Podcast
Questions and Answers
Levels 5 and 6 can produce tools that outperform humans in well-structured problems.
Levels 5 and 6 can produce tools that outperform humans in well-structured problems.
True (A)
Level 7 is considered a practical approach to artificial intelligence development.
Level 7 is considered a practical approach to artificial intelligence development.
False (B)
Unstructured problems require higher levels of intelligence than well-structured problems.
Unstructured problems require higher levels of intelligence than well-structured problems.
True (A)
Knowledge representation helps in solving well-defined problems only.
Knowledge representation helps in solving well-defined problems only.
For a robot to navigate effectively, it must first characterize its environment.
For a robot to navigate effectively, it must first characterize its environment.
Model-based optimization can produce solutions that are worse than what a human can achieve.
Model-based optimization can produce solutions that are worse than what a human can achieve.
Neural networks directly capture the physics of a problem.
Neural networks directly capture the physics of a problem.
Companies are now able to access unprecedented amounts of data, including dark data they previously did not know they had.
Companies are now able to access unprecedented amounts of data, including dark data they previously did not know they had.
The objective for fitting a statistical model is always to maximize a distance metric.
The objective for fitting a statistical model is always to maximize a distance metric.
Optimization models require an objective function specified by the analyst.
Optimization models require an objective function specified by the analyst.
Automation through AI increases costs while providing inconsistent results.
Automation through AI increases costs while providing inconsistent results.
Natural Language Processing (NLP) allows machines to understand and respond to human queries in a natural way.
Natural Language Processing (NLP) allows machines to understand and respond to human queries in a natural way.
Sequential decision problems involve a repetitive process of decision and information gathering.
Sequential decision problems involve a repetitive process of decision and information gathering.
Machine learning and deep learning are parts of AI that require explicit programming to learn from data.
Machine learning and deep learning are parts of AI that require explicit programming to learn from data.
In sequential decision problems, a decision can only be binary.
In sequential decision problems, a decision can only be binary.
The policy in sequential decision problems maps the state variable information to a decision.
The policy in sequential decision problems maps the state variable information to a decision.
Companies that successfully scale their AI initiatives see lower returns compared to those stalled at the pilot stage.
Companies that successfully scale their AI initiatives see lower returns compared to those stalled at the pilot stage.
AI-powered automation has no impact on industries like manufacturing or transportation.
AI-powered automation has no impact on industries like manufacturing or transportation.
For sequential decision problems, the performance optimization is typically based on a metric that is sample-specific.
For sequential decision problems, the performance optimization is typically based on a metric that is sample-specific.
Virtually all C-suite executives believe leveraging AI is crucial for achieving growth objectives.
Virtually all C-suite executives believe leveraging AI is crucial for achieving growth objectives.
Robotics integrated with AI enables machines to perform physical tasks with reduced accuracy.
Robotics integrated with AI enables machines to perform physical tasks with reduced accuracy.
The only danger of LLMs is misinformation.
The only danger of LLMs is misinformation.
Deterministic optimization involves training using a dataset.
Deterministic optimization involves training using a dataset.
Rule-based logic was the first form of AI that emerged in the 1960s and 1970s.
Rule-based logic was the first form of AI that emerged in the 1960s and 1970s.
Sophisticated algorithms in deterministic optimization search over feasible regions to improve performance metrics.
Sophisticated algorithms in deterministic optimization search over feasible regions to improve performance metrics.
ML techniques and deterministic optimization are fundamentally similar.
ML techniques and deterministic optimization are fundamentally similar.
Rule-based logic was successful in meeting all early expectations of AI.
Rule-based logic was successful in meeting all early expectations of AI.
Deterministic optimization requires a performance metric to evaluate decisions.
Deterministic optimization requires a performance metric to evaluate decisions.
Basic machine learning methods began to emerge under the umbrella of statistics in the early 1900s.
Basic machine learning methods began to emerge under the umbrella of statistics in the early 1900s.
Rule-based logic is only relevant in historical AI systems and has no application in modern machine intelligence.
Rule-based logic is only relevant in historical AI systems and has no application in modern machine intelligence.
The 1990s saw the emergence of tools for scheduling airlines that improved efficiency using deterministic optimization.
The 1990s saw the emergence of tools for scheduling airlines that improved efficiency using deterministic optimization.
In deterministic optimization, controllable parameters are also called variables.
In deterministic optimization, controllable parameters are also called variables.
Neural networks have been popular since the 1970s and are used in many deterministic estimation problems.
Neural networks have been popular since the 1970s and are used in many deterministic estimation problems.
Neural networks only require deterministic optimization to fit training data.
Neural networks only require deterministic optimization to fit training data.
Linear models have no relationship to machine learning and are not used as input variables.
Linear models have no relationship to machine learning and are not used as input variables.
Expert systems represented the first wave of AI advancements in the 1980s.
Expert systems represented the first wave of AI advancements in the 1980s.
Nonparametric models only emerged after the introduction of linear models.
Nonparametric models only emerged after the introduction of linear models.
Creativity is not required when we have a performance metric but lack a well-defined set of decisions.
Creativity is not required when we have a performance metric but lack a well-defined set of decisions.
Judgment is only necessary for well-structured problems with clear metrics.
Judgment is only necessary for well-structured problems with clear metrics.
Reasoning requires the ability to navigate well-structured problems only.
Reasoning requires the ability to navigate well-structured problems only.
Narrow AI is designed to perform a wide range of tasks effectively.
Narrow AI is designed to perform a wide range of tasks effectively.
General AI is capable of handling new and unfamiliar tasks independently.
General AI is capable of handling new and unfamiliar tasks independently.
The main goal of large language models is to minimize the difference between predicted and actual words.
The main goal of large language models is to minimize the difference between predicted and actual words.
Optimizing decisions involves finding the best option within an undefined set of possible actions.
Optimizing decisions involves finding the best option within an undefined set of possible actions.
AI can only be classified by its functionalities and not by its capabilities.
AI can only be classified by its functionalities and not by its capabilities.
Flashcards
Rule-Based Logic
Rule-Based Logic
The first form of AI using predefined human rules for decision-making.
Expert Systems
Expert Systems
Advanced AI systems that utilized rule-based logic to emulate human experts.
Multidimensional Diet Example
Multidimensional Diet Example
An example showcasing complex rule-based logic in healthcare decisions.
Basic Machine Learning
Basic Machine Learning
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Lookup Tables
Lookup Tables
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Parametric Models
Parametric Models
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Nonparametric Models
Nonparametric Models
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Neural Networks
Neural Networks
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Misinformation
Misinformation
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Deterministic Optimization
Deterministic Optimization
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Controllable Parameters
Controllable Parameters
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Performance Metric
Performance Metric
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Search Algorithms
Search Algorithms
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Operations Research
Operations Research
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Neural Network Optimization
Neural Network Optimization
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Model-Based Optimization
Model-Based Optimization
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Dark Data
Dark Data
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AI in Business
AI in Business
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Return on AI Investment
Return on AI Investment
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Natural Language Processing (NLP)
Natural Language Processing (NLP)
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Machine Learning
Machine Learning
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Deep Learning
Deep Learning
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Robotics and Automation
Robotics and Automation
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AI Goals
AI Goals
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Levels 5 and 6
Levels 5 and 6
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Level 7: Science Fiction
Level 7: Science Fiction
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Knowledge Representation
Knowledge Representation
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Unstructured Problems
Unstructured Problems
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Economic Justification
Economic Justification
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Creativity
Creativity
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Judgment
Judgment
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Reasoning
Reasoning
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Narrow AI
Narrow AI
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General AI
General AI
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Complex Judgment
Complex Judgment
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Idea
Idea
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Objective function
Objective function
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Sequential decision problems
Sequential decision problems
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Policy (π)
Policy (π)
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Stochastic programming
Stochastic programming
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Finite horizon
Finite horizon
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Expectation in optimization
Expectation in optimization
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Study Notes
Table of Contents
- Table Of Contents
- I. Blockchain
- History of Blockchain
- Blockchain Explained
- How Does A Blockchain Work?
- Why Do People Use Peer to Peer Network
- The Three Main Pillars of Blockchain Technology
- II. Artificial Intelligence
- What is Ai?
- Need for Ai
- What are the Major Goals of Artificial Intelligence?
- What Comprises to Artificial Intelligence
- Advantages of Artificial Intelligence
- Disadvantages of Artificial Intelligence
- History of Artificial Intelligence
- Levels of Ai
- Types of Ai
- References
Blockchain
-
History
- Blockchain has a history spanning decades, beginning in the 1980s with the emergence of cryptography.
- Cryptographer David Chaum introduced blind signatures, a method for digital currency and privacy.
- In 1991, researchers Haber and Stornetta proposed timestamping digital documents for security.
- In 2008, Satoshi Nakamoto's Bitcoin blockchain marked a disruptive innovation with the "genesis block". This introduced decentralized digital currency enabled peer-to-peer transactions without intermediaries.
-
Blockchain Explained
- A distributed database that stores records in blocks linked together.
- Data is recorded in a public ledger, making it transparent.
- Each block contains data from multiple transactions that are confirmed.
- Blockchains are decentralized, meaning no single entity controls the system; every participant has a copy.
- Data is immutable, making it secure and resistant to modifications or alterations.
-
How Blockchain Works
- Transactions are grouped into blocks.
- Blocks are linked together via cryptographic hashing.
- Blocks are validated and added to the chain.
- Cryptographic hashing ensures data integrity and immutability.
-
Peer-to-peer Network
- A network architecture where each device (peer) can communicate and share resources with other peers.
- No single server controls the network; it is decentralized and distributed among participants.
- This approach is cost-effective because it eliminates the need for a central server, plus it promotes flexibility and adaptability by allowing easy expansion and adding new clients.
-
Three Main Pillars
- Decentralization: No single entity owns or controls the data; it's distributed among numerous nodes.
- Transparency: All transactions are recorded publicly on a shared ledger, promoting accountability.
- Immutability: Once data is recorded in a block, it cannot be altered or erased and permanently secured.
Artificial Intelligence
-
What is AI?
- AI's ability, displayed by computers and robots to perform tasks that usually require human intelligence like reasoning, learning, problem-solving, and understanding language. AI can mimic or even surpass human capabilities.
-
Need for AI
- AI facilitates human efficiency in diverse areas.
- It enhances industrial productivity and reduces errors.
- It expands the capacity for performing complex and tedious tasks.
- It fosters innovations across different sectors.
-
Major Goals for AI
- Problems Solving and Decision Making
- Analyzing large datasets and identifying patterns.
- Data-driven decisions for superior efficiency in various applications, from healthcare to finance and beyond.
- Natural Language Processing (NLP) -AI-powered comprehension and response mechanisms, mirroring human language.
- Machine Learning and Deep Learning
- Machines learning from data, making sophisticated predictions possible.
- Robotics and Automation
- Enhanced and accurate physical tasks through AI integration.
- Enhancing Healthcare and Medicine
- Improving diagnostics and treatments through AI algorithms.
- Problems Solving and Decision Making
-
Advantages
- Reduction in human error
- Precise and accurate decision-making
- Increased efficiency through automation
- Availability and accessibility in diverse environments
- Enhanced health outcomes
- Enhanced safety in hazardous environments
-
Disadvantages
- Creativity limitations: The capacity for true originality and imagination may be absent
- Emotional intelligence limitations: The ability to comprehend and respond to human emotions may be missing
- Encouraging laziness: Excessive reliance on AI and reduced active engagement.
- Privacy concerns: Risk in data breaches, corporate misuses or unlawful manipulation.
- Job displacement: Potential negative impacts on jobs, especially in routine tasks.
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History of AI
- Key developments in the field of AI over time, from early concepts to advances made in the current era.
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Levels of AI
- Different stages of AI development and their defining characteristics, from simple logic to more sophisticated machine learning models.
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Types of AI
- Categories to differentiate and classify AI systems based on their capabilities and functions, such as reactive, limited memory, and theory of mind.
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