In a recent interview with AITechPark, Kognic's CEO, Daniel Langkilde, provided a glimpse into the important levers inside our ADAS/AD industry and how Kognic is accelerating the pace of change in Embodied AI.
A quick excerpt
During the interview, Daniel was asked if there are specific strategies or best practices for implementing an iterative mindset in AI - ML product development. As more customers enable our dataset management platform, Daniel remarked that Kognic helps to,
"...remap software organisations to think about “programming with data” versus “programming with code”. For this, the skill sets of product developers, engineers and other technical staff need to be adept and comfortable with exploring, shaping and explaining their datasets. Stop trying to address machine learning as a finite process, but rather an ongoing cycle of annotation, insights and refinement against performance criteria."
More context - what matters for ADAS / AD?
Daniel also discussed the ongoing challenge of aligning a systems outcome with human expectation. In our industry of Automotive and Mobility as a whole, this is a critical dynamic. What if you asked a team of engineers, product developers and annotators this question - what is a road?
"The answer can actually vary significantly, depending on where you are in the world, the topography of the area you are in and what kind of driving habits you lean towards. For these factors and much more, aligning and agreeing on what is a road is far easier said than done.
So then, how can an AI product or autonomous vehicle make not only the correct decision but one that aligns with human expectations? To solve this, our platform allows for human feedback to be efficiently captured and used to train the dataset used by the AI model.
Doing so is no easy task, there’s huge amounts of complex data an autonomous vehicle is dealing with, from multi-sensor inputs from a camera, LiDAR, and radar data in large-scale sequences, highlighting not only the importance of alignment but the challenge it poses when dealing with data."
To read the full interview with Daniel, link over to AITechPark here.
