Vision and brain

What is the main topic discussed in the provided text?

• The history of artificial intelligence

• The development of computational approaches

• The impact of viewpoint changes on object recognition

• The ethical implications of AI

According to the text, what has been a significant area of research in the last two years?

• Ethical implications of AI

• Generative artificial intelligence systems

• Historical development of AI

• Human-computer interaction

What challenge to the visual system is identified in the text?

• Ethical implications of AI

• Viewpoint variations

• Lack of computational power

• Human-computer interaction

What happens to the performance of neural networks when the orientation of an object changes relative to the viewer?

• Performance improves

• Performance remains the same

• Performance decreases

• Performance becomes unpredictable

What example is given in the text to illustrate the impact of viewpoint changes on object recognition?

• A school bus being identified as a harmonica

• A cat being identified as a dog

• A tree being identified as a building

• A car being identified as a bicycle

What is one of the key differences between human object recognition and deep neural networks?

• Humans are viewpoint dependent, while neural networks are viewpoint independent.

• Humans are viewpoint independent, while neural networks are viewpoint dependent.

• Both humans and neural networks are viewpoint dependent.

• Both humans and neural networks are viewpoint independent.

How do deep neural networks perform when dealing with distorted images?

• They perform poorly.

• They perform very well.

• Their performance is inconsistent.

• They cannot process distorted images.

What happens to human performance when images are scrambled or edges are altered?

• Performance improves.

• Performance remains the same.

• Performance goes down.

• Performance becomes unpredictable.

What is the effect of adding noise to images on neural network performance?

• Performance improves.

• Performance remains the same.

• Performance is compromised.

• Performance becomes unpredictable.

What is referred to as "superhuman performance" in the context of neural networks?

• When neural networks perform worse than humans.

• When neural networks perform equally to humans.

• When neural networks perform better than humans.

• When neural networks fail to recognize images.