Mike Pence: China is the top economic and strategic threat to the U.S.
Former Vice President Mike Pence: China is the Greatest Threat to the United States
In a recent interview with CNN’s Jake Tapper on “State of the Union,” former Vice President Mike Pence expressed his strong belief that communist China poses the biggest economic and strategic threat to the United States in the 21st century.
Pence’s remarks were in response to former South Carolina Governor Nikki Haley’s statement that she considers China an enemy of the U.S.
“China is the greatest economic and strategic threat facing the United States in the 21st century. And I was proud to lead during our administration on changing our national policy toward China,”
Pence emphasized the need to address China’s trade abuses, intellectual property theft, military provocations, and human rights violations against Muslim Uyghurs and Christian pastors. He highlighted the actions taken during his tenure, including imposing $250 billion in tariffs on China.
“We sent a message to China… that enough was enough. We put $250 billion in tariffs on China. And while the Biden administration hasn’t undone those tariffs yet, to be honest with you, we should have been increasing the pressure as we see even more aggressive behavior by China.”
Pence concluded by stating that if he were to become president, he would confront China with American strength, limiting their access to the U.S. economy until they abide by international trade rules. He also emphasized the importance of building a powerful Navy to surpass China’s naval capabilities in the Asia-Pacific region.
“We’re going to meet this moment with American strength… peace and prosperity comes through strength.”
Watch the interview below:
What is the purpose of an activation function in a neural network and why is it necessary?
A neural network is a model inspired by the human brain that is used to process and understand complex patterns and relationships in data. It consists of a network of interconnected artificial neurons known as perceptrons or nodes.
Each perceptron takes in multiple inputs and performs a simple computation on them, such as taking a weighted sum. The output of the perceptron is then passed through an activation function, which introduces non-linearity to the network. This non-linearity allows the neural network to learn and represent complex patterns in the data.
Neural networks learn by adjusting the weights and biases of the perceptrons through a process known as training. During training, the network is presented with a set of input data, and the output of the network is compared to the desired output. The difference between the actual and desired output is used to calculate a loss or error, and this error is backpropagated through the network to update the weights and biases.
The goal of training a neural network is to minimize the loss or error by finding the optimal weights and biases that produce the most accurate predictions. Once the network is trained, it can be used to make predictions on new, unseen data by feeding the input through the network and obtaining the output.
Neural networks have been successfully applied to a wide range of tasks such as image classification, speech recognition, natural language processing, and even playing games. With the recent advancements in computing power and data availability, neural networks have become increasingly powerful and are widely used in various industries.
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