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It’s behind the scenes in our favourite apps.
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Making breakthroughs in the lab,
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and constantly in the news.
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Artificial Intelligence is all around us.
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But how does it work?
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Let’s shed some light on the world of AI.
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‘Artificial Intelligence’ is an umbrella term that covers a wide range of systems that
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all work in slightly different ways.
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But many of the most widely known use mathematics to find patterns in data.
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These patterns are then used to make predictions.
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The most common forms of these AI systems use something called machine learning.
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Where algorithms analyse data, arranging the patterns and features into models.
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You can think of models a bit like a map.
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Let’s look at how a machine learning model might use an image of a koala.
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This image has millions of pixels.
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When we put this digital file through a machine learning model,
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these data points are processed via many layers of multiplication and addition
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until patterns start to emerge for different features.
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You can think of these like islands.
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The more images we add, the more comprehensive the map becomes.
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Here on ‘Koala Island’, the west side of the island represents koalas with small ears,
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and the east, big ears.
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What’s really mind blowing, is that ear size is only one feature,
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and to represent all of the different shapes, colours, moods and compositions
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of koala images, we not only have to get 3D
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but imagine thousands of dimensions.
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Maybe for now, let’s keep it at just 2!
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Now, if we take this map that’s been trained with millions of koala images,
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and ask what an image might look like… here,
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an AI system can generate a completely new image related to that location.
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This is called Generative AI.
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Amazingly, this mapping process works for any data.
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Whether it’s text, images, sound - whatever can be described with numbers!
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When we train models with these different data types together, it’s a bit like combining two maps.
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Here we have a text-to-image model, trained using images and their text labels.
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These models can take on complex tasks, answer questions,
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write poems and music, and even generate videos from scratch.
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But fundamentally, what we’re doing is giving computer systems a way of mapping information,
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and making connections between patterns.
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It’s maths, not magic.
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So while these outputs are very convincing,
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it’s important to understand that they’re only a prediction based on training data.
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If we go back to our text-to-image map,
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and imagine that our text map was trained using American examples, instead of Australian
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This prompt might give us a much different result.
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Sometimes this can lead to something called bias. Where unfair or unbalanced
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outputs are generated that amplify inaccuracies or gaps in the data.
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Take planning a playlist for example.
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If an AI was trained using only your listening history,
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it won’t have the right data to generate a playlist that appeals to everyone.
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It’s important to remember that human and artificial intelligence
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are different things.
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For example, humans can instinctively understand context and apply common sense.
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AI systems approach things differently.
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That’s one of the reasons it’s important to understand how AI systems work,
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to ask questions and decide when and how we want to use them.
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In the right hands, AI systems can be incredibly powerful tools.
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Helping manage huge data sets, see patterns that humans can’t see
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and automate complicated processes.
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But it’s up to all of us to ensure it’s being pointed at the brightest future possible.
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To find out more check out csiro.au/ai
