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Questions and Answers
Natural Language Processing is a subfield of Generative AI.
False
All robotics systems are considered intelligent in the same sense as human intelligence.
False
Generative AI techniques can be used outside of NLP for tasks like image generation and music composition.
True
Ethical considerations in AI are only relevant in high-stakes applications such as healthcare and criminal justice.
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AI algorithms can only inherit biases from the data they are trained on if the data is intentionally biased.
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Transparency in AI decision-making processes is only necessary in applications where human life is at risk.
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Generative AI is a subfield of Natural Language Processing.
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Robotics is a subfield of Artificial General Intelligence.
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Machine Learning is a superset of Artificial Intelligence.
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Currently, there are existing examples of Strong AI.
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Supervised Machine Learning involves training models on unlabeled data.
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Pattern Recognition is a superset of Machine Learning.
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Arthur Samuel is the founder of Artificial Intelligence.
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Regression in Machine Learning involves predicting categorical values.
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Machine Learning gives computers the ability to learn without being explicitly programmed.
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Strong AI is already widely used in many industries.
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Machine learning is a type of narrow AI.
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Unsupervised learning is a type of machine learning where the machine is provided with labeled data.
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Natural Language Processing (NLP) is a type of machine learning.
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Artificial intelligence is a simulation of human intelligence done by humans.
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Neural networks are a type of supervised learning algorithm.
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Reinforcement learning is a type of unsupervised learning.
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Self-driving cars are an example of general AI.
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All AI systems built till date fall under the category of narrow AI.
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Study Notes
Generative AI
- Focuses on the creation of new data or content across various modalities
- Can be used within NLP for tasks like text generation, language modeling, and dialogue generation in conversational AI systems
Robotics
- Involves the integration of AI into physical systems, enabling them to perceive and interact with the environment
- Types:
- Intelligent robotics: autonomous vehicles, social robots, and robots used in complex tasks
- Non-intelligent robotics: industrial robots used in manufacturing processes, robotic arms in assembly lines
Ethical Considerations
- Crucial due to the potential impact on individuals, society, and the environment
- Issues:
- Bias in algorithms: unfair outcomes, discrimination, and perpetuation of societal inequalities
- Transparency and accountability: needed in AI decision-making processes to ensure trustworthiness, especially in high-stakes applications
General AI (Strong AI)
- Machines will possess the ability to think and make decisions like humans
- Currently no existing examples, but believed to be possible in the future
Machine Learning
- Subset of AI that focuses on the ability of machines to learn from data and change algorithms as they learn more
- defined by Arthur Samuel (1959) as a field of study that gives computers the ability to learn without being explicitly programmed
Machine Learning vs Pattern Recognition
- Machine Learning: development of algorithms and statistical models to enable computers to learn from and make predictions or decisions based on data
- Pattern Recognition: field within machine learning that deals with the identification and classification of patterns within data
Supervised Machine Learning
- Type of machine learning where the model is trained on labeled data, with input features and corresponding target labels or outputs
Regression vs Classification
- Regression: aims to predict a continuous numerical value or quantity
- Classification: aims to predict a categorical label or class
Course Outline
- Introduction to Artificial Intelligence
- Problem-Solving and Search Algorithms
- Knowledge Representation and Reasoning
- Machine Learning Fundamentals
- Neural Networks and Deep Learning
- Natural Language Processing (NLP)
- Reinforcement Learning
- AI Ethics and Future Directions
Course Evaluation
- 25% Midterm
- 20% Quizzes and Assignments
- 10% Presentation (Case Study or specific topic)
- 5% Peer Evaluation
- 40% Final Exam
Definition of AI
- Simulation of human intelligence by machines programmed by humans
- Machines need to learn how to reason and do some self-correction as needed along the way
- Artificial intelligence addresses the use of computers to mimic the cognitive functions of humans
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Description
Quiz about generative AI and its applications in natural language processing, including text generation and language processing. Covers topics such as virtual assistants, automated content creation, and simulations.
