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An In-Cabin AI That Heeds You About Your Driver’s Mood While Driving


ABC new, just uploaded a video ​​of an Uber self-driving car accident. Though it disturbs us psychologically, what is important to note from that video are 2 things. Every vehicle in San Francisco is attached to a sensor which updates its presence for GPS tracking purposes. The cyclist removed the sensor and walked in a wrong direction. The driver of the car is not alert. She is performing activities on the phone while driving. There is an old saying -” It takes two hands to clap and make a noise”. In this incident, the driver and the cyclist both made mistake. The final result is a loss of a human life. Now, to avoid such things further, especially in this techno-wing of emerging self-driving cars, the only way is to heed at every minute of our life. But, we are human beings and mistakes is are natural. Some could be reflexes, other be unthinking.As a solution, the In- cabin Artificial Intelligence software from Affectiva tracks the emotional state of driver and passengers through the journey. Why In-cabin AI software for self-driving cars? Most of the self-driving operate using the lidar, cameras and radars located outside the car. So, a car sees what’s going outside

How Big Tech is Using Neural Machine Translation to Bridge Borders


As technology for machine learning (ML) and artificial intelligence (AI) develops, one industry experiencing a big impact is translation. Recently, three of the major players in the tech world announced that they had already successfully reached major milestones in the field, or were well on their way to doing so. Facebook announced that they were making the shift towards a new neural network system for translation, and Microsoft laid ground for making their own translator hub, which is expected to be done using AI and deep learning. The most impressive of these announcements was Google’s Neural Machine Translation (GNMT), a system that introduced a completely new world of machine translation. What is NMT? Most media translation services nowadays will translate a phrase or sentence word by word. This often results in less exact translations, as it is easy for this system to miss out on contextual meanings. Neural Machine Translation, on the other hand, is the process of training a large neural network to optimize translation performance. It does this by using neural network models to learn statistical operations for machine translation. The system can be trained as one, allowing for a single output rather than multiple. To understand what this means, we must first