When chatting on a website, using an online translator, or editing photos online, we always face the results of machine learning. Machine learning (ML) is an area of artificial intelligence allowing computers to learn without programming. The very idea behind the technology is that analytic systems can learn to identify patterns and make decisions with minimal human involvement.
What is machine learning?
ML helps artificial intelligence simulate human behavior. Artificial intelligence systems are used to handle complex tasks in the same way as humans do – machines recognize visual scenes, understand text, or perform some action in the physical world.
Machine learning empowers computers to learn without explicit programming. Traditional programming can be compared to baking – you have a recipe that requires precise amounts of ingredients, and certain cooking conditions to be met. The same is with programming – it also involves creating detailed instructions for the computer.
However, in some cases writing a program for a machine is too difficult and time-consuming or even impossible at all. For example, one cannot write a program that allows a computer to recognize images of different people. Humans, on the other hand, can do this easily. ML takes the approach whereby computers can learn to program themselves based on experience.
Machine learning is all about data – numbers, words, images, videos, speech. The more data, the more efficient is machine learning and the more accurate is the future result. Data is collected and prepared to be used as raw information to be trained by the machine learning model.
Next, programmers choose the model and provide data. The computer model trains itself to find patterns. To get more accurate data programmers can customize models, change their parameters, etc.
Thus, the machine learning process is divided into three main stages:
- data collection and processing
- training and evaluation of the model
- using the trained model.
For performing each of these steps, we use specialized platforms. They differ in a programming language (Python, Cython, C, C++, CUDA, Java), operating systems (Linux, Mac OS, Windows), and the type of tasks they can be applied to.
The learning process requires a significant amount of computing resources.
Machine learning types
The types of machine learning training methods are divided into three subcategories:
- Supervised learning
- Unsupervised learning
- Reinforcement learning.
Supervised ML is based on examples. The data prepared for analysis initially contains the correct answer, so the goal of the algorithm is not to answer, but to understand the reason why exactly so. The algorithm identifies patterns in the data, trains from observing, and makes predictions. The operator then corrects these predictions. The process proceeds until the algorithm reaches a high level of accuracy/performance.
Unsupervised learning. In this case, machine learning algorithms explore the data to identify patterns. The program determines correlations and relationships based on an analysis of the available data. Unsupervised learning involves the machine learning algorithm independently interpreting large data sets and drawing conclusions from them. The algorithm arranges data and describes its structure. This may look like grouping the data into clusters or making it systematic.
NLP (Natural Language Processing) is a field in ML dealing with the recognition, generation, and processing of oral and written speech. Through NLP, computers can read, interpret, understand human language, and produce responses. Typically, the processing is based on the intelligence level of the machine decoding human messages into meaningful information. NLP applications are all around us – chatbots, virtual assistants like Siri, Google or Yandex search, machine translation, and many others.
Neural networks are a specific class of machine learning algorithms. Neural networks simulate the work of the human brain, consisting of neurons constantly forming new links with each other. They can be defined as a network with many inputs and one output. The AI collects data from all inputs, evaluating their weight according to specified parameters, then performs the desired action and provides the result. At first, it is random, but eventually, through multiple cycles, it becomes more and more accurate. A well-trained neural network works like a normal algorithm or more accurately.
Deep Learning is a subfield of machine learning involving multilayer neural networks that self-train on a large data set. Artificial intelligence with deep learning finds the algorithm for solving the original problem, trains on its mistakes, and provides a more accurate result after each training iteration. Deep learning is used in computer vision, machine translation, and speech recognition. One of the most essential hardware for developing a deep learning model is the GPU.Cloud4Y offers GPU-accelerated cloud server for deep learning.
How businesses are using machine learning
Machine learning is at the core of many business models. The scope of its application is very wide and keeps growing.
Recommendation algorithms. The recommendation mechanisms behind suggestions on social networks, YouTube, and other platforms are powered by machine learning.
Image analysis and object detection. ML can analyze images to get different kinds of information, such as learning to identify people and distinguish them.
Fraud detection. Machines analyze patterns to understand a person’s behavior, for example, how he/she usually spends money, and detect fraudulent transactions if action is unusual.
Voice assistants or chatbots. Implementing chatbots helps to save money on human operators. The algorithms use machine learning and natural language processing, and the bots learn from previous conversations to generate better responses.
Medical visualization and diagnostics. Machine learning is becoming more and more common in healthcare. Algorithms cannot replace doctors, but they do help them with routine tasks. Programs can be taught to analyze medical images or other information and look for certain markers of disease.
Challenges in adopting machine learning
When applying ML to business, it is important to understand its weak points. First, one cannot really know exactly how the models are making decisions. This is important because systems have the potential to be fooled or simply fail to perform certain tasks, even those that seem very simple to humans. For example, adjusting the metadata in images can confuse computers – after a few adjustments, the machine identifies the image of a dog as an ostrich.
In addition, machines are trained by humans, and human biases can be incorporated into algorithms. In some cases, models create or intensify social problems. For example, social media content based on machine learning and containing extreme content might lead to polarization and the spread of conspiracy theories.
To deal with bias, rigorous verification of training data is essential, as well as organizational support for ethical efforts in artificial intelligence.
Machine learning can truly change every industry, but it is important to remember not only its possibilities but also its limitations.
