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Content article: What is the difference between Artificial Intelligence and Machine Learning?

There is much confusion in the media about the definition and application of these two terms with some technology giants claiming that many companies are not using the terms correctly in their advertising. (1)

In this article, I aim to unpack some of those myths and explore the potential applications of this exciting new branch of science.

What is Artificial Intelligence?

According to computer science experts at Carnegie Mellon University, Artificial (AI) is defined as “the science and engineering of making computers behave in ways that until recently required human intelligence.”

Machine Learning (ML), on the other hand is “the study of computer algorithms that allow computer programmes to automatically improve through experience.” Therefore, ML it is a branch of AI.

How can we use ML to achieve AI?

ML is used to examine data and find common patterns and find nuances.

There are several types of machine learning- supervised, unsupervised and relational.

Supervised learning

This uses algorithms to try to model relationship and dependencies between the target prediction output and the input features so new output values based on this data can be predicted. It is currently used by firms such as Netflix and Spotify to analyse the music or films you listen to the most and then used to recommend other films or music you may like based upon these choices.

Unsupervised learning

This is used in pattern detection and descriptive modelling. The algorithms don’t have output categories or labels on the data. It allows the machine to self-improve based upon real world experience. It is used in robotics for example to detect when someone is smiling by correlating facial patterns and words such as “what are you smiling about?” In doing so, this can be used to recognise human behaviour and changes in emotion.

Reinforcement learning

Reinforcement learning is the idea that the optimal behaviour or action is reinforced by a positive reward. Forbes magazine compares it to a toddler learning to walk. Just as a toddler has to adjust the size of step they take if their previous step caused them to fall over, so reinforcement learning algorithms will enable a machine or software agent to adjust its behaviour based upon feedback from the environment. From medicine and games to industrial automation and robotics, the potential applications of this are huge.

There is much confusion in the media about the definition and application of these two terms with some technology giants claiming that many companies are not using the terms correctly in their advertising. (1)

In this article, I aim to unpack some of those myths and explore the potential applications of this exciting new branch of science.

What is Artificial Intelligence?

According to computer science experts at Carnegie Mellon University, Artificial (AI) is defined as “the science and engineering of making computers behave in ways that until recently required human intelligence.”

Machine Learning (ML), on the other hand is “the study of computer algorithms that allow computer programmes to automatically improve through experience.” Therefore, ML it is a branch of AI.

How can we use ML to achieve AI?

ML is used to examine data and find common patterns and find nuances.

There are several types of machine learning- supervised, unsupervised and relational.

Supervised learning

This uses algorithms to try to model relationship and dependencies between the target prediction output and the input features so new output values based on this data can be predicted. It is currently used by firms such as Netflix and Spotify to analyse the music or films you listen to the most and then used to recommend other films or music you may like based upon these choices.

Unsupervised learning

This is used in pattern detection and descriptive modelling. The algorithms don’t have output categories or labels on the data. It allows the machine to self-improve based upon real world experience. It is used in robotics for example to detect when someone is smiling by correlating facial patterns and words such as “what are you smiling about?” In doing so, this can be used to recognise human behaviour and changes in emotion.

Reinforcement learning

Reinforcement learning is the idea that the optimal behaviour or action is reinforced by a positive reward. Forbes magazine compares it to a toddler learning to walk. Just as a toddler has to adjust the size of step they take if their previous step caused them to fall over, so reinforcement learning algorithms will enable a machine or software agent to adjust its behaviour based upon feedback from the environment. From medicine and games to industrial automation and robotics, the potential applications of this are huge.

One thing is clear, whatever your understanding of AI and ML, this new and exciting branch of science is rapidly changing the face of society as we know it.

References

https://medium.com/datadriveninvestor/differences-between-ai-and-machine-learning-and-why-it-matters-1255b182fc6††††††††

https://simplicable.com/new/unsupervised-learning

https://www.forbes.com/sites/bernardmarr/2018/09/28/artificial-intelligence-what-is-reinforcement-learning-a-simple-explanation-practical-examples/#170ae27c139c

Categories:Uncategorized

Star Copy Edit

Copywriter and proofreader specialising in educational and scientific copy

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