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5. AI tools can generate new ideas, explore possibilities, and repetitive tasks.
6. Artificial Intelligence, where Artificial defines " " and Intelligence defines “thinking ability”.
7. There are players in Turing Test.
8. has intelligence more than human beings, it can do tasks beyond human capabilities.
9. This type of data lacks any specific organisation, making it more challenging to analyse compared to structured
data. This data is called .
10. NLG is a subfield of NLP, but instead of understanding, it focuses on human language.
C. State whether the following statement is true or false.
1. Data science focuses on processing text and speech inputs so computers can understand,
interpret, and generate human language.
2. Cognitive computing understands and simulates reasoning.
3. Self-driving cars use computer vision to navigate roads safely.
4. Cancer researchers use deep leaning to automatically detect cancer cells.
5. Machine learning is divided into 2 main categories.
SECTION B (Subjective Type Questions)
A. Short answer type questions.
1. Define machine learning. Also, give two applications of machine learning in our daily lives.
Ans. Machine learning is a subset of Artificial Intelligence that enables machines to improve at tasks with experience
(data). The goal of machine learning is to enable machines to learn by themselves using the provided data and
make accurate predictions and decisions.
Machine learning is used in Snapchat Filters, NETFLIX recommender system.
2. Differentiate between Artificial Intelligence, Machine Learning, and Deep Learning.
Ans.
Artificial Intelligence Machine Learning Deep Learning
The goal of AI is to enable The goal of ML is to enable The goal of deep learning is to solve
the machine to think without the machine to learn from past complex problems as the human
any human intervention. experiences. brain does, using various algorithms.
It requires a huge amount of It can work with less data It requires a huge amount of data
data to work. compared to deep learning and AI. compared to ML.
3. Explain supervised learning. Also, name two algorithms that use supervised learning and two real-world applications
of supervised learning.
Ans: In supervised learning, the machine needs external supervision to learn from data. The supervised learning models are
trained using labelled data. Regression and classification are the two main algorithms that use supervised machine
learning. In the real world, supervised learning can be used for risk assessment and fraud detection.
4. The goal of reinforcement machine learning is to maximise rewards. Explain the statement.
Ans. In reinforcement learning, the AI machine/model faces a game-like situation. The machine uses trial and error to
come up with a solution to the problem. The machine gets either rewards or penalties for the actions it performs. It’s
up to the model to figure out how to perform the task to maximise the reward, starting from totally random trials
and finishing with sophisticated tactics and human-like skills.
5. Define computer vision.
Ans. Computer vision is a branch of AI that involves extracting information from digital images such as videos and
photographs, analysing them and understanding their content.
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