Machine Learning Applications Areas in Daily Life
Here are the some examples of artificial intelligence(ai) and machine learning (ml) applications.
Deep Blue is one of the most important milestones in the AI history. Deep Blue was a chess-playing computer developed by IBM. It is known that the first computer chess-playing system wins the chess match against a reigning world champion. Deep Blue won its first game against a world champion Garry Kasparov on February 10, 1996. However, Kasparov won three and drew two of the following five games, defeating Deep Blue by a score of 4–2. Deep Blue was then heavily upgraded and played Kasparov again in May 1997. Deep Blue won game six, therefore winning the six-game rematch 3½–2½ and becoming the first computer system to defeat a reigning world champion in a match under standard chess tournament time controls. (Deep Blue has given right.)
Chess was thought to be a game of intelligence. Playing chess well is a very hard task even for humans. Because of this, the first chess match winning by a computer against to world champion was talked about too much in those years.
How it could be possible? Let’s take a deeper look.
The Shannon number is a conservative lower bound (not an estimate) of the game-tree complexity of chess of 10120, based on an average of about 103possibilities for a pair of moves consisting of a move for White followed by one for Black, and a typical game lasting about 40 such pairs of moves. Shannon calculated it to demonstrate the impracticality of solving chess by brute force, in his 1950 paper “Programming a Computer for Playing Chess”. 10120number is very huge, as comparison total number of atoms in the universe are estimated between 1079– 1081. If a variation calculating takes 1 microsecond, calculating every variation takes 1090years. For achieving this situation, computers need a lot of processor capacity. Since those days technology did not allow it, therefore the depth of tree which is calculated is limited.
As we have explained before, AI focus on creating a system which works similar to human brain. Due to some reasons, AI can be applied to some specific application area. Chess has been one of the successfully applied artificial intelligence area to a field of practice.
There is some other mind game about the challenge of artificial intelligence. Its name is GO. GO which was invented in china is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent. In the years when deep blue defeat Kasparov, some people are considered that humans can not be defeated by a computer in GO or it takes very very long time. Almost twenty years after 1996, AI has defeated humans in the GO. Let’s take a look at the numbers about GO. Initially, GO table size is changeable, so people can play GO with table size 7×7, 9×9, 19×19 or 21×21. In our example, we think we want to play GO with table size 19×19 against to computer. And assume that average move count is about 200 in the game of experts. (Because researchers show so.) Average choice count for every move is about 250. The total number of variations that must be calculated by the computer is 3 × 10511 when table size is 19×19. This number is much more than that of chess. (Do not forget total count of all atoms in the universe are between 1079– 1081.) In professional games, overall move counts can take 350. The total number of variations that must be calculated by the computer for this move count is 1.3 × 10895 You get the idea why this problem is so hard to solve. (If you want to analyze number about this topic check this link).
Humans are defeated by artificial intelligence application with name ALPHA GO in GO game. AlphaGo is a computer program developed by Google DeepMind in London to play the board game Go. In October 2015, it became the first Computer Go program to beat a professional human Go player without handicaps on a full-sized 19×19 board. In March 2016, it beats Lee Sedol (best player in the world ) in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicaps. This is the one other most important milestone in AI history. Alpha Go was trained with deep learning. How this success can achieve using a program which has trained by deep learning is explained in the following sections.
The two examples which are explained above focus on defeat human in areas that require intelligence. But most of AI applications focus on support human instead of defeating them. This type of applications uses machine learning technique to learn specific problem to support people. Every person in the world uses many applications which have been developed using machine learning in their daily life consciously or unconsciously. It is given that some examples of these type of applications below.
Recommendation Systems are one of the well-known machine learning topics in the literature and business sector. Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. The suggestions which has provided are aimed at offering something to users in various decision-making processes, such as what items to buy, what music to listen, what movie to watch or what news to read. Recommender systems have proven to be valuable means for online users to cope with information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed and during the last decade, many of them have also been successfully deployed in commercial environments.
The interesting thing is that system calculate information about items and estimate approximately how much a user will vote on an item not seen before. Amazon recommends a book or some other products that the user probably likes. Facebook shows advertisements, recommends friendship relations or some events that the user probably likes. Youtube recommends a video to a user and Spotify recommends music to the user. There are countless examples on this subject.
Recommendation systems are used widespread across the globe. According to a report published by NetFlix in 2014, ⅔ of movies watched at NetFlix are watched as a result of the recommendation. Recommendations generate 38% more click through for Google News. Similarly, 35% of amazon sales are made through suggestion systems. Youtube and some other firms are using recommendation systems strongly. Recently recommendation systems are developed using deep learning. Big companies such as Youtube and Facebook benefit from the power of deep learning in large quantities.
Another well-known machine learning application area is the activity recognition. The main purpose of this type of application is detecting which activity performed by the user at the certain time. This process can be done on the mobile phone or some external devices such as the smartwatch. Big mobile phone producers research on this topic heavily. Such big companies Apple and Samsung has a mobile application for activity recognition which is one of the default application for their phones. For the develop intelligence system for activity recognition, it is needed pieces of information which are produced by sensors. Accelerometer, gyroscope and GPS sensors are most commonly used sensors in this area. It is used machine learning techniques to detect which activity performed by the user. This type of applications can give us information about burned calorie, how many kilometers walked or how healthy the user’s daily life is.
Machine learning can also be used for the prediction about future. For example, in weather forecasting applications current weather data and past data processed and gathering information about future weather conditions. Another example of prediction is atm cache optimization. The money which is located on atm is not useful for a bank when that money is not being used by customers. In this situation money neither useful for customer nor bank. If it is developed an intelligent system
to predict optimum money for atm weekly or monthly, banks can use that money for other purposes. In a recent study, banks can double the number of ATMs without changing the total amount of money in overall ATM’s using an intelligent system that estimates the optimum amount of money in ATMs. Some other example is house price prediction. In this type of problem, the system tries to predict the actual value for house using information about the house, house location, knowledge of nearby transportation vehicles or land value like information. There are so many other examples of forecasting about future.
Image processing is one of the most frequently used field of machine learning techniques. In imaging science, image processing is processing of images using mathematical operations by using any form of signal processing techniques. Inputs may be an image, a series of images, or a video, such as a photograph or a video frame. The output of image processing may be either an image or a set of characteristics or parameters related to the image. Some examples of
image processing using machine learning techniques are; face recognition, fingerprint recognition, moving object recognition, information retrieval from image or medical applications. Moving object recognition is widely used in the military purposes or traffic intensity detection like applications. There are a lot of studies which has gained high accuracy rate using deep learning technique in this field. Machine learning can also be used for text based applications like language translate in real time, detect the main idea about an article etc.
Another trending topic of machine learning area is Autonomous Car. An autonomous car (driverless car, self-driving car, robotic car) is a vehicle that is capable of sensing its environment and navigating without human input. Autonomous cars can detect surroundings using a variety of techniques such as radar, lidar, GPS, odometry, and computer vision. Google’s self-driving car is an autonomous car project. For creating an autonomous car, the system must be equipped with a strong artificial intelligence. (Image source: https://waymo.com/ )
The project started in 2009 and completed in 2015. This project completed its first driverless ride on public roads. This project is testing in Austin Texas now. In December 2016, Google transitioned the project into a new company called Waymo, housed under Google’s parent company Alphabet. Alphabet
describes Waymo as “a self-driving tech company with a mission to make it safe and easy for people and things to move around.” The new company plans to make self-driving cars available to the public in 2020 (image sourceMcKinsey & Company). Google’s self-drive car designed for autonomous driving, so this car has no pedal or steering wheels in it. All processes are doing with sensor input.
There is no directly input from the human. Sensor inputs are processing by machine learning techniques. Google is not only autonomous car producer in the sector. Many of the big companies in the automobile industry are doing research on driverless cars.
