What does artificial intelligence (AI) use to simulate intelligence?
Computers.
Which human behaviors does AI simulate?
Recognizing patterns, learning, making decisions, and solving problems.
Name one example of AI in virtual assistants.
Siri.
What is an example of AI in self-driving cars?
Tesla FSD.
Why has AI grown in importance?
Because of increased computing power, big data availability, and AI research breakthroughs.
What is machine learning (ML)?
A subset of AI that enables computers to learn from data and improve without explicit programming.
What do machine-learning algorithms identify in input data?
Patterns in input data.
What do machine-learning algorithms use patterns for?
To make predictions or decisions on new data.
Name one application driven by machine learning.
Email spam filtering.
Name another application driven by machine learning.
Personalized product recommendations.
Give a third application driven by machine learning.
Fraud detection in banking.
Give a fourth application driven by machine learning.
Natural language processing.
What type of data does supervised learning use for training?
Labeled data with correct answers.
What is the goal of supervised learning models?
To make predictions or classifications on new data.
What kind of data is given to a model in unsupervised learning?
Unlabeled data.
What does unsupervised learning ask a model to find?
Patterns, relationships, or groupings within data.
How does reinforcement learning train a model?
Through interaction with an environment using rewards or penalties.
What is the objective of reinforcement learning over time?
To maximize performance.
What kind of neural networks does deep learning use?
Multilayered neural networks.
What type of tasks can deep learning perform?
Complex tasks on large datasets.
What does supervised learning use to learn relationships?
Labeled examples.
What is a key limitation of supervised learning despite its accuracy?
It requires large labeled datasets that are costly to create.
How does unsupervised learning group data points?
By clustering similar data points based on shared features.
What is a requirement that unsupervised learning does not have?
It does not require labeled data.
What does an agent do in reinforcement learning?
It interacts with an environment, takes actions, and receives rewards or penalties.
Name one application of reinforcement learning.
Self-driving cars.
What is an artificial neural network inspired by?
Biological neural networks like the human brain.
How do deep-learning networks process data?
By passing it through input, multiple hidden, and output layers.
What is a key advantage of deep learning?
It achieves state-of-the-art performance in tasks like image recognition.
What is a major drawback of deep learning?
It is resource intensive and can be a difficult-to-interpret black box.
What does Predictive AI use to analyze data and predict outcomes?
Machine learning analyzes historical data to predict future outcomes.
What does Generative AI create using learned data patterns?
New content such as text, images, or audio.
Name one application of Predictive AI in real-world scenarios.
Healthcare outcome prediction.
Which AI models are used for text generation in Generative AI?
ChatGPT, Gemini, and Copilot.
What is one risk associated with Generative AI content creation?
Misuse such as deepfakes or plagiarism.
What can predictive AI forecast in networks?
Network traffic, security threats, and hardware failures.
What types of content can generative AI create for networks?
Network documentation, device configurations, designs, troubleshooting solutions, and automation scripts.
What kind of automation scripts can generative AI produce for network devices?
Python scripts for configuring network devices.
How does predictive AI analyze data for network optimization?
By analyzing historical and current network data to identify patterns.
What network improvements does predictive AI support through pattern identification?
Bandwidth optimization, threat mitigation, and reduced downtime.
What does AI Network Analytics establish in a network?
Baseline network behavior.
What does AI Network Analytics provide besides baseline behavior?
Optimization insights and recommendations.
What continuous function does AI Network Analytics perform?
Monitors the network to detect and predict anomalies.
What is the role of the Machine Reasoning Engine in network issues?
Performs root-cause analysis using AI.
What actions can the Machine Reasoning Engine suggest or take?
Suggest resolutions or take automated corrective actions.
What does AI Endpoint Analytics identify and classify?
Network devices.
What does AI-enhanced Radio Resource Management dynamically adjust?
Radio settings to balance wireless load, reduce interference, and improve coverage.
What benefits does Cisco Catalyst Center's AI-enabled features provide?
Identify issues before user impact, reduce resolution time, and improve network performance and security.
Test your knowledge with 21 questions on AI and Machine Learning Fundamentals.
1. Which network task is a direct application of predictive AI?
2. A system receives customer records without category labels and discovers groups of customers with similar behavior. Which learning type is being used?
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