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It is finally resonating with me that incorporating Deep Learning at the Edge has the potential to create a paradigm shift in the way robots are deployed in manufacturing operations. FANUC's aggressive move to integrated Deep Learning technologies could revolutionize the way robotic systems are deployed. When you consider how robots are deployed in manufacturing operations today, the application programs employ traditional procedural and function programming methods. But as robots increasingly rely upon vision systems to identify and locate geometric patterns on a work piece, the logic and decision making no longer has to be all pre-programmed in order to process the workpiece.
Today, every robotic application program applies the experiential knowledge of a human expert to account for every possible situation that may arise in the manufacturing operation. So if you can envision the application incorporating innumerable "it then" statements throughout the body of the application it is only possible to make a robot marginally adaptive in a production environment. By injecting Deep Learning into the mix, it becomes possible is to make a robot adaptive and biological in nature. FANUC appears to be placing a big bet on Deep Learning at the Edge. This is in contrast to the majority of the market relying on Big Data in the Cloud where machine learning algorithms are employed to identify patterns in the data.
In Aug 2015, FANUC announced a capital investment in the Tokyo based startup, Preferred Networks (PFN) (https://www.preferred-networks.jp/en). Preferred Networks is focused on applying artificial intelligence technologies to support next generation manufacturing technologies for the Industrial Internet of Things. The company uses machine learning and deep learning technologies that process big data at edge, at real time and enable high level of automation at manufacturing sites such as machine tools and robotics. In Oct 2016,
NVIDIA (http://www.nvidia.com/object/machine-learning.html) and FANUC announced a collaboration to implement artificial intelligence on the FANUC Intelligent Edge Link and Drive (FIELD) system, to increase robotics productivity and bring new capabilities to automated factories worldwide. NVIDIA has developed highly advanced Graphical Processing Units (GPU) that incorporate Deep Learning tools (Neural Nets). With a platform that supports consumer market applications such as autonomous driving vehicles as well as medical applications, the combination of the research in Deep Learning algorithms from PFN and advanced GPUs from NVIDIA has a high likelihood of changing IIoT architectures where more and more processing of Big Data is localized at the production line.
Initially, applications will be applied to improve upon the decision making for robotics systems to locate and pick work pieces using vision systems. However, the future of Deep Learning combined with AI has an increasing number of possibilities as the collaboration of robots and humans becomes more prevalent. And as increasingly more robots are simultaneously collaborating on a work piece, a localized AI system will simplify the application programming for these relatively complex applications.
Today, every robotic application program applies the experiential knowledge of a human expert to account for every possible situation that may arise in the manufacturing operation. So if you can envision the application incorporating innumerable "it then" statements throughout the body of the application it is only possible to make a robot marginally adaptive in a production environment. By injecting Deep Learning into the mix, it becomes possible is to make a robot adaptive and biological in nature. FANUC appears to be placing a big bet on Deep Learning at the Edge. This is in contrast to the majority of the market relying on Big Data in the Cloud where machine learning algorithms are employed to identify patterns in the data.
In Aug 2015, FANUC announced a capital investment in the Tokyo based startup, Preferred Networks (PFN) (https://www.preferred-networks.jp/en). Preferred Networks is focused on applying artificial intelligence technologies to support next generation manufacturing technologies for the Industrial Internet of Things. The company uses machine learning and deep learning technologies that process big data at edge, at real time and enable high level of automation at manufacturing sites such as machine tools and robotics. In Oct 2016, NVIDIA (http://www.nvidia.com/object/machine-learning.html) and FANUC announced a collaboration to implement artificial intelligence on the FANUC Intelligent Edge Link and Drive (FIELD) system, to increase robotics productivity and bring new capabilities to automated factories worldwide. NVIDIA has developed highly advanced Graphical Processing Units (GPU) that incorporate Deep Learning tools (Neural Nets). With a platform that supports consumer market applications such as autonomous driving vehicles as well as medical applications, the combination of the research in Deep Learning algorithms from PFN and advanced GPUs from NVIDIA has a high likelihood of changing IIoT architectures where more and more processing of Big Data is localized at the production line.
Initially, applications will be applied to improve upon the decision making for robotics systems to locate and pick work pieces using vision systems. However, the future of Deep Learning combined with AI has an increasing number of possibilities as the collaboration of robots and humans becomes more prevalent. And as increasingly more robots are simultaneously collaborating on a work piece, a localized AI system will simplify the application programming for these relatively complex applications.