nvidia x1 Connectivity Solutions Will Not Help with Autonomo(2)

The main problem is that machine learning needs huge amounts of data to yield good results. The connectivity on fields is often bad. We are lucky if we have a 2G connection in rural areas. This is not enough for 50 data points per second. The telematics box must buffer the data and send them later when the connectivity is better.

A good 3G connection can easily handle 100 and more data points. However, we run into another limiting factor: cost. Transferring 100 data points per second with 6 bytes per data point accumulates 1.5 GB of raw data per month. We can bring the data volume down to 300-500 MB by applying some clever compression and compaction techniques. Currently, we pay roughly 50 Euros per machine per month for such a connectivity solution. We do not only pay for the harvesting season of 2-4 months but also for the other 8-10 months.

If we wanted to use the data for machine learning, we would talk about huge amounts of data. We would have to transfer the complete CAN traffic and the video streams from several cameras – just to start with. The cost would be astronomical.

If we cannot transfer the data from the machine to the cloud, we move the learning of a model and its application from the cloud to the machine. This is certainly the way to go, but it is not feasible at the moment.

Machine learning requires a lot of compute power to crunch through Terabytes of data and to find an optimal model for image recognition or the continuous, automatic adjustment of the cutting height of maize or corn. They need groups of high-performance computers, which are equipped with graphic cards, where thousands of little cores work together in parallel, and which are connected by high-speed networks.

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