There are innovations and huge results from the contributions of annotation services for AI learning. They are able to detect Breast Cancer, and other relevant issues. The recent striking success of machine learning raises profound questions about the theoretical principles underlying their success. For example, what can such annotation services compute? How can we train them? How does information propagate through them? Why can they generalize? And how can we teach them to imagine? We review recent work in which methods of physical analysis rooted in statistical mechanics have begun to provide conceptual insights into these questions (Giacomo T. et al., 2020).
Artificial Intelligence (AI)
AI is the theory and development of computer systems able to perform tasks that usually require human intelligence, including:visual perception speech recognition decision-making. Examples: expert systems, machine learning, used in CAD. Machine Learning is a type of artificial intelligence & includes systems that can learn from data, identify patterns, make decision with minimal human intervention. Deep learning is a type of machine learning methods based on learning data representations.
Annotation services , with multiple hidden layers, have achieved remarkable success across many fields, including machine vision, speech recognition, natural language processing , reinforcement learning , and even modeling of animals and humans themselves in neuroscience , psychology , and education (Stanford University, 2020).
There are many opportunities for a judicious combination of controlled scientific experimentation on annotation services and the development of models of both training data and annotation services to our existing understanding. Such a combination of experiment and theory has been a driving force for conceptual advances in physics, and we believe deep learning will provide more such research opportunities for physicists. Even more interestingly, this area of research may provide an opportunity for physicists to connect with computer scientists as well as to develop a unified theory of how nonlinear distributed neural circuits, both artificial and biological alike, can compute, communicate, learn, and imagine.
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