Case Study - Conclusion - Tesla and Artificial Intelligence

In conclusion, Tesla is the highest rated car manufacturing company for autonomous cars in the present and it continues to develop internationally and this is owing to the fact that Tesla properly uses artificial intelligence to full potential to produce its vehicles and implement it in every way possible to produce innovative self driving electric vehicles which dominated the market. This case study clearly shows the connection between the main project theme of this blog and the success of the Tesla company and further illustrates the importance of AI. The research conducted is significant to help understand ways AI can be implemented and to encourage other companies and potential entrepreneurs to implement AI with vision to popularize the use of AI and revolutionize the planet. However, because of the limitations presented by the research and the case study, further research is suggested to provide a more accurate significance of artificial intelligence, I will be proceeding by further researching into how other companies use AI to be convinced of its importance in addition to learning and horning my skills in this marvelous subject.

Comments

Neural Network (Initial Idea 2)

Neural Networks Neural Networks, colloquially known as artificial neural networks (ANNs) and also simulated neural networks (SNNs), are a sub-fiend of machine learning which in it self is a sub-field of Artificial Intelligence, they are the brain of AI and are components of deep learning. The name and structure of NNs are similar to the human brain and were inspired by it, they imitate the biological neurons by signaling to each other (IBM, 2020). ANNs consist of an input layer of neurons, one or two hidden layers of neurons and a final layer of output neurons (Wang, 2003). The diagram illustrated exemplifies a simple architecture of a neural network, the lines which connect the neurons are called weights and are associated with a number which represent the strength of connection between neurons. There are many different types of neural networks and each are used for specific motives. Below are some of the most common ones used in the field: The perceptron “t...

Computer methodology - Artificial intelligence

Diagram of a simple feed-forward artificial neural network, with one “hidden layer,” also known as a “perceptron.” Image: Wikipedia Deep learning simply means “stacked neural networks” which are networks composed of several layers (Pathmind, 2021). Based on Rosenblatt’s perceptron, the simplest version of an artificial neural network has three layers of neurons. The first is the input layer which takes input values ,which in other words is data. This first layer of neurons is connected to the “hidden” layer. The outputs of these “hidden” neurons are then connected to the final output layer. This final layer is what gives you the answer to what the network has been trained to do (Medium, 2021). The simplest neural networks consist of input layer, hidden layer and output layer, which are attached to each other with predictors, these predictors come attached with coefficients called "weights" , this is a non-linear network known as multilayer feed-foward network (Hyndman, R.J....

Computer skills and Research Sofiane Benzid

Search This Blog