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Iyantras AI
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Artificial Intelligence Solutions
Artificial Intelligence Solutions

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Let AI maintain the human centric culture,by observing their emotions into and describe as [Happy, Sad and Anger]

#FacialExpressionRecognition #HumanEmotions #FaceRecognition #Iyantras
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We provide #humanrecognition with #motioncapture for #trafficsecuritysolutions that ensures better and safe travel on roads.
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Our AI system implements video analytics for organizations involving #crowdmonitoring,#surveillance and #infrastructuremanagement
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We deliver emotion status of an individual, with enhanced #imageprocessing and #deeplearning techniques
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Social Media for Blind

People upload and share more than 2 billion photos across social media like Facebook, Instagram, Messenger and WhatsApp every day. While visual content provides fun and expression for people online, it’s not so for people with low vision or blindness as it’s a challenge to them. Artificial neural networks convert pictures to automatic alt text for screen reader users on mobile and web. Companies working on ‘object recognition technology’ are based on a neural network that has billions of parameters and trains the system with millions of examples. It describes about the visual content for the blind.

#SocialMediaforBlind
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Autism to Artificial Intelligence

Maithilee Kunda, an ex-MIT innovator and visionary has done laudable work in artificial intelligence, specifically in computational cognitive systems. She has been recognised for her work that looks at how visual thinking contributes to learning and intelligent behaviour with a focus on interactive applications for individuals on the autism spectrum. The experiments were conducted to show case the cognitive interlinking of different sensory inputs to produce meaningful information.

Iyantras salutes all women involved in providing AI solutions for betterment of society like Maithilee Kunda.

Happy International Women’s Day 2017

#Womensday #Internationalwomensday #Artificialintelligence
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3/8/17
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Cognitive Intelligence in #ridesharing improves traffic on Roads:

“Instead of transporting people one at a time, drivers could transport two to four people at once, resulting in fewer trips, in less time, to make the same amount of money”-gives boost to companies like Uber and this ride sharing needs an intelligent factor in resolving a problem of least distance covered by each driver to connect all passengers.

Using data from 3 million taxi rides, the new algorithm works in real-time to reroute cars based on incoming requests, and can also proactively send idle cars to areas with high demand — a step that speeds up service 20 percent.

A key challenge is to develop a real-time solution that considers the thousands of vehicles and requests at once that enables us to understand and abstract the road network at a fine level of detail. This “anytime optimal algorithm,” gets better the more times you run it.





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Machine learning approach to track the Trajectory of humans in Crowd :

Team of AI group develops an algorithm that predicts the human trajectory by analyzing the traveled path. The project titles pedestrians how follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicles like tesla navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of #RecurrentNeuralNetwork (RNN) models for sequence prediction tasks, propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. It demonstrates the performance of our method on several public datasets. This model outperforms state-of-the-art methods on some of these datasets; also analyze the trajectories predicted by our model to demonstrate the motion behavior learned by our model. Resources from Alexandre Alahi.

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2/7/17
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AI that makes #Lipreading Possible with better Accuracy:

Better be careful while you whisper in TV Shows, Now Google Deep Mind and University of Oxford has applied deep learning to a huge data of BBC Programmes to create lip reading systems. Over 5000 hours from six different TV programmes that contains 118000 sentences,

This AI vastly outperformed a professional lip-reader who attempted to decipher 200 randomly selected clips from the data set. The professional annotated just 12.4 per cent of words without any error. But the AI annotated 46.8 per cent of all words in the data set without any error. And many of its mistakes were small slips, like missing an‘s’ at the end of a word. With these results, the system also outperforms all other automatic lip-reading systems.

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Ant #NeuralNetwork:
Ant with pin tip sized brain trains a neural circuit individually and exposed to a virtual environment where a swarm of ants performed a resource food searching task. This model comprises an associative and unsupervised learning strategy for the neural circuit of the ant. The neural circuit adapts to the environment by means of classical conditioning. Initially unknown environment includes different types of stimuli representing food (rewarding) and obstacles (harmful) which, when they come in direct contact with the ant, elicit a reflex response in the motor neural system of the ant: moving towards or away from the source of the stimulus. The spiking neural circuits of the ant are trained to identify food and obstacles and move towards the former and avoid the latter. The ants are released on a landscape with multiple food sources where one ant alone would have difficulty harvesting the landscape to maximum efficiency. so they use #collectiveintelligence to solve the complex problem and achieve results.
#Iyantras #ArtificialIntelligence
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