How is AI used in cars?

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Artificial intelligence (AI) systems, which use data and algorithms to mimic the cognitive functions of the human mind, and have the ability to learn and solve problems independently, are rapidly being deployed across a variety of industries and use cases. The automotive industry is among the industries at the forefront of using AI to mimic, augment, and support the actions of humans, while simultaneously leveraging the advanced reaction times and pinpoint precision of machine-based systems. Indeed, today’s semi-autonomous vehicles and the fully autonomous vehicles of the future will rely heavily on AI systems.

Beyond self-driving vehicles, AI can also be used to make life in the car more convenient and safer, for both the driver and the passengers.  In-car assistants, driven by natural language processing (NLP) and machine learning techniques, allow the vehicle’s systems to respond to voice commands and infer what actions to take, without human intervention. Despite the technological potential of both autonomous vehicles and in-car assistants, an abundance of caution relating to safety concerns, and a desire to ensure users enjoy a smooth, glitch-free experience, these AI systems likely will be deployed gradually. Tractica forecasts that the market for automotive AI hardware, software, and services will reach $26.5 billion by 2025, up from $1.2 billion in 2017.

Companies such as Waymo and Tesla are heavily invested in driverless cars. Currently, Waymo has begun testing of driverless cars again after stopping in 2017. Testing is done with drivers inside the vehicles until the company is able to gain enough data to move towards a completely driverless solution.

The Amazing Ways Tesla Is Using Artificial Intelligence

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Tesla has become a household name as a leader and pioneer in the electric vehicle market, but it also manufactures and sells advanced battery and solar panel technology.

Each Tesla computer has two AI chips, a redundant design for better safety, Venkataramanan said. There’s redundancy in the chips’ power supplies and data input feeds, too. Even the car’s cameras are on two separate power supplies to guard against failures.

“There are a lot of redundancy features, which makes sure … nothing untoward happens to the system” if a sensor, component, camera or power supply fails, Venkataramanan said. Each chip makes its own assessment of what the car should do next. The computer compares the two assessments, and if the chips agree, the car takes the action. If the chips disagree, the car just throws away that frame of video data and tries again, Venkataramanan said. That’s one of the reasons Tesla wanted powerful AI chips that could handle such a high frame rate for video.

Tesla has clearly always been a company that has put data collection and analysis at the heart of everything it does. It isn’t just design and manufacturing either, with the company processing customer data with AI and even parsing its online forum for text insights into common problems. Whether this focus will lead to victory in the upcoming battle for supremacy of the autonomous car market remains to be seen, but it has certainly provided itself with a head start.