If Elon Musk says something is hard, you can bet your bottom dollar that it is bloody hard. Automotive applications are some of the toughest, most unforgiving environments to deploy AI in. Most AI applications in automotive are mission critical - they just can’t go wrong. This requires that all the input they process is of a very high resolution or quality. They also need to operate in real-time, often perceive the environment around them and respond to it in a matter of milliseconds. Lastly, they need to operate with a very low power requirement, in an environment that is subject to dust, vibration and wide temperature variations. 

This intersection of constraints means that deep learning algorithms for automotive applications need to be designed right, and optimised constantly. This is where Deci comes in. Think of Deci as an easy-to-use platform that offers a set of tools to optimise your machine learning algorithms, and deliver order-of-magnitude performance improvements. 

In this episode of the AI in Automotive Podcast, I am joined by Yonatan Giefman, Co-founder & CEO of Deci. Yonatan tells us how AI applications are brought to life, and why they need to be optimised. We discuss the Deci platform, and how it can help data scientists and deep learning architects to optimise their algorithms, using the company’s proprietary neural algorithm search technology. We also talk about machines building machines, and even take a peek into science fiction :)

If you liked my chat with Yonatan today, do subscribe to the AI in Automotive podcast, and give us a shout on your social media.

https://www.ai-in-automotive.com/aiia/114/yonatangeifman

AI in Automotive Podcast