Everything that moves will be autonomous.

Machines are integrating into the real world as Embodied AI. Embodied AI systems control a physical device, like an autonomous vehicle or a robot. These systems move and interact with our physical environment, similar to how humans do. In contrast, most language models live on cloud servers, unable to take action. Despite their size, they are simpler as they only deal with text as the underlying data.

We believe Data is code.

The evolution from "programming by code" to "programming by example" – driven by machine learning – places the dataset at the center. Since machine learning learns by brute-force, datasets need to be extensive. This is where the Kognic platform comes into play. Annotating data is programming. With Kognic, it's made easier, more efficient, consistent, and accurate.

Sensor-fusion is the best way for Embodied AI to see the world 

Sensor-fusion combines input from different sensors - cameras, LiDARs, and radars - allowing  machines to see the world. It requires specialized tools and workflows and generates large datasets.

Human feedback is necessary and critical.

Annotating is more than manual work; it's the encoding of human knowledge where it matters most. Some believe AI is improving so rapidly that humans will soon be redundant. However, we see evidence to the contrary. We are running out of training data for AI, and the only way to create more is for humans to encode more knowledge. The need for digitally encoded human knowledge increases as AI becomes more capable. 

Real-world AI products require evolving datasets.

Simply treating datasets as a fixed asset, used only once by a machine learning model, severely underestimates the dynamic nature of data capture and model development. Kognic has designed its platform around an iterative way of working, recognizing that datasets evolve and grow, to optimize sensor-fusion datasets.

Better data, better AI.

Fine-tuning through human feedback helps bridge the semantic gap, providing the AI with insights into complex, context-dependent aspects of data that are not easily captured through automated processes alone. Humans can provide labeled data, corrections, and guidance for accurately training AI models. Refining model predictions reduces errors and improves overall performance, allowing adaptation to new patterns, trends, and expectations.

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