NVidia Caterpillar

 

I keep hearing the same subtext when people talk seriously about modern perception systems, and the podcast made it explicit. Perception is no longer about a clever model running on top of generic hardware. It is a full stack problem where sensors, electronics, data, simulation, training, deployment, and iteration speed all matter equally. If one of those layers is sloppy, the system fails no matter how good the neural network looks on a benchmark.

What resonated most is how far we have moved away from the idea that perception starts with data and ends with inference. In practice, perception starts with physics. Photons, vibrations, motion, noise, timing, power stability, thermal drift. These shape the data long before a model ever sees it. If you ignore this layer, you end up compensating with bigger models, more compute, and endless data cleaning. That is not sophistication, it is waste.

This is where the opportunity for making becomes obvious. Instead of building generic robots or chasing full autonomy, the real leverage is in building small, purpose-built perception instruments. A node, not a platform. One sensing problem, one or two sensors, tightly integrated electronics, deterministic timing, clean power, and just enough local intelligence to extract structure from the signal. Everything else can be pushed upstream.

The podcast emphasized simulation and synthetic data as first-class tools, not backups. That only works if your hardware is well defined. When you control the sensor characteristics, the sampling, the noise profile, and the geometry, simulation becomes meaningful. When your hardware is ad hoc, synthetic data becomes fiction. Making your own electronics is what closes that gap. It turns simulation into a usable engineering tool rather than a marketing slide.

From a practical standpoint, this reframes how I think about AI on the edge. The device does not need to be smart in a human sense. It needs to be precise. Timestamping, synchronization, filtering, event detection, compression, maybe a small embedding or classifier. That is enough. The heavy reasoning, training, and iteration live on a workstation or server where iteration is cheap. Edge intelligence exists to reduce ambiguity and bandwidth, not to impress.

The build loop becomes very concrete. Design a small board around a camera, IMU, microphone, or low-cost LiDAR. Get the clocking right. Get the power right. Mount it correctly. Collect data you trust. Augment it with simulation that actually matches the device. Train a narrow model for one task. Deploy it back. Observe failure modes. Revise both the electronics and the model. Repeat. This loop is faster and more educational than any abstract model comparison.

What I take away most strongly is that iteration speed beats theoretical optimality. Teams and individuals who can close the loop from field failure back to retraining and redeployment will always outperform those chasing perfect architectures. Custom hardware accelerates that loop because it removes unknowns. You know what the sensor is doing because you built it.

For anyone interested in #make perception with AI, the path is clear. Do not start with autonomy. Start with perception primitives. Build devices that see, hear, or feel one thing well. Treat electronics as part of the learning system, not a carrier for it. When physics and electronics are handled with care, the AI becomes smaller, simpler, and more reliable. That is not a compromise. That is good engineering.