Which of the following statements about wearable technology applications is NOT correct? ()
A. Wearable health monitoring devices can collect and monitor physiological indicators such as weight, blood pressure, blood sugar, heart rhythm, sleep, and galvanic skin response to protect users' health.
B. Wearable technology can use the coaching function to perform specific fitness tasks or training for users.
C. Flexible materials can allow wearable technology to provide users with a more comfortable wearing experience.
D. The current wearable technology can complete the detection of the user's internal organ health.
查看答案
() does not belong to the software layer technology or core algorithm of internal brain computing
A. Multimodal cooperative perception
Brain computer interface
C. Impulse neural network
D. Inferential and deductive decision
Which of the following statements about brain-like computing development level is not correct? ()
A. Brain-like computers and brain-like robots belong to the product layer.
B. Machine vision, auditory perception, multimodal collaborative perception, natural language understanding, etc. belong to the software layer.
C. Pulse neural networks, brain plasticity mechanisms, brain maps, etc. belong to the basic theoretical layer.
D. Brain-like chips belong to the hardware layer.
Which following is wrong about the characteristics of reinforcement learning
A. Quick and prompt feedback
B. No supervision, only reward
C. Strong versatility and popularization, the agent is also applied in a new environment of true uncertainty.
D. The behavior of the agent will affect the subsequent data and have a continuous impact on the environment
What is correct for deep learning is
A. By building a multi-hidden layer model and massive training data to learn more useful features, and ultimately improve the accuracy of classification or prediction
B. "Deep model" is the means, "feature learning" is the purpose
C. Emphasizes the depth of the model structure, usually there are 5-10 layers of hidden nodes
D. Compared with the method of artificially constructing features, using big data to learn features is more able to characterize the rich internal information of the data
微信支付 
支付宝支付