Artificial intelligence has to be a really intelligent technology. The world has literally lost count of the number of applications that it has. Not the least it will now be studied as an independent branch of engineering. A topic once is now a huge science by itself. AI has many parts and sub-parts. And one of it is the Generative Adversarial Network that is GAN.
What is GAN?
GAN or the Generative Adversarial Network is a class of artificial intelligence algorithm. It is an application for unsupervised machine learning. The Generative Adversarial Network works with two neural networks. These two neural networks exist in a zero-sum game framework. Zero-sum game framework means the success and failure of one network are perfectly balanced by the other network.
GAN differs from a general neural network because it not only recognizes but also generates examples to learn from. It studies these generated examples and produces outstanding results.
How does GAN work?
GAN works on two neural networks. The first network generates a particular instance. And the second one evaluates the instance. The first network learns from the true data set. It then generates instances that appear to be from the true dataset. What the second network does is it discriminates these instances to be from the true dataset and the one that isn’t. It learns from these instances that it receives.
The discriminator network is initially trained with a true dataset. Once it acquires some level of accuracy it is then subjected to the instances prepared by the first network.
GAN in Biomedicine.
GAN is used for the research of drug discovery. Mainly to study and prepare drugs. Drug discovery is a complex task. It involves moving and targeting parts and also manipulating these tasks to make it a remedy. It is a highly important task as it can cause the effect to human health.
GAN is used for finding the root cause of a disease and also making molecules. It can do these things much quicker as compared to the traditional techniques. GAN allows the researchers to see the chemical particle that does not exist in chemical space. This will help in easy experimentation and easy discovery of medicines.
GAN is basically AI’s Imagination. Definitely, it is imagination but based on statistics, research and previous knowledge. GAN is a well-controlled and predictive imagination of AI. GAN uses Deep learning hardware, AI techniques and the neural networks, due to this the accuracy of GAN increases to about 94% as compared to the traditional neural network that has an efficiency of 75%.
Limitations of GAN
The Generative Adversarial Network works on a simple principle. The first neural network recognizes particular instances and creates examples. The second neural network studies them and makes recognition algorithm. The problem with GAN here is there is no much computation. But it requires a lot of memory.
GAN i.e. the Generative Adversarial Network isn’t in use much yet. But the time isn’t far when GAN will be in use everywhere. GAN is here to make the work of AI easier and to increase the accuracy of the artificially intelligent systems. After all artificial intelligence is in competition with humans and no matter how error-prone humans are they will never admit it. And will strive to make a system that can give almost complete accuracy. GAN is a step to this 100% needed accuracy.