ML21201A366
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| ML21201A366 | |
| Person / Time | |
|---|---|
| Issue date: | 07/07/2021 |
| From: | Boring R NRC/RES/DSA |
| To: | Idaho National Lab |
| Matthew D | |
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| Download: ML21201A366 (37) | |
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June 29, 2021 Ronald Laurids Boring, PhD, FHFES Introduction to Artificial Intelligence (AI) and Some of Its Basic Terminology
Is this AI?
Decoder Ring
Is this AI?
Microsoft Clippy
Is this AI?
Google Maps
Is this AI?
Apple Watch Heartrate Monitor
Is this AI?
Microsoft Power BI Sample Dashboard
Is this AI?
NuScale Power Control Room Simulator
They All Feature Applications of AI Lets Look at Some of the History and Technology Underlying AI
It all began in 1956
1956 Was a Watershed Year
- Two Congressional Hearings on Automation
- Dartmouth Summer Workshop on Artificial Intelligence We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
Birth of AI, featuring founders like Marvin Minsky, John McCarthy, Claude Shannon, Allen Newell, and Herb Simon
- Symposium on Information Theory at MIT on September 11, 1956 Birthplace of information processing theory and study of cognition Featured George Miller, Noam Chomsky, Allen Newell, and Herb Simon, and others
- Birth of AI and cognitive psychology occurred at the same time, because they were interested in the same problems Deconstructing human thinking into information allowed us to make computer models of it
1956 Was a Watershed Year
- Two Congressional Hearings on Automation
- Dartmouth Summer Workshop on Artificial Intelligence We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
Birth of AI, featuring founders like Marvin Minsky, John McCarthy, Claude Shannon, Allen Newell, and Herb Simon
- Symposium on Information Theory at MIT on September 11, 1956 Birthplace of information processing theory and study of cognition Featured George Miller, Noam Chomsky, Allen Newell, and Herb Simon, and others
- Birth of AI and cognitive psychology occurred at the same time, because they were interested in the same problems Deconstructing human thinking into information allowed us to make computer models of it
1956 Was a Watershed Year
- Two Congressional Hearings on Automation
- Dartmouth Summer Workshop on Artificial Intelligence We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
Birth of AI, featuring founders like Marvin Minsky, John McCarthy, Claude Shannon, Allen Newell, and Herb Simon
- Symposium on Information Theory at MIT on September 11, 1956 Birthplace of information processing theory and study of cognition Featured George Miller, Noam Chomsky, Allen Newell, and Herb Simon, and others
- Birth of AI and cognitive psychology occurred at the same time, because they were interested in the same problems Deconstructing human thinking into information allowed us to make computer models of it
1956 Was a Watershed Year
- Two Congressional Hearings on Automation
- Dartmouth Summer Workshop on Artificial Intelligence We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
Birth of AI, featuring founders like Marvin Minsky, John McCarthy, Claude Shannon, Allen Newell, and Herb Simon
- Symposium on Information Theory at MIT on September 11, 1956 Birthplace of information processing theory and study of cognition Featured George Miller, Noam Chomsky, Allen Newell, and Herb Simon, and others
- Birth of AI and cognitive psychology occurred at the same time, because they were interested in the same problems Deconstructing human thinking into information allowed us to make computer models of it
1956 Was a Watershed Year
- Two Congressional Hearings on Automation
- Dartmouth Summer Workshop on Artificial Intelligence We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
Birth of AI, featuring founders like Marvin Minsky, John McCarthy, Claude Shannon, Allen Newell, and Herb Simon
- Symposium on Information Theory at MIT on September 11, 1956 Birthplace of information processing theory and study of cognition Featured George Miller, Noam Chomsky, Allen Newell, and Herb Simon, and others
- Birth of AI and cognitive psychology occurred at the same time, because they were interested in the same problems Deconstructing human thinking into information allowed us to make computer models of it
1956 Was a Watershed Year
- Two Congressional Hearings on Automation
- Dartmouth Summer Workshop on Artificial Intelligence We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
Birth of AI, featuring founders like Marvin Minsky, John McCarthy, Claude Shannon, Allen Newell, and Herb Simon
- Symposium on Information Theory at MIT on September 11, 1956 Birthplace of information processing theory and study of cognition Featured George Miller, Noam Chomsky, Allen Newell, and Herb Simon, and others
- Birth of AI and cognitive psychology occurred at the same time, because they were interested in the same problems Deconstructing human thinking into information allowed us to make computer models of it
1956 Was a Watershed Year
- Two Congressional Hearings on Automation
- Dartmouth Summer Workshop on Artificial Intelligence We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
Birth of AI, featuring founders like Marvin Minsky, John McCarthy, Claude Shannon, Allen Newell, and Herb Simon
- Symposium on Information Theory at MIT on September 11, 1956 Birthplace of information processing theory and study of cognition Featured George Miller, Noam Chomsky, Allen Newell, and Herb Simon, and others
- Birth of AI and cognitive psychology occurred at the same time, because they were interested in the same problems Deconstructing human thinking into information allowed us to make computer models of it
Big Picture in Information Processing Human-System Interface (HSI)
- Computer output = human sensation and perception
- Human action = computer input
- Its a feedback loop Each step also represents a form of intelligence that may be modelled artificially
- Perception: Pattern recognition, computer vision, natural language processing
- Knowledge: Expert systems
- Actions and behaviors: Automated controllers
Big Picture in Information Processing Human-System Interface (HSI)
- Computer output = human sensation and perception
- Human action = computer input
- Its a feedback loop Each step also represents a form of intelligence that may be modelled artificially
- Perception: Pattern recognition, computer vision, natural language processing
- Knowledge: Expert systems
- Actions and behaviors: Automated controllers
How Does AI Work?
Two Types of AI
- Good Old-Fashioned AI (GOFAI)
Symbolic logic systems to represent basic elements of human thought like language, numbers, or goals Production systems featuring if-then logic
- General Problem Solver created by Newell and Simon in 1959 Cognitive modeling architectures
- Systems like Soar and ACT-R with a heavy emphasis on how humans accomplish goals Much of focus is not to create learning but to capture human-like intelligence related to how humans carry out decisions and actions
Two Types of AI
- Good Old-Fashioned AI (GOFAI)
Symbolic logic systems to represent basic elements of human thought like language, numbers, or goals Production systems featuring if-then logic
- General Problem Solver created by Newell and Simon in 1959 Cognitive modeling architectures
- Systems like Soar and ACT-R with a heavy emphasis on how humans accomplish goals Much of focus is not to create learning but to capture human-like intelligence related to how humans carry out decisions and actions
Two Types of AI
- Good Old-Fashioned AI (GOFAI)
Symbolic logic systems to represent basic elements of human thought like language, numbers, or goals Production systems featuring if-then logic
- General Problem Solver created by Newell and Simon in 1959 Cognitive modeling architectures
- Systems like Soar and ACT-R with a heavy emphasis on how humans accomplish goals Much of focus is not to create learning but to capture human-like intelligence related to how humans carry out decisions and actions
Two Types of AI
- Good Old-Fashioned AI (GOFAI)
Symbolic logic systems to represent basic elements of human thought like language, numbers, or goals Production systems featuring if-then logic
- General Problem Solver created by Newell and Simon in 1959 Cognitive modeling architectures
- Systems like Soar and ACT-R with a heavy emphasis on how humans accomplish goals Much of focus is not to create learning but to capture human-like intelligence related to how humans carry out decisions and actions
Two Types of AI
- Neural Networks Perceptron developed in 1958 as approximation of single-cell neuron By 1960s, mathematical algorithms like backpropagation developed to allow perceptrons to learn through training
- Machine learning Multiple perceptrons chained together to create neural networks
- More layers of neural networks chained to together to create deep learning
- Facilitated by greater availability of parallel computing (e.g., graphical processing units)
Two Types of AI
- Neural Networks Perceptron developed in 1958 as approximation of single-cell neuron By 1960s, mathematical algorithms like backpropagation developed to allow perceptrons to learn through training
- Machine learning Multiple perceptrons chained together to create neural networks
- More layers of neural networks chained to together to create deep learning
- Facilitated by greater availability of parallel computing (e.g., graphical processing units)
Two Types of AI
- Neural Networks Perceptron developed in 1958 as approximation of single-cell neuron By 1960s, mathematical algorithms like backpropagation developed to allow perceptrons to learn through training
- Machine learning Multiple perceptrons chained together to create neural networks
- More layers of neural networks chained to together to create deep learning
- Facilitated by greater availability of parallel computing (e.g., graphical processing units)
Two Types of AI
- Different Uses GOFAI is good at following rules and making decisions Neural networks are good at pattern recognition when trained
- Self-Driving Vehicle Example Use GOFAI for the rules of the road
- Procedural knowledge
- Control automation Neural networks used to recognize the world
- The eyes on the road
- Information automation
Two Types of AI
- Different Uses GOFAI is good at following rules and making decisions Neural networks are good at pattern recognition when trained
- Self-Driving Vehicle Example Use GOFAI for the rules of the road
- Procedural knowledge
- Control automation Neural networks used to recognize the world
- The eyes on the road
- Information automation
Very Briefly Noted Some Key Applications of AI in Nuclear Industry
Key Applications of AI in Nuclear Industry Automation
- Control automation: Using AI to control a system (or a plant, such as might be the case in a microreactor)
- Information automation: Using AI to intelligently gather information that operator needs Detection of problems such as early warning systems and condition monitoring Prediction
- Predictiveinstead of prescriptivemaintenance systems Human-System Interface
- Smart notification systems like alarm filtering
- Natural language processing for hands-free interactivity
Example Possible Automation in Nuclear Power Information Automation (Top), Control Automation (Middle), and Analog Control (Bottom)
Computerized Operator Support System (INL)
Key Applications of AI in Nuclear Industry Automation
- Control automation: Using AI to control a system (or a plant, such as might be the case in a microreactor)
- Information automation: Using AI to intelligently gather information that operator needs Detection of problems such as early warning systems and condition monitoring Prediction
- Predictiveinstead of preventativemaintenance systems Human-System Interface
- Smart notification systems like alarm filtering
- Natural language processing for hands-free interactivity
Predictive Maintenance
- Look for signs of performance degradation through sensor data Catch parts that are failing sooner than anticipated Leave perfectly good parts in operation
- Anomaly detection using machine learning
- Convey information to human Cassia Networks
Key Applications of AI in Nuclear Industry Automation
- Control automation: Using AI to control a system (or a plant, such as might be the case in a microreactor)
- Information automation: Using AI to intelligently gather information that operator needs Detection of problems such as early warning systems and condition monitoring Prediction
- Predictiveinstead of preventativemaintenance systems Human-System Interface
- Smart notification systems like alarm filtering
- Natural language processing for hands-free interactivity
Example Smart Notification System Computerized Operator Support System (INL)
Who Knows What the Future Will Bring, But AI Will Be Part of It!
ronald.boring@inl.gov