A Level Computer Science: Artificial intelligence and emerging technology, questions with answers

Max 4: supervised learning uses labelled training data to predict future events based on past outcomes; unsupervised learning uses unlabelled training data / data without instructions on what to do with it / without correct or expected outcomes, to search for hidden patterns, relationships, structures, similarities or outliers; ... to get closer to the desired outcome as training progresses.

Supervised learning uses labelled examples; unsupervised learning finds structure in unlabelled data.

• Uses image recognition / facial recognition
• Measures the distance between facial features of the customer
• Stores the values in a database of user information
• An image is captured using the digital camera
• The AI identifies that the image is a face
• By analysing the pixels to find patterns
• The distance between features is calculated and compared to those in database

4 marks: 1 each to max 4.

Negative: May miss developing reasoning skills, negative impacts on communication, digital divide. Positive: Additional support for struggling students. Long-term: Intellectual property concerns.

Multiple points covering negative, positive, and long-term impacts.

• Captures an image of the face
• Uses image recognition
• Trained to identify the features of a face in an image using a large number of images
• Analyses images for facial features
• Uses the probability of a match

4 marks: 1 each to max 4.

• It uses image recognition
• The pixels of each image from the camera are stored and matched to the expected shape/size/colour of registration number
• It uses optical character recognition
• The pixels within the registration number area identified are analysed and compared to expected characters/letters/numbers

4 marks: 1 for each bullet point to max 4.

• An artificial neural network is the component of artificial intelligence that is meant to simulate the functioning of a biological brain
• Artificial neural networks are a key component of machine learning
• They can solve problems that would prove impossible or difficult for humans // have self-learning capabilities
• Can be layered (input, hidden and output layers) // have many interconnected layers
• Weights are assigned between nodes
• Weights are adjusted through training to give a more accurate result
• More complex learning capabilities / more accurate results with larger numbers of hidden layers

4 marks: 1 for each mark point to max 4.

• Deep learning makes good use of unstructured data
• Deep learning outperforms other methods if the data size is large
• Deep learning systems enable machines to process data with a nonlinear approach
• Deep learning is effective at identifying (hidden) patterns / patterns that humans might not be able to see
• It can provide a more accurate outcome with higher numbers of hidden layers

2 marks: 1 for each correct point to max 2.

• Artificial neural networks are intended to replicate the way human brains work
• Weights/values are assigned for each connection between nodes
• The data are input at the input layer and are passed into the system
• They are analysed at each subsequent (hidden) layer where characteristics are extracted/outputs are calculated
• This process of training/learning is repeated many times to achieve optimum outputs
• Reinforcement learning takes place

4 marks: 1 each to max 4.