Computer vision is the lynchpin of the current AI/machine learning (ML) renaissance. In 2013, deep learning based vision solutions crossed a historic milestone of achieving human level accuracy.
Since then, the pace of algorithm sophistication, training data proliferation and performance has accelerated even faster.
The majority of AI/machine learning powered solutions across the supply chain follow a sense→think→act paradigm. Consequently, Computer and AI vision is critical for all such applications where visual information is a key for sensing.
It is useful to think about vision-enabled solutions along two dimensions:
First, learning algorithms can go from narrow (supervised learning) to more flexible paradigms. Specifically, reinforcement and unsupervised learning. Narrow based solutions can only solve problems that is similar to its original training context. Flexible based solutions can handle situations that it has not necessarily seen before during initial training.
Second is the type of data input required for the solution to work as intended. Does the solution take discrete visual signals as input? Or is there a need to ingest and respond to a real-time, continuous stream of visual inputs? (see figure below):
While flexible based solutions are still in proof of concept stages, companies should continue to monitor progress as breakthroughs would significantly lower the current high training data requirements for solution development.
The above blog originally appeared in SupplyChainDive Influencer series