Sameer Wasson discusses the future of automation and intelligence at the edge.
The future of intelligent machines rests on innovation at the edge – the embedded technology that enables real-time sensing and processing for more dynamic decision-making.
Automation that used to be preprogrammed and structured has evolved so that machines now understand in real time what’s happening in their environment and can react to it intelligently, safely, securely and autonomously. The technology that enables this is machine learning – a subset of artificial intelligence – and it’s transforming machines that were once line cooks into chefs. But they’re not quite master chefs.
As signal processing technology has evolved and added more machine learning features along the way, we have opened the door for advances in vehicle occupancy detection, intuitive human-machine interaction and more without needing to rely on cloud processing every time.
For example, edge intelligence in your future vehicle will be able to sense an object nearby and classify it as a pedestrian. The machine learns from this experience in real time and evaluates data, such as response time between object detection to vehicle action, to improve over time.
And when you park it in the garage that evening, it connects to the cloud and shares that knowledge with the entire connected fleet.
Now take that technology into a field of corn, where planters are programmed to sow seeds about every six inches. Since the ground can be inconsistent, sometimes seeds don’t do well – they might need to be planted deeper or spaced farther apart. Embedded intelligence enables the planter to analyze the soil for moisture, nutrients and other data before a seed is planted. It can predict how many seeds will successfully mature, and the data can be uploaded to the cloud so that farmers can forecast yields.
Or imagine your future shopping experience: in stores that are on the cutting edge of retail automation, customers scan their phone as they walk in. A combination of cameras and in-shelf sensors tally up the items put into their basket, automatically billing customers when they leave.
Currently, this requires sending streams of data from potentially hundreds of thousands of stores up to the cloud for processing by machine learning algorithms. That's an enormous amount of data, which can present significant challenges. With TI mmWave sensors and processors – highly intelligent sensors that integrate precise, real-time decision-making and processing on a single chip – that data can be processed in the store itself to reduce that load.
Eventually, the boundary between the edge and the cloud will start to get very interesting. How rapidly the technology can prioritize which data to send to the cloud quickly, repeatedly and consistently – and receive actionable information back – will be the next problem to solve.
As we find solutions at the edge for automation, our everyday machines will continue to make our lives more convenient, efficient and safer.
Sameer Wasson is vice president and general manager of our Processors business unit.
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