What is machine learning; To learn a skill, we first acquire the necessary knowledge, practice it carefully, and monitor our performance during the process. Eventually, by doing these things, we get better at it. Machine learning is one of the most important branches of artificial intelligence, which basically makes such a process possible for computers. In the following, we will discuss this technology and its importance.
What is machine learning
Can computers learn?
Defining intelligence is hard work. We all know what we mean by intelligence, but it is not easy to describe it accurately. Regardless of emotion and self-awareness, an accurate description of intelligence can be the ability to learn new skills, absorb knowledge, and apply them to new situations to achieve the desired result.
Given the difficulty of defining intelligence, the exact definition of artificial intelligence will not be easy. Simply put, if a computing device is able to do something that usually requires human reasoning and intelligence, we say it uses artificial intelligence.
For example, smart speakers like Amazon Echo and Google Nest can hear our verbal commands, interpret sounds as words, extract the meaning of words, and ultimately try to meet our needs. These include requests such as playing music, answering a question, or turning off lights.
In all interactions, except for very simple things, these requests are transmitted to the computers in the cloud, which perform the most heavy-duty AI processing. The voice command is decomposed, the meaning is extracted, the answer is prepared, and finally this information finds its way to the smart speaker.
Machine learning is the basis of most AI systems we interact with. Some of these systems are in the form of smart gadgets in the home, and others are part of the services we use online. The suggestions that YouTube offers for playing new videos and the playlists that are automatically created on Spotify are based on machine learning. Search engines use machine learning, and online stores use the technology to offer new purchases based on user activity.
Computers can access a large set of data. They can do things that are difficult and time consuming for humans very quickly and often. So if learning requires knowledge, practice and performance feedback, then the computer should be an ideal candidate.
Of course, this does not mean that the computer can really think in a human sense or that it has the same understanding and thinking as humans. But he can learn and he gets better with practice. Overall, what challenges do engineers have to overcome in order to be able to teach new concepts to computers?
Animal brains contain a network of neurons. Neurons can transmit signals to other neurons via synapses. This small action, repeated millions of times, creates our thought processes and memories.
Inspired by biological neural networks, artificial neural networks were created to mimic some of the characteristics of their organic counterparts. Hardware and software have been developed since the 1940s that include thousands or millions of “nodes.” These nodes, like neurons, receive signals from other nodes. They can also generate signals to present to other nodes. These nodes are able to send and receive signals from multiple nodes simultaneously.
If an animal concludes that flying yellow and black insects always bite painfully, that animal will avoid all flying yellow and black insects. Flower flies (a type of bee) use this fear. This insect is like a yellow and black honey bee but has no stings. Animals that have been exposed to bees and have learned a painful lesson no longer have anything to do with flower flies. They see a yellow and black insect and decide it is time to retreat. But the fact that flower flies can stay afloat in a certain range in the air is not even taken into account; But bees can not do without honey.
The importance of flight and the black runway overshadows everything else. The importance of these signals is called “weighting” that information. Artificial neural networks can also use weighting. A node should not consider all of its inputs equally and should prefer some signals over others.
Machine learning uses statistics to find patterns in the datasets on which it is trained. A dataset may include words, numbers, images, user interactions such as website clicks, or anything else that can be stored and stored digitally. The system must identify the essential parts of the information received and then match them to the patterns it identifies in the dataset.
If a system wants to identify a flower, it must identify the length of the stem, the size and shape of the leaf, the color, the number of petals, and so on. Of course, in reality, the system needs much more information. Once the system obtains the details of the sample, it begins the decision-making process, resulting in the loss of available data.
A machine learning system learns from the mistakes made by updating its algorithms to be able to correct problems with its reasoning. The most complex types of neural networks are deep neural networks. Conceptually, these systems are made up of a large number of neural networks stacked on top of each other. The same process gives the system the ability to identify and use even small patterns in its decision-making processes.
These layers are commonly used to weigh information. The so-called hidden layers can act as “expert” layers. They provide weighted signals about a single feature of the test subject. In the case of flowers, for example, hidden layers may be used to identify the shape of the leaves or the size of the buds.
Different types of learning
There are three main techniques for teaching machine learning systems; Supervised learning, unsupervised learning and reinforcement learning.
Supervised learning is the most common way to teach systems. In supervised learning, data is labeled and structured before presentation to the system to define the criteria used in the decision-making process for the machine learning system. For example, YouTube uses this method to suggest new videos to users.
Learning without supervision
Learning without supervision does not require data preparation. This means that the data is not pre-tagged. The system scans the data and identifies patterns. Unsupervised learning techniques have been used to ensure the success of cyberspace. Machine-augmented intrusion detection systems can detect the activity of unauthorized networks because they do not conform to the patterns of behavior observed by authorized users.
Reinforcement learning is younger than the other two methods. Simply put, an augmentation learning algorithm uses trial and error and feedback to achieve an optimal model of behavior in order to achieve a specific goal. This requires feedback from individuals who rate the system’s efforts for the positive or negative impact of its behavior on achieving the goal.
The practical aspect of artificial intelligence
Because of the many applications that machine learning has found in various fields, it is referred to as the “applied aspect of artificial intelligence.” Machine-based learning systems are highly valued, and many commercial frameworks have been developed to take advantage of this technology.
If you do not have an urgent need for this technology and you want to learn more about machine learning using programming languages like Python, there are some great free resources available. For example, we can refer to the Torch open source framework and a set of tools provided on the Scikit-Learn site that are compatible with the Python programming language. You can use the Caffe framework for image processing, and Keras is another Python-based machine learning framework.