Dell PowerEdge C4140 Deep Learning Performance Comparison - Scale-up vs. Scale - Page 7

Deep Learning Performance: Scale-up vs Scale-out 2 Introduction Figure 1: Artificial Intelligence, Machine Learning and Deep Learning [Source: MIT] Artificial Intelligence First coined in 1956 by John McCarthy, AI involves machines that can perform tasks that are characteristic of human intelligence. While this is rather general, it includes things like planning, understanding language, recognizing objects and sounds, learning, and problem solving. Machine Learning Arthur Samuel coined the phrase not too long after AI, in 1959, defining it as, "the ability to learn without being explicitly programmed." You see, you can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees. So instead of hard coding software routines with specific instructions to accomplish a task, machine learning is a way of "training" an algorithm so that it can learn how. "Training" involves feeding massive amounts of data to the algorithm and allowing the algorithm to adjust itself and improve. Spiking Neural Networks Spiking neural networks (SNNs) are artificial neural network models that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential - an intrinsic quality of the neuron related to its membrane Architectures & Technologies Dell EMC | Infrastructure Solutions Group 6