Image Recognition Plenary

What is a neural network in the context of artificial intelligence?

• A type of computer virus

• A system of algorithms that attempts to recognise patterns

• A network of human neurons

• A type of hardware used in computers

What is the main purpose of backward propagation in neural networks?

• To move data backwards through the network

• To update the weights of the network to reduce error

• To increase the speed of data processing

• To visualise the network structure

Which of the following is an example of machine learning?

• A computer playing a pre-recorded video

• A robot vacuum cleaning a room

• A program that improves its performance by learning from data

• A calculator performing arithmetic operations

What distinguishes deep learning from traditional machine learning?

• Deep learning uses shallow networks

• Deep learning requires less data

• Deep learning uses multiple layers of neural networks

• Deep learning is only used for image recognition

Why are datasets important in artificial intelligence?

• They are used to store software programs

• They provide the data needed to train AI models

• They are used to cool down computer systems

• They are used to display graphics

Which of the following is an example of AI being used for good in the real world?

• AI systems that create fake news

• AI algorithms that improve online shopping recommendations

• AI tools that assist doctors in diagnosing diseases

• AI programs that generate spam emails

What is forward propagation in the context of neural networks?

• The process of updating weights

• The process of moving data from input to output layers

• The process of deleting unnecessary data

• The process of visualising data

Which of the following is NOT a common application of AI?

• Autonomous vehicles

• Weather forecasting

• Cooking food

• Language translation

Which of the following is a benefit of using deep learning over traditional machine learning?

• Deep learning models require less computational power

• Deep learning models can automatically extract features from raw data

• Deep learning models are always more accurate

• Deep learning models are easier to interpret

How does a neural network learn during the training process?

• By memorizing the input data

• By adjusting the weights based on the error of the output

• By increasing the number of neurons

• By reducing the number of layers