Introduction to Machine Learning

Introduction

In 1955, John McCarthy and his teammates introduced the idea that computers would learn how to execute certain tasks by leveraging specific algorithms. In accordance with this approach, the term that John used was “Artificial Intelligence”. The computers would collect information from the environment and make decisions.

At first, the focus was on the abstract side of things — symbolic AI that tried to provide machines with abstract thinking. But nowadays the term we hear the most is Machine Learning. Arthur Samuel was the first who introduced the definition “machine learning” as a description of his work with computer checkers in 1959. This concept really took off in the past few decades when computers became powerful enough. Now the research facilities like OpenAI and DeepMind present groundbreaking advances almost once a week.

Today ML, due to the popularity of the technology, has become similar to Artificial Intelligence. What’s the difference?

Difference Between AI and Machine Learning Techniques

Let’s start with Artificial Intelligence. It will be a mistake to think that AI is a system. No, it’s a part of a system. There are many definitions. We can call AI the study of how to make computers perform things that humans can do better at the moment. It’s a way to simulate human intelligence processes by computers.

The definition of Machine Learning could sound like: it is a study where computers could teach themselves without being heavily programmed by humans. It could be viewed as an implementation of AI that enables the machines to evolve and adjust based on gained experience.

So, what exactly are the key differences between AI and ML?:

Artificial Intelligence:

• The goals of AI are to collect and apply knowledge.
• The focus is to get a better chance of success, but not the accuracy.
• AI functions as a program for smart work.
• The whole point is to make a simulation of natural intelligence to solve sophisticated problems.
• AI is for making decisions.
• AI results in creating a system for mimicking human response and behavior.
• AI works to find a solution that is optimal for the situation.
• AI helps one gain intelligence or wisdom.

Machine Learning:

• The goals of ML are to gain knowledge and skills.
• The focus is on improving accuracy, but not the final result.
• A computer collects data and learns from it.
• The focus is on processing information for some purpose and achieving the best performance the machine allows.
• ML enables systems to learn new things from information.
• ML builds new self-learning algorithms for the system.
• ML will choose one solution, it doesn’t matter whether it’s optimal or not.
• ML leads to obtaining knowledge.

Deep Learning vs Machine Learning

What is Deep Learning? It’s a subdivision of Machine LearningL. DL leverages ML algorithms and neural networks to make a simulation on how a human makes a decision. Being really costly, it requires massive amounts of datasets. The reason is in the astonishing amount of nuances that are needed to be understood. For example, a Deep Learning algorithm could be ordered to understand what a dog looks like. It needs huge amounts of images to distinguish small details and the difference between a dog and other animals, for example, a bear, a cat, or a monkey.

Machine Learning Basics

ML technology uses statistics to pick out patterns in enormous amounts of information. Basically, everything that can be converted to digital information can be processed using a Machine Learning algorithm — numbers, pictures, clicks, and so on.

Machine Learning is one of the major technologies now. It enables Netflix, Spotify, and YouTube to provide recommendations. Among other search engines, Google and Baidu function using ML. There are many more examples, like the feeds of various social media (such as Twitter and Facebook), or voice assistant applications, like Siri and Alexa. In these cases, the platform collects data about you — what you are watching, what links you click, what posts you comment — and uses an algorithm to make a guess about what you might be interested in. Voice assistants do the same, but with the sounds that people produce.

We can highlight the following types of Machine Learning models:

Supervised Learning
This method teaches computers by example. A system could be fed a large amount of marked data, like figures that are written by hand with an annotation about which number they represent. Given enough correct examples, this type of learning would understand shapes, pixels, and clusters and distinguish them as handwritten numbers.

Unsupervised Learning
This method gives the power to algorithms to detect the patterns in information, categorizing it according to the similarities it contains. Two illustrations of this approach are Airbnb that groups houses that are available for rent by a specific region and Google News that groups similar reports.

Semi-supervised Learning
This approach combines both previous methods. It relies on a small amount of labeled information and a huge amount of unlabeled information. A ML model is trained on labeled data and then tries to label the unlabelled data — this process is called pseudo-labeling. After that, the model trains itself on data resulting from this mix. An example of this approach is Generative Adversarial Networks (GANs) that create new cats based on images of existing ones.

Reinforcement Learning

It’s easy to understand the essence of this approach by imagining someone is playing an old computer game for the first time — the controls and rules are unfamiliar, but by trial and error, the gamer’s performance in the game will improve. Google DeepMind’s Deep Q-network is a great example of this. It has beaten human gamers at a variety of vintage video games — Video Pinball, Boxing, Breakout, Star Gunner, and others. However, the network’s performance in Ms. Pac-Man, Asteroids, and Seaquest is not that good at the moment.

Benefits for Businesses

Faster Decision-Making
ML algorithms can help you automate and prioritize decisions for your business process. They can also alert you on opportunities and actions that must be taken fast so that you can achieve better business results.

Adaptability
Thanks to processing information in real-time you can adjust your processes quickly. Just like a self-driving vehicle that stops before an accident, you can avoid business failures.

Innovation
Using advanced ML models results in a higher level of automation. This change can help you introduce entirely new business models, services and products.

Predictive Insights
Due to the capability of ML to work with large amounts of complex streaming information, it can help you get insights beyond human capability and launch appropriate action patterns.

Maximized Efficiency
Business processes powered by ML algorithms could drastically boost the efficiency of your business. This can be achieved through accurate forecasts, automated tasks, cost reductions, and elimination of human intervention.

Driving Better Outcomes
Accurate prediction of results in your decision-making and use of smart action triggers ensure much better business outcomes.

Top Industries

A lot of businesses are ready for ML. Here are the main industries that are benefiting from it:

Manufacturing

This industry gathers the gigantic amount of information from sensors on the production through IoT devices. This is perfect for ML! Anomaly detection, computer vision, the control of quality, demand prediction are just a few fields that ML is involved in.

Finance

This industry is perfectly suited for ML technology. The algorithms can be used for stock trading, fraud detection, risk assessment, and insurance. There are also chatbots that guide customers and portfolio alignment for the goals of clients.

Healthcare

Applications of Machine Learning in Healthcare could outdo any team of doctors in processing patient information and spotting certain patterns in it. ML can detect cancer at early stages, analyze medical images, execute robot-assisted surgery, and develop new drugs. The opportunities here are truly endless.

Automotive
ML will revolutionize the automotive industry in the coming years. Algorithms already make possible the perception and decision making for self-driving vehicles, but there are still a lot more to discover.

Conclusion

Yes, ML isn’t a new technology, but it has become a global trend — today researchers have enough access to information to train their models. More computing power is than ever is available, so the opportunities are limitless. If you are interested in Machine Learning development or have any questions, feel free to contact us!