First, we preview a simple programming problem as an introduction. The program can easily easy to write using conventional programming techniques. In other words, you should provide several samples to do done through a learning procedure. After that, we introduce machine learning application in smart cities, medical, modeling, and stock trading.
There are a lot of problems that you can’t solve them without machine learning techniques (easily). For example, suppose a program for face detection purpose. For a 32x32 pixel with 8 bits of depth (grayscale image), there are 32x32x28 = 262,144 different probabilities. Now, suppose a 2MP color image! You may think that you can find the curvature of a face but you find it is also hard to define it. Here machine learning comes to solve the problem. You should prepare several faces and non-face images and write tens lines of code (of course with help of a library such as OpenCV). I should note that do not imagine machine learning as magic to solve everything. It has its pros and cons also.
Today, thanks to many advances in algorithms, electronics, and a huge amount of data expanded over the Internet, developing AI programs are more available. Machine learning techniques are used in finding networking attacks, face detection, spam detection, medicine discovery, law enforcement, and even finding new types of algorithms. Recently a new type of machine learning algorithm can generate and invent something new. For example, finding a new formula for medicines, generating a new face, generating new models of clothes.
By growing population, customary or old approaches are not good enough to handle traffic jams, car pollutions, long queues of people registering for events, etc. How many police officer does it need to do law enforcement in roads? How many workers do you need to manage public parking? To cope with such problems you can use ML. For example, a license plate detection program can be a solution for the problems. ITS (intelligent transportation system) is a solution for car-based problems such as traffic jams, emissions, transportations cost, etc.
Fig... depicts the architecture of hierarchical and networked vehicle surveillance which can be used in ITS. As it can be seen image acquisition in the first layer is a base for electronic toll collection, security monitoring, etc in the 4th layer. In the second layer, we must detect, recognize, or track cars. One property of each car that you can perform detection, recognition, and tracking is license plate. because of this, license plate detection plays a key role in ITS and is a trade in many ML journals, especially image processing related ones.
Over the past decade, there has been a remarkable increase in the amount of available compound activity and biomedical data owing to the emergence of new experimental techniques. ML can help to test a simulated drug on a simulated disease. For example, chemistry experts can simulate behaviors of a virus using ML and also test a simulated drug on the virus. This process also helps drug specialization for different patients leads to better effectiveness and lower side effects. Medical experts can even predict side effects by ML [https://healthitanalytics.com/news/machine-learning-method-identifies-adverse-drug-side-effects]
In the field of medical imaging, ML helps doctors to find tumors easily, especially when they are tired. Also in many areas, expert doctors are not available at all times. Using a ML program detecting tumors may help people, at least temporarily. In radiology facilities, ML helps to use less radioactive material using image enhancement techniques or you can use less power of x-rays for imaging.
Fig... shows an example ML for image enhancements. Part A in Fig... depicts an unenhanced x-ray image. It can be realized that is matt while enhanced imaged in part B is more pleasant in our eyes. We can see details in B better than A.
Many people think that AI or ML applications are limited to classification or detecting something. AI applications are far away from detecting an object, tracking them, or classification. Many approaches of the ML help decide business action. Recently, a new type of ML tool lets you generate something new. For example, you can feed a lot of videos as training samples by caption and then ask a ML program for a new video by a caption. Another example is modeling that you can generate new models.
Part of A of Fig... depicts several input image samples. A ML model (generative adversarial network) can be trained using such images. Part B is a possible output of such networks. As it can be seen there are all new models. So we can see a limited amount of innovation here.
Many have seen time-machine-related movies. Imagine you can travel to a future time and see what will be happed to stock prices! ML is a tool for time travel! To be more precise you extract a model behavior of stocks. Such problems are known as time-series problems, simply because the price varies according to time. you can suppose it as a regression problem.
Fig... depicts a simple prediction using an AI model. As you can see it is not accurate in terms of predicting details. If you need to predict more details you need complex models and more data. for example, you can predict better with more years or knowledge about fundamental events in bitcoin usage.