What was considered to be fiction not so long ago, now is a reality. The technology that only could be seen in movies and read in books is currently a reality we live in. While some of the greatest minds only could’ve dreamed in the past about what is machine learning and what it could bring to humanity, the phenomenon is very much alive.
Machine learning, or shortened as ML, is a computer science term standing for machine intelligence. It is a technology that can learn and mimic cognitive functions such as neurons. It can solve problems on its own and not just answer questions like a virtual assistant.
With the rise of machines' capability to improve people’s lives, we can already notice machine learning software in some parts like face recognition, self-driving cars, social networking, and auto-pilots in planes. As the Tesler’s Theorem says “ML is whatever hasn’t been done yet”. Machine intelligence capabilities that are classified as ML can successfully understand human speech, military simulations, competing at the highest level of computer games, and more.
If you want a practical guide into machine learning, feel free to check out our guide to the DataCamp machine learning courses here. And now that we have tasted a bit of what is machine learning, let’s dive deeper, shall we?
Table of Contents
A deeper insight into Machine Learning Technology
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From the virtual assistants like Siri and Alexa, machine learning software is rapidly integrating into our daily lives. Although some of these examples could not be considered as the “true” machine intelligence that can make decisions on its own, the spin-off projects’ impact continues to advance in capability and prevalence.
To have a better understanding of what is ML, it is needed to go back a little over its development.
A short history of ML
The very first ideas of artificial beings were mentioned in antiques and have been in the fiction scene for a very long time. Stories like Frankenstein were the results of it. The field of artificial intelligence studies was born in 1956, at Dartmouth College in the United States. A group of scientists from universities like MIT and CMU became the founders of ML technology research. The programs they created have been considered as the first machine learning basics. They were the ones to create a computer system that could learn checkers' strategies, solve problems in algebra, and prove logical theorems. They believed that in 20 years or so the machines will be capable to do anything that a man can do.
Although they were very optimistic about the progress of their creation, they failed to realize what is machine learning development going to challenge them next. Because of the hard financial times, both the United States and British governments decided to stop funding the projects on the exploratory research of ML. The period, in which it was very hard to find enough funds to continue the research was called “ML winter”.
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Nonetheless, the “ML winter” did not last too long. By 1985, the research was alive again and by that time, the market of machine learning reached over a billion dollars. Through stumbles and falls, by the end of 20th, and by the beginning of the 21st century, machine intelligence development has been used in medical diagnosis, logistics, data mining, etc. Machine learning software started gaining success because of increasing computational power. As Moore’s law states, the speed, and capability of computers can be expected to double every two years. That means that the evolution of computer science progresses unquestionably fast and it will continue to increase the quality of people’s work accordingly.
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The basic concept of ML
Machine learning as a process and as a product is very hard to understand if it’s not in your expertise. To make it as simple as possible, ML technology is a software, that takes input information and turns it into other information, that is output.
The biggest difference between machine intelligence and other kinds of software programs is that to machine intelligence, the creator, that is a programmer, did not have to give instructions on every feature that it is doing. Through examples and practices, it learns the needed information by itself.
Why is machine learning important?
Understanding what is machine learning and its’ importance has to begin with the very simple statement - it was created to reduce human effort and help in the areas where it is dangerous for a person to step in. Although there are many different ways of using machines’ intelligence, it works as a speed-up to some sort of a process and gives the user an accurate result. The idea of ML software is to create an error-free world. Let’s break down some of it’s main and most important features:
- Machine-learning learns through repetitive learning and discovery through data. Instead of handling the information by yourself, the ML makes robotic automation that can perform high-volume, computerized tasks without experiencing any form of tiredness and tardiness. It is worth to mention that this process still needs a human inquiry since the ML system needs to have the right questions.
- It will get the most out of the data. As mentioned above, with the right set up from an expert, ML technology can work without fatigue for a very long time. What is machine learning amazing for creating a competitive advantage against business competitors. Data collecting has grown significantly over the last years, and the importance of it has become huge. It’s no surprise that there have been many scandals and data protection regulations over this time. Everyone knows, that the data can play a big role in many work areas, and ML can make it easier to sort through it.
- Machine learning software plays a huge role in safety. By giving the ML access to data storage, it can work as a fraud detection system a lot faster with the help of deep learning.
- Using machine learning basics to improve current products. If you are familiar with digital marketing then you know that the internet of things is coming whether we like it or not. Web 3.0, the alternative name to the internet of things (IoT). The definition of IoT means that it extends the purpose of casual and everyday devices that we use. In the consumer market, the internet of things is the synonym of the things that make a “smart home”. It covers devices, appliances, security cameras, thermostats, etc.
- Deep neural networks help us achieve extreme precision. What is machine learning also astonishing about is that through deep learning, image classification, and object recognition machine intelligence can spot cancer on MRIs just as precise as an expert radiologist.
As we can see machine learning impact is undeniable in the current stage of computer science and technologies. Don’t get it wrong, it’s not all advantages of ML technology, there are much more than that. But now that we mentioned deep learning and neural networks, what exactly are they?
Theoretically, the neural network is a circuit or a network of neurons. In this case, it is an artificial neural network that helps machine learning to solve a problem. A neural network is a set of certain algorithms that have been modeled to be similar to the human brain. These algorithms are designed to recognize patterns of information.
The information is recognized through a machine perception, labeling or clustering raw input. Just like it would be real-life images, sounds or texts, artificial neural networks understand it through n-dimensional tensors(arrays) that hold the values and numbers. It is one of the most important things about what is machine learning all about.
Neural networks help to cluster and classify data. The whole process helps to group unlabeled data according to similarities among the example inputs, and neural networks classify data when have a labeled dataset to train on. This type of learning is called supervised. On the other hand, there is unsupervised learning, that helps to find previously unknown patterns in data set without pre-existing labels.
Another essential part of machine intelligence is deep learning. This process is a machine learning technique that helps them to learn from examples, just like humans do. If you have seen self-driving cars then you probably had your first contact with machine learning.
In deep learning, machine intelligence can learn to perform tasks from images, texts, sounds, like a human from books, videos or lectures. Human beings always have a chance to make a mistake, while computers with deep learning models can achieve picture-perfect accuracy and exceed human performance. Deep learning models are part of neural networks since they use the labeled data and datasets that have been collected. It is a huge part of what is machine learning.
Real-life example: Sofia the Robot
Although the name itself suggests that it is a robot, do not get tricked. The robot is what is on the outside - the skeleton of the whole project. What is most impressive about Sofia - it is her mind.
Sofia is a social humanoid robot that has been developed by a company Hanson Robotics. She was activated on February 14th in 2016.
Combined with many algorithms, Sofia the Robot can see, follow movements, sustain eye contact with its companion, and recognize people. It can even understand facial, expressions of people, and understand companions’ emotions. This whole process is done through the cameras that are in her eyes. In 2018 she was upgraded and since then, Sofia the Robot can walk.
The creator of Sofia, David Hanson, said that the goal was to create a machine learning-driven robot that could serve in healthcare, customer service, therapy or education. Sofia’s machine intelligence is constantly being trained in the lab, so she is developing new skills and making fewer errors as we speak.
Moreover, what is machine learning of Sofia so groundbreaking is that it combines cutting-edge neural networks, expert systems, machine perception, conversational natural language processing, adaptive motor control, and cognitive architecture.
Sofia the robot can function in separate ways - the first is a completely ML autonomous operation, the second is ML operation mixed with human-generated words. It is a fully functioning hybrid human-ML intelligence.
It is hard to deny that machine learning is currently the biggest cutting-edge technology out there. It is important to acknowledge that if we want to grow and continue making human lives better it is one of the best ways to do so. If you want to understand better on what is machine learning and learn more about it, head over to our BitDegree course and give it a try. If you're interested in the absolute machine learning basics, then head over to this course.
Additionally, you can check out what DataCamp has to offer. As a data science learning platform, it naturally offers some courses about machine learning in its catalog of more than 390 different choices. Our guide to machine learning on DataCamp will introduce you to the Machine Learning Fundamentals with Python skill track, as well as other courses you might be interested in checking out as you dive deeper into this field.