Machine Learning vs Artificial Intelligence: What's the Difference?

The Relationship Between Artificial Intelligence and Machine Learning

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

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:

• Supervised learning
• Enhanced learning
• Unsupervised learning

Artificial intelligence

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.

How AI and Machine Learning work together

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:

Phase 1: An AI system is built using machine learning and other techniques.

Phase 2: Machine learning models are created by studying patterns in the data.

Phase 3: Data scientists optimize Machine Learning models based on patterns.

Phase 4: The process is repeated and refined until the accuracy of the models is high enough for the tasks to be performed.

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.

The Power of Artificial Intelligence and Machine Learning

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:

PREDICTIVE ANALYTICS

This capability helps companies predict trends and patterns of behaviour by discovering cause-and-effect relationships in data.

RECOMMENDATION ENGINES

With recommendation engines, companies use data analysis to recommend products that someone might be interested in.

SPEECH RECOGNITION AND NATURAL LANGUAGE UNDERSTANDING

Speech recognition allows a computer system to identify words in spoken language, and natural language understanding recognises meaning in written or spoken language.

IMAGE AND VIDEO PROCESSING

These capabilities enable the recognition of faces, objects and actions in images and videos, and the implementation of functionalities such as visual search.

Benefits of AI and Machine Learning

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:

MORE SOURCES OF DATA INPUT

AI and machine learning enable businesses to discover valuable information across a wider range of structured and unstructured data sources.

BETTER AND FASTER DECISION-MAKING

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.

INCREASED OPERATIONAL EFFICIENCY

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.

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Barbara the Edge AI Platform for deploying AI models at scale

At Barbara we help  industrial organizations deploy, run, and manage their AI models organizations remotely, across distributed locations.

With cybersecurity at heart, Barbara is the Edge AI Platform for organizations seeking to overcome the challenges of deploying AI, in mission-critical environments. With Barbara, enterprises turbocharge their deployments to production and leverage the full potential of AI without compromising their security and operational efficiency.

From Edge Nodes, Barbara can communicate with different industrial machines and run AI applications or algorithms on the Edge node itself. These applications and algorithms can be created by the user or purchased from Barbara Marketplace, our marketplace in the cloud. All management of both the nodes and the applications running on them is carried out from Barbara Panel, our remote management dashboard.

EdgeAI: the new model for Distributed Artificial Intelligence vs. the Cloud

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:

• Real-time: Industries where millisecond decision making is required.
• Connectivity: Today's mobile networks are often patchy and cannot always guarantee connection to the cloud. Some services need to be always connected.
• Data volume: The amount of data generated by sensors can be enormous, which could clog wide-area communication channels.
• Context: a business context that follows the trend of decentralisation, allowing IoT data to be interpreted for decision-making.

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The disruption of Edge Technologies 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.

‍Edge AI is a new model of fully distributed computing that supports a wide range of communications and interactions. This enables such powerful functionalities such as:

• Autonomous and local decision making based on incoming IoT data and cached enterprise information.
• Peer-to-peer networks: devices that communicate with each other about an object within their range.
• Distributed queries across data that is stored on devices, in the cloud and anywhere.
• Distributed data management, e.g. data ageing: what data to store, where and for how long.
• Self-learning algorithms that learn and run on the Edge, or in the cloud.
• Isolation, with devices that are switched off for long periods of time, operating with minimal power consumption to maximise their lifespan.

The critical data in the industry originates 'at the edge', across thousands of IoT devices, industrial plants, and equipment machines. Explore how to transform data into real-time insights and actions, using the most efficient and zero-touch Edge AI Platform. Get a free trial today.

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