Artificial Intelligence and Machine Learning are the most popular technologies used to create intelligent systems and although they are related, they are not the same. Because of this relationship, when you look at Artificial Intelligence versus Machine Learning, you are actually analyzing their interconnectedness.
Analysing Artificial Intelligence and Machine Learning from a very general level, we can distinguish how AI is a broader concept, where we create intelligent machines that can simulate the capacity and behaviour of human thinking, while Machine Learning is an application or subset of AI that allows machines to learn from data without explicitly programming them.
Once we have understood this, we can conclude that while Artificial Intelligence and Machine Learning are closely related, they are not the same. Rather, ML is considered a subset of AI.
While an "intelligent" computer uses Artificial Intelligence to think like a human and perform tasks on its own, Machine Learning is how a computer system manages to develop this intelligence.
Machine learning is about extracting knowledge from data. It can be defined as a subfield of Artificial Intelligence, which allows machines to learn from data or past experiences.
Machine Learning is an application of AI, which uses mathematical models of data to help a computer learn without direct instruction. This allows a computer system to continue to learn and improve itself, based on the experience of historical data. It works only on specific fields of the data provided. For example, if we are creating a machine learning model to detect images of cats, it will only give results for images of cats, but if we provide new data such as an image of a dog, it will stop responding.
Some examples of well-known Machine Learning use cases are: the online recommendation system for Google's search algorithms, the spam filter, Facebook's automatic tagging suggestion, etc.
It can be divided into three types:
As we have seen above in a very summarised form: artificial intelligence is a field of computer science that creates a computer system that can mimic human intelligence. It is made up of two words "Intelligence" and "Artificial", which means "a man-made synthetic thinking power".
Artificial intelligence is the ability of a computer system to mimic human cognitive functions, such as learning and problem solving. Through AI, a computer system is able to use logic and mathematics to simulate the reasoning people use to learn from new information and make decisions.
Artificial Intelligence systems do not require programming, but instead use intelligent algorithms.
When looking for the difference between artificial intelligence and machine learning, it is more useful to know how they interact through their close connection than to look for how they differ in order to better understand them. This is how AI and machine learning work together:
This close connection is why the idea of AI versus ML is best addressed by understanding the ways in which AI and machine learning work together.
Neural networks are a series of algorithms that are modeled after the human brain and are used to train a computer to be able to mimic human reasoning. The neural network helps the computer system achieve Artificial Intelligence through deep learning, also known as Deep Learning. Companies in almost every industry are discovering new opportunities through the connection between AI and machine learning.
These are just some of the use cases that have emerged for this technology to help companies transform their processes and products:
This capability helps companies predict trends and patterns of behaviour by discovering cause-and-effect relationships in data.
With recommendation engines, companies use data analysis to recommend products that someone might be interested in.
Speech recognition allows a computer system to identify words in spoken language, and natural language understanding recognises meaning in written or spoken language.
These capabilities enable the recognition of faces, objects and actions in images and videos, and the implementation of functionalities such as visual search.
The connection between artificial intelligence and Machine Learning also offers powerful benefits for almost every industry. These are just some of the main benefits that industries have already been able to realise:
AI and machine learning enable businesses to discover valuable information across a wider range of structured and unstructured data sources.
Companies use Machine Learning to improve data integrity and use Artificial Intelligence to reduce human error, a combination that leads to better decisions based on better data.
With AI and machine learning, businesses become more efficient through process automation, reducing costs and freeing up time and resources for other, higher priority needs.
Industrial AI is starting to pay off. For the past few years, the number and type of devices sending data has been skyrocketing while the cost of storing data has been decreasing, meaning that most companies are collecting more data in more types of formats than ever before.
This has become even more evident with the advent of technologies such as IoT or Edge Computing that allow not only to collect data directly from machines, but are now also able to run Artificial Intelligence algorithms directly on them.
Industry is finally starting to make meaningful use of this data to produce real value. The pandemic has driven this trend even further, as many organisations have had to change their businesses and have seen AI as an opportunity to transform their processes and business model.
Industries that have been successful in delivering results through AI have realised that it is not one ML model that will make a difference, but hundreds or thousands. And that means scaling up big data efforts.
To achieve the scale needed to make AI a success, organisations need to make data part of their day-to-day activities and this is only possible by democratising data and making it accessible at all levels. This is already possible thanks to flexible platforms such as Barbara's.
Barbara's Industrial Edge platform is a tool that allows:
This means cost and time savings in all parts of the processes (from connecting to data to building ML models and implementing them).
The Edge Barbara platform provides both the necessary flexibility and control to easily scale ML and AI initiatives and the ability to facilitate rapid iteration in a collaborative manner.
Edge technology enables more computing power every day, so its ability to deploy increasingly complex applications opens the door to distributed and decentralised Artificial Intelligence, revolutionising the computing model by introducing new usage scenarios with the following conditions in common:
The disruption of the cloud model does not mean the disappearance of the cloud, but rather its extension to the periphery. The cloud will continue to exist. In fact, certain functions are better performed in the cloud, such as the training of predictive algorithms, as usually only the cloud has all the necessary history.
Recommended reading: Top AI Trends at the Edge in 2022
Edge AI is this, a new model of fully distributed computing that supports a wide range of communications and interactions. This enables such powerful functionalities such as:
Barbara is the Cybersecure Industrial Edge Platform designed to govern distributed intelligence. We help industrial companies in their digitisation process, connecting industrial equipment to the Internet and enabling Edge Computing in a secure way.
We have developed the technology platform to create, deploy and run Machine Learning and Artificial Intelligence algorithms on the Edge in an agile and secure way, for sectors with dispersed critical assets.
In addition, we have recently been named one of the 100 Spanish startups that will lead the way the future, according to Venture Capital experts.
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