ai and Ml

What does AI stand for?

• Artificial Intelligence

• Analog Input

• Advanced Interface

• Automated Inference

What is the difference between AI and ML?

• AI is the broader concept, while ML is a subset of AI.

• AI is only used in robotics, while ML is used in software development

• AI focuses on human-like intelligence, while ML focuses on data analysis

• AI and ML are the same thing

Give an example of a machine learning algorithm.

• Random Forest

• K-means Clustering

• Linear Regression

• Decision Tree

Explain the concept of neural networks.

• Neural networks are based on linear regression models.

• Neural networks are physical networks of neurons in the human body.

• Neural networks are algorithms inspired by the human brain's structure, consisting of interconnected nodes that process data through layers and learn patterns through training.

• Neural networks are only used in computer hardware design.

How is supervised learning different from unsupervised learning?

• Supervised learning uses unlabeled data, while unsupervised learning uses labeled data.

• Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.

• Supervised learning is used for clustering, while unsupervised learning is used for classification.

• Supervised learning does not require a model, while unsupervised learning does.

What is the role of data in AI and ML?

• Data is used to train models, validate their performance, and make predictions in AI and ML.

• Data is only used for visualization purposes in AI and ML.

• AI and ML models can function effectively without any data.

• Data has no impact on the performance of AI and ML models.

Discuss the importance of training data in machine learning.

• Training data is essential in machine learning as it is used to teach the model patterns and relationships within the data, enabling the model to make accurate predictions or classifications.

• Using random data for training is more effective than using real data.

• Training data is optional in machine learning and does not impact model performance.

• Training data is only necessary for traditional statistical methods, not for machine learning.

What are some common applications of AI and ML?

• Agricultural irrigation, music production, space exploration

• Recommendation systems, image and speech recognition, natural language processing, autonomous vehicles, predictive analytics

• Email marketing, virtual reality gaming, cryptocurrency mining

• Weather forecasting, social media monitoring, online shopping

What is the future potential of AI and ML?

• AI and ML have no future potential.

• AI and ML will never be widely adopted.

• AI and ML are outdated technologies.

• AI and ML have significant future potential.

How can bias be introduced in AI algorithms and how to mitigate it?

• Bias can be introduced through biased training data, algorithms, or human input. Mitigate by using diverse data, auditing algorithms, and involving diverse perspectives.

• Mitigate bias by ignoring diverse perspectives and only focusing on one viewpoint.

• Bias is not a concern in AI algorithms and does not need to be mitigated.

• Bias can be introduced by using unbiased training data and algorithms.