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Machine learning
AFP English
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2 years ago
Machine learning applications are ubiquitous. How does it work? This videographic answers. VIDEOGRAPHIC
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News
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00:00
[Music plays]
00:07
Machine learning applications are ubiquitous.
00:10
We are using them on a daily basis, often without realising it.
00:13
On your smartphone alone, there are already countless applications.
00:17
When you log on, browse for products while shopping online,
00:21
check emails, type a text message, or plan a route.
00:25
So what is machine learning and how does it work under the hood?
00:28
It is a subfield of artificial intelligence that gives computers
00:31
the ability to learn without explicitly being programmed,
00:35
by enabling them to learn from data and experience.
00:38
Sometimes an algorithm can learn a task under supervision.
00:42
This involves using labelled data to train a model to make predictions or decisions.
00:48
It is used on online shopping to predict a user's preferences
00:51
based on historical data such as purchase history,
00:54
browsing behaviour, and product attributes,
00:58
and train a model to recommend products accordingly.
01:01
Other times, algorithms can learn without being supervised.
01:04
This involves identifying patterns and relationships in unstructured data
01:09
to create structure and categorisation.
01:11
It is used to cluster different products based on attributes
01:15
such as price, category, brand, and descriptions,
01:18
without labelled examples or guidance.
01:20
The goal is to find hidden structures and patterns
01:23
and identify similarities or differences between products.
01:27
Finally, sometimes algorithms learn from their own performance
01:30
and previous experiences as they adjust their behaviour
01:33
to improve the decision-making process.
01:35
This can be used to train a model to recommend products
01:38
that maximise user engagement or satisfaction
01:41
by recommending the right products to the right users.
01:44
Machine learning has a wide array of applications,
01:47
but it comes with ethical implications we need to consider
01:50
surrounding issues related to bias, fairness, accountability,
01:53
privacy, and transparency.
01:55
[Music plays]
01:57
[Music fades]
01:59
[Music fades]
02:01
[Music fades]
02:03
[BLANK_AUDIO]
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