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Design goal Compatible with other formats Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration DNN model interconnect Common platform TensorFlow, Keras, Caffe, Torch: Algorithm training No No / Separate files in most formats No No No Yes ONNX: Algorithm training Yes
Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [ 1 ]
Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial ...
The basic idea is that a visual metaphor enhances intuitive thinking, inductive reasoning, and pattern recognition—important cognitive skills usually less accessible in a verbal or text discussion. A business decision map can be seen as one approach to a formal decision language to support decision intelligence. [7]
They can, for instance, generate an automatic transcript from a video. The combination of automation and artificial intelligence (AI) enables autonomy for the robots, along with the capability to perform cognitive tasks. [6] [self-published source] At this stage, the robot can learn and improve processes by analyzing and adapting them. [7]
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