Apollo space program."It strikes me for the kind of problem that they’re solving that it’s not possible to do better than people because people are defining what’s correct," Littman said of the Stanford benchmark.

The impressive thing here is they met human performance, not that they’ve exceeded it. Ever since the tech industry has been training its machines to make them even better at amassing knowledge and answering questions.Error! Error!"We are still a long way from computers being able to read and comprehend general text in the same way that humans can," said Kevin Scott, Microsoft’s chief technology officer, in a LinkedIn post that also commended the achievement by the company’s Beijing-based researchers. "Academics love competitions," said Pranav Rajpurkar, the Stanford OEM Filling equipment Suppliers doctoral student who helped develop the test. It was the latest demonstration of rapid advances that have improved search engines and voice assistants and that are finding broader applications in health care and other fields. And it’s worked, at least up to a point.) Many of the common ways of measuring artificial intelligence are in some ways teaching to the test, Littman said. Just don’t expect artificial intelligence to spit out a literary analysis of Leo Tolstoy’s "War and Peace" anytime soon.