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Introduction

In the 1955 John McCarthy and his teammates introduced the idea that computers would learn how to execute certain tasks leveraging specific algorithms. The term that John used was artificial intelligence. The computers would collect information from the environment and make decisions, according to this approach.

 

At first, the focus was on the abstract side of things — symbolic AI, which tried to provide machines with abstract thinking. But nowadays the term we hear the most is Machine Learning. Arthur Samuel was the first who introduce “machine learning” definition 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 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 the system. There are many definitions. We can call AI the study of how to make the computers perform things that humans can do better at the moment. It’s a way to simulate human intelligence processes by computers.

 

Machine Learning definition 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 the key differences between AI and ML are:

 

What is Artificial Intelligence:

  • The goals are to collect and apply knowledge
  • The focus is to get a better chance of success, but not the accuracy
  • It functions as a program for the smart work
  • The whole point is to make a simulation of natural intelligence to solve sophisticated problems
  • AI is for making decisions
  • It 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 to achieve intelligence or wisdom

 

What is Machine Learning process:

  • The goals are to learn knowledge and skills
  • The focus is on improving accuracy, but not a final result
  • A computer collects data and learns from it
  • Concentration on processing information of certain goal to make the best performance of a machine for this
  • ML enables systems to find out new things from the information
  • ML builds new self-learning algorithms for the system
  • ML will choose one solution, it doesn’t matter it’s optimal or not
  • ML leads to obtaining knowledge

 

 

Read more on AI/ML in IoT Solutions

 

 

Deep Learning vs Machine Learning

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

 

Data Processing

Machine Learning Basics

ML technology use statistics to pick out patterns in enormous amounts of information. Basically, everything that can be converted to digital information can be given to the advanced Machine Learning algorithm — numbers, pictures, clicks, e.c.

 

Machine Learning is one of the major technologies in the world right now. It enables Netflix, Spotify, and YouTube to provide recommendations. Google and Baidu among other search engines are functioning using it. There are much more ML examples, like feeds of different social media such as Twitter and Facebook or voice assistant application — Siri and Alexa. In these cases, a platform is gathering data about you — what you are watching, what links you click, what posts you are commenting — and using an algorithm to try to make an accurate guess what you might be interested in. Voice assistant is doing the same thing 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 big amount of marked data, like figures that a written by hand with annotation of 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 similarities in it. Here are some illustrations of this approach — Airbnb is grouping houses that could be rented by a specific region and Google News putting together similar reports. The goal is to create groups by similarities and not to pick a specific category of information.

 

Semi-supervised Learning

This approach combines both previous methods, relying on a little amount of labeled information and a huge amount of unlabeled information. ML model is trained by 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 by this mix. Example of this approach is Generative Adversarial Networks (GANs) that create new cats based on images of existing ones.

 

Reinforcement Learning

You can understand this by imagining someone plays an old computer game for the first time — controls and rules are unfamiliar, but by trial and error, the performance in the game will be better. Google DeepMind’s Deep Q-network is a great example of this, it had beaten people in a variety of vintage video games — Video Pinball, Boxing, Breakout, Star Gunner and others. However, the performance in Ms. Pac-Man, Asteroids, and Seaquest is not that good at the moment.

 

 

Read more on Machine Learning in Enterprise

 

 

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 you can get greater 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

Advanced ML models lead to achieving a higher level of automation. This change could 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 could 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. Accurate forecasts, automated tasks, cost reductions and elimination of human intervention are leading to it.

 

Driving Better Outcomes

Accurate result prediction for your business decisions and smart action triggering give you the best business outcomes in general.

 

 

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Top Industries

 

Industrial AI

 

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 at the production and IoT devices. This is perfect for ML! Anomaly detection, computer vision, the control of quality, demand prediction — just a few fields that ML is involved in.

 

Finance

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

 

Healthcare

Applications of Machine Learning in Healthcare could overtake any team of doctors in processing patient’s information and spotting certain patterns in them. It 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 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 accessible information to train their models, available computing power is greater than ever, so the opportunities are limitless. If you are interested in Machine Learning development or have any questions feel free to contact us!