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Artificial Intelligence

Artificial Intelligence

What is Artificial Intelligence?

Artificial Intelligence is the ability of machines to mimic human capabilities in a way that we would consider 'smart'.

You most likely have come across – or are aware of – AI applications such as self-driving cars, facial recognition, Chess/Go players, security systems, or speech/voice recognition (eg. those used in an intelligent virtual assistant).

How is AI different from 'normal' computing?

In conventional computing, a programmer writes a computer program that precisely instructs a computer what to do to solve a particular problem. With AI, however, the programmer instead writes a program that allows the computer to learn to solve a problem by itself.

That sounds like overdoing it, but this is really the way we do things: At school, students learn the rules that allow them to solve a vast number of different problems: Instead of teaching 1,000 solutions to 1,000 problems, teachers teach students the practices and techniques how to solve a variety of problem instead. The idea behind AI is that we teach a computer to learn to solve problems. And because machines are good at crunching large amounts of data without ever getting tired, computers can solve some tough problems that our brains would struggle with.

What is machine learning?

Machine Learning is an application of AI. Over recent times, the increased amount of data available for use in powerful computer systems has enabled the implementation of AI. With Machine Learning, we give the machine lots of examples of data, demonstrating what we would like it to do so that it can figure out how to achieve a goal on its own. The machine learns and adapts its strategy to achieve this goal.

Artificial Neural Networks (ANNs - some of which are called Deep Learning) are inspired by the workings of the biological brain's neural networks. ANNs can learn to identify patterns by using a feedback loop to learn from mistakes and improve their results.

Is there an AI that can solve any problem?

Current AI solutions are limited to particular applications, such as Chess, Go, autonomous driving, or facial recognition. AI solutions aren't currently versatile like we humans are. But they can beat even the best human player at a game of Chess or Go.

AI Criticism

While there are many promising aspects of AI, its use also raises some concerns. For example:

  • Who is responsible when things go wrong in an application powered by AI?
  • How can we identify when AI systems exhibit bias due to training data or hidden algorithms?
  • What will happen when an AI surpasses human intelligence?

Connections to the Australian Curriculum:

In the Digital Technologies learning area, AI:

  • provides valid and interesting context and applications for curriculum skills and knowledge about data and algorithms
  • is a good application of computational thinking by demonstrating abstraction and decomposition (for example, in Artificial Neural Networks)
  • is an industry-relevant area of computer science
  • is relevant to student personal experience, eg social media or Netflix algorithms
  • is rich in potential real-world applications for investigation, eg driverless cars and face recognition
  • is highly suited to a discussion of the ethical and social impacts of technology
  • is scalable for understanding concepts, from primary through to secondary students

Connections to General Capabilities and other learning areas include:

  • The study of AI provides a mirror for students to reflect on their learning strategies to become more effective learners.
  • Issues arising from the use of AI solutions touch ethical, social and intercultural areas.
  • Machine Learning solutions frequently draw on bodies of data from fields as diverse as History, Geography, Media Studies and English.
  • Artificial Neural Networks mimic the biological brain, connecting to Biology.

See the Curriculum Links section for more detail.

In summary

AI is about making computers smarter by allowing them to learn. Machine Learning is the application of AI using big data and powerful computing. Some of the approaches in Machine Learning are inspired by biology.

This information was developed in collaboration with Computer Science Education Research (CSER) group and The Digital Technologies Institute.

Image: geralt/pixabay

RESOURCES

Learn more about it


Videos and webinars


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Demystifying Artificial Intelligence (54 mins)

A webinar focused on how we can teach AI to students. It explores underpinning concepts of AI, from simple algorithms through to machine learning from data sets. Dr Joshua Ho shares his experience on developing AI classroom activities for primary school students (see Facial Recognition, Machine Learning), to demystify the concept of AI.



Online courses and interactive texts



Quick reference

LESSON IDEAS

How to teach it



Unplugged lessons:

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Home automation programming

We often converse with automatic chatbots for customer service without even knowing. But how do these services work? Is there artificial intelligence (AI) in them? Three projects are offered to cater for student interest and different programming skill levels.

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Fun projects with language translation

Investigate home automation systems, including those powered by artificial intelligence (AI) with speech recognition capability. These suggested activities provide a level of differentiation to cater for students’ range of programming skills.

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Data bias in AI

Artificial intelligence can sometimes be biased to certain shapes or colours. When such AI systems are applied to situations that involve people, then this bias can manifest itself as bias against skin colour or gender. This lesson explores bias in AI, where it comes from and what can be done to prevent it.

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Home automation with AI

Home automation is all the rage. You talk to your mobile phone to control the lights, the fan, the air conditioner, or your pool pump. But how does it work? In this lesson, we explore the AI that could power a home automation system.

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AI Smartphone Security

This lesson provides an opportunity to investigate security measures, including those powered by artificial intelligence (AI), that are used to protect users from unauthorised (unapproved, unwanted) access to their digital devices.

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AI image recognition: exploring limitations and bias

A hands-on activity to practise training and testing an artificial intelligence (AI) model, using cartoon faces, including a discussion about sources of potential algorithmic bias and how to respond to these sources.

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Note the music

We can program a computer to play music. Alternatively, we can train an artificial intelligence (AI) computer about what notes go well with others, so it can play a duet with a human musician. Students can make their own instrument that plays a particular note for a set beat or they can incorporate the random function to mimic AI.

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Anti-bullying AI

Sometimes we write and post things on social media in a hurry. Such posts can hurt people and even make them feel bullied. Wouldn't it be great if an Artificial Intelligence application could check our posts as we write them, and warn us if they were potentially hurtful? This lesson was developed by the Digital Technologies Institute in collaboration with DT Hub.

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Can AI guess your emotion?

Discuss emotions as a class, and introduce the idea of artificial intelligence (AI). This lesson can also be used to introduce image classification – a key application of AI. Developed in collaboration with Digital Technologies Institute.

AI classroom activity: Facial recognition

AI classroom activity: Facial recognition

An article discussing an unplugged activity to explore machine learning for facial recognition with Primary students using data on cartoon princesses. See also the webinar with Dr Joshua Ho, in which this activity is described.



Lessons using technology:

AI classroom activity: Machine learning

AI classroom activity: Machine learning

An article discussing a coding activity (suitable to most visual or general purpose languages) that demonstrates machine learning with a searching algorithm to guess a number. The activity is then adapted to robotics by improving a robot’s bowling accuracy. See also the webinar with Dr Joshua Ho, in which this activity is described.