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AI · 2.1

What AI Is, and What It Is Not

Section A: 10 multiple choice questions, 10 marks. Section B: 3 written questions, 13 marks. Total 23 marks.

Name

Class

Date

Mark  / 23

Section A

Circle the letter of the correct answer. One mark each.

  1. 1What does the word 'infers' add to the EU AI Act's definition of an AI system?1
    1. AIt must be connected to the internet to count as AI
    2. BIt must be built and sold by a large technology company
    3. CIt must be able to hold a spoken conversation with people
    4. DIt works out how to produce outputs instead of following a fixed formula
  2. 2Which is an example of narrow AI?1
    1. AA program that recognises faces in photos
    2. BA robot that is better than humans at everything
    3. CA human expert using a computer
    4. DA machine as capable as a person at almost any task
  3. 3How do AI, machine learning and deep learning relate?1
    1. AThey are separate fields with nothing in common
    2. BThey are three names for the same thing
    3. CAI is part of deep learning, which is part of machine learning
    4. DDeep learning is part of machine learning, which is part of AI
  4. 4In machine learning, what goes in and what comes out of training?1
    1. AAnswers go in; data comes out
    2. BRules and data go in; answers come out
    3. CData and answers go in; rules, called a model, come out
    4. DOnly rules go in; data comes out
  5. 5Which problem is best solved with hand-written rules rather than machine learning?1
    1. ADetecting faces in photos
    2. BRecognising handwriting
    3. CTranslating between languages
    4. DCalculating VAT at 20% on a bill
  6. 6In the agent view of AI, what are actuators?1
    1. AThe parts the agent uses to sense its environment
    2. BThe goals the agent is given
    3. CThe parts the agent uses to act on its environment
    4. DThe data used to train the agent
  7. 7A thermostat switches the heating on whenever the room is below 19 °C. What kind of agent is it?1
    1. AA utility-based agent
    2. BA simple reflex agent
    3. CA general AI
    4. DA learning agent
  8. 8What is a hallucination in AI?1
    1. AA confident output that is false or made up
    2. BA picture generated by AI
    3. CA dream the model has while training
    4. DAn error message from the computer
  9. 9What does Moravec's paradox say?1
    1. AComputers will never beat humans at chess
    2. BMore data always makes a model worse
    3. CReasoning is easy for computers while perception and movement are hard
    4. DEvery AI system eventually becomes general
  10. 10Which task is generative AI?1
    1. APredicting tomorrow's temperature
    2. BWriting a new image from a text description
    3. CFinding the shortest route
    4. DSorting emails into spam and not spam

Section B

Answer in the space provided. The number of marks tells you how many separate points to make.

  1. 1ExplainExplain the difference between traditional programming and machine learning.4
  2. 2DiscussA school wants software that recognises which student is in a photo. Discuss whether it should use hand-written rules or machine learning.6
  3. 3DescribeDescribe a self-driving car as an AI agent.3

Mark scheme

Section A

1D2A3D4C5D6C7B8A9C10B

Section B

1 Explain the difference between traditional programming and machine learning. 4

One mark for each of the following, to a maximum of 4.

  • In traditional programming a person writes the rules
  • The computer applies the rules to data to give answers
  • In machine learning the computer is given data and answers / examples
  • It learns the rules, the model, itself

Model answer. In traditional programming a person writes the rules, and the computer applies those rules to data to produce answers. In machine learning the person supplies data together with the correct answers, as labelled examples, and the computer works out the rules for itself. The learned rules are called a model, which is then used to produce answers for new data it has never seen.

2 A school wants software that recognises which student is in a photo. Discuss whether it should use hand-written rules or machine learning. 6

One mark for each of the following, to a maximum of 6.

  • Rules for faces are very hard to write
  • Faces vary: lighting, angle, expression, glasses, hair
  • Machine learning can learn patterns from labelled photos
  • It needs enough photos of each student
  • It will sometimes be wrong, so mistakes need handling
  • Privacy or consent concerns about storing faces

Model answer. Hand-written rules would be almost impossible here, because nobody can describe a particular face precisely enough, and the same face changes with lighting, angle, expression, glasses and hair. Every rule would have exceptions. Machine learning suits the problem, because it can learn the patterns that distinguish each student from labelled photos. However, it needs several photos of every student, it will sometimes misidentify someone, so there must be a way to handle mistakes, and storing biometric data about children raises serious privacy and consent issues under data protection law. Machine learning is the right technique, but only with consent, security and a human fallback.

3 Describe a self-driving car as an AI agent. 3

One mark for each of the following, to a maximum of 3.

  • Sensors such as cameras, radar or GPS perceive the environment
  • It decides what to do, such as when to brake or where to steer
  • Actuators such as steering, brakes or accelerator act

Model answer. A self-driving car perceives its environment through sensors such as cameras, radar and GPS. Its software decides what to do next, such as when to brake, how fast to go and where to steer, to reach its goal safely. It then acts on the world through actuators: the steering, brakes and accelerator.