AI is a new ﬁeld that appeared a few decades ago and has seen unprecedented growth. The AI approach has been used in numerous ﬁelds, such as ﬁnance, medicine, music, customer service, and transportation. Some of the big challenges we have to cope with are the computing power and lack of documentation. Big companies such as Google and Apple do not share advances in this ﬁeld, the secretism characterizes this scientiﬁc ﬁeld and thus the information we can get is very restrictive. In this work, we introduce a model of an autonomous car and then examine diﬀerent algorithms capable of driving the car in a simulated. We also discuss the diﬃculties we need to deal with such as local minimums, losing diversity, ﬁtness functions, localization, performance, and future applications.
Magnetic and Electrosatatic simulator in UE4 and C++
In physics (particularly in electromagnetism) the Lorentz force (or electromagnetic force) is the combination of electric and magnetic force on a point charge due to electromagnetic fields. A particle of charge q moving with a velocity in an electric field and a magnetic field experiences a force.
I have used C++ in this project, the program attempts to predict updated stock market data. I have used a neural network library created by myself to train our model an used it to predict Stock market data.
In this video, I show you a real-time simulation of space-time distortions that produce gravity because of its mass. The simulator is made in Unreal Engine 4 and C++.
In 1905, Albert Einstein determined that the laws of physics are the same for all non-accelerating observers and that the speed of light in a vacuum was independent of the motion of all observers. This was the theory of special relativity. It introduced a new framework for all of physics and proposed new concepts of space and time.
Einstein then spent 10 years trying to include acceleration in the theory and published his theory of general relativity in 1915. In it, he determined that massive objects cause a distortion in space-time, which is felt as gravity.
Our neural network implementation in Unreal Engine 4 can have different purposes, educational or as a tool for data scientist developers.
This tool is really easy to be used: you can change the number of layers and the neurons per layer. In the example showed I have used the MNIST dataset for number recognition, furthermore, I can use different datasets.
If you want to use this tool for educational purposes, you can debug and see how the neural networks work. It is really practical and I recommend whoever wants to start AI. This application is progress, although you would be able to see the advances.