Podcast
Questions and Answers
In the context of AI/ML, which of the following is the primary goal of a classification algorithm?
In the context of AI/ML, which of the following is the primary goal of a classification algorithm?
- To find the shortest path between two points.
- To group similar data points together.
- To assign data points to predefined categories. (correct)
- To predict a continuous numerical value.
Which type of error occurs when a spam mail filter incorrectly identifies a legitimate email as spam?
Which type of error occurs when a spam mail filter incorrectly identifies a legitimate email as spam?
- True Negative
- False Negative
- False Positive (correct)
- True Positive
Which AI/ML application is most closely associated with identifying patterns to group customers with similar purchasing behaviors?
Which AI/ML application is most closely associated with identifying patterns to group customers with similar purchasing behaviors?
- Classification
- Recommendation
- Clustering (correct)
- Regression
What deep learning architecture underpins the functionality of modern Large Language Models (LLMs)?
What deep learning architecture underpins the functionality of modern Large Language Models (LLMs)?
In the context of Large Language Models, what does 'in-context learning' or 'few-shot learning' refer to?
In the context of Large Language Models, what does 'in-context learning' or 'few-shot learning' refer to?
Which AI application is most directly used for predicting a continuous outcome, such as house price estimation based on features like size and location?
Which AI application is most directly used for predicting a continuous outcome, such as house price estimation based on features like size and location?
What distinguishes a False Negative error from a False Positive error in a binary classification problem?
What distinguishes a False Negative error from a False Positive error in a binary classification problem?
Given the ethical considerations surrounding AI, how does the 'Trolley Problem' relate to the development of autonomous vehicles?
Given the ethical considerations surrounding AI, how does the 'Trolley Problem' relate to the development of autonomous vehicles?
Consider a scenario where an AI model is used to predict loan defaults. The model has a high accuracy but disproportionately denies loans to individuals from a specific demographic group. Which ethical consideration is MOST directly violated?
Consider a scenario where an AI model is used to predict loan defaults. The model has a high accuracy but disproportionately denies loans to individuals from a specific demographic group. Which ethical consideration is MOST directly violated?
An engineer seeks to optimize a recommendation engine using collaborative filtering. After deploying the updated system, they observe that the engine exhibits a 'filter bubble' effect, where users are predominantly shown content aligning with their pre-existing preferences, significantly reducing exposure to diverse viewpoints. Which strategy MOST directly addresses this unintended consequence, while still leveraging collaborative filtering?
An engineer seeks to optimize a recommendation engine using collaborative filtering. After deploying the updated system, they observe that the engine exhibits a 'filter bubble' effect, where users are predominantly shown content aligning with their pre-existing preferences, significantly reducing exposure to diverse viewpoints. Which strategy MOST directly addresses this unintended consequence, while still leveraging collaborative filtering?
Flashcards
Pathfinding
Pathfinding
Finding the most efficient route between points, like Google Maps does.
Classification
Classification
Classifying data, like sorting emails into 'spam' or 'not spam'.
False Positive (Type I Error)
False Positive (Type I Error)
Incorrectly classifying a negative case as positive (e.g., a genuine email marked as spam).
False Negative (Type II Error)
False Negative (Type II Error)
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Recommendation System
Recommendation System
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Regression
Regression
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Clustering
Clustering
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Large Language Models (LLMs)
Large Language Models (LLMs)
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Transformer (in LLMs)
Transformer (in LLMs)
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Few-Shot Learning
Few-Shot Learning
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Study Notes
- ITCT101 Computer Technologies, Module 2: AI, ML, and Data Science
- Topic: AI & ML Applications
Applications of AI and ML
- AI methods are applied across many fields and industries
- These include Information Technology, Healthcare, Game-Playing, Media, and Business
AI/ML Algorithms
- Applications can include Search: Google Maps Pathfinding, Classification: Spam mail filter and Recommendation: Netflix
Search algorithms
- Google maps uses pathfinding algorithms
- Pathfinding algorithms are also found in game applications
Classification algorithms
- Spam mail filters use classification algorithms
- Other applications include language translation and image anaylsis
Classification Errors
- True Positives (TP): A hit
- True Negative (TN): Correct rejection
- False Positive (FP): False alarm or Type I error
- False Negative (FN): Miss or Type II error
Recommendation algorithms
- Include netflix recommendations
- Approaches include content-based filtering and collaborative filtering
Regression algorithms
- Can include keypoint detection
Clustering algoriths
- Used for customer segmentation
Large Langauge Models
- Behind modern chatbots and other applications like LIDA and DALL-E 3
- Learns to predict the missing word using articles, books, forums, and programming
- LLMs use a deep learning architechture called a "Transformer"
- They learn from lots of text in different languages
- Growth of LLMs can be mesaured by the number of parameters
- GPT-3 has 175B Parameters
- Megatron-LM has 8.3B parameters
- BERT-L has 340M parameters
- ELMO has 94M parameters
- They are few-shot learners (in-context learning)
Ethical Concerns
- Potential Abuse: deepfake videos, autonomous weapons, and driving assistance
- Machine learning methods can have bias
- The co-pilot helps us write code by copying from open source
- Self-driving cars prioritize saving their owner, potentially at the expense of pedestrians
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