1 - Desperately Seeking Patterns Flashcards
Who was Konrad Lorenz?
An Austrian scientist and ethologist known for pioneering studies in animal behavior, particularly imprinting.
Lorenz won the Nobel Prize for Physiology or Medicine in 1973.
What is imprinting?
The ability of many animals, including baby ducks and geese, to form bonds with the first moving thing they see upon hatching.
Lorenz famously got ducklings to imprint on him.
Which Nobel Prize did Konrad Lorenz win?
The Nobel Prize for Physiology or Medicine in 1973.
He shared this award with Karl von Frisch and Nikolaas Tinbergen.
What did Lorenz, von Frisch, and Tinbergen discover?
They made discoveries concerning organization and elicitation of individual and social behavior patterns in animals.
What can newly hatched ducklings imprint on?
They can imprint on both living creatures and inanimate objects.
Ducklings can recognize shapes and colors.
What is a linear relationship?
A relationship where the dependent variable y depends only on the independent variables x1 and x2, without any powers or products of x1 and x2.
What are coefficients or weights in a linear equation?
Constants that define the relationship between the dependent variable y and independent variables x1, x2, etc.
What does the equation y = w1x1 + w2x2 represent?
A linear equation connecting the dependent variable y to independent variables x1 and x2, where w1 and w2 are weights.
What is supervised learning?
A type of machine learning where a model learns from labeled training data to make predictions about new, unseen data.
What is regression in machine learning?
A problem-solving technique where a model predicts the value of a dependent variable based on independent variables.
What did Frank Rosenblatt invent?
The perceptron, an early algorithm for learning patterns in data.
It was the first brain-inspired algorithm that could learn from data.
What is the significance of the perceptron?
It can learn patterns in data and is guaranteed to converge on a solution in finite time under certain assumptions.
What did McCulloch and Pitts propose in their 1943 paper?
A simple model of a biological neuron and how it could implement basic Boolean logical operations.
What are dendrites?
Treelike projections of a neuron that receive inputs.
What is an axon?
A longer projection of a neuron that sends electrical signals to neighboring neurons.
What is an artificial neuron also known as?
A neurode, which combines the concepts of ‘neuron’ and ‘node’.
What basic Boolean operations can a single neurode implement?
AND, OR, NOT.
More complex operations like exclusive-OR require multiple neurodes.
What is the relationship between x1, x2, and y in the equation y = x1 + 2x2?
y is determined by the values of x1 and x2 according to the given linear equation.
What is the goal of learning weights in machine learning?
To establish a predictive model that can calculate the dependent variable y from new inputs.
What is the relationship between the availability of data and the AI revolution?
The modern AI revolution has been fueled by the availability of large amounts of annotated or labeled data.
True or False: Ducklings learn through labeled data provided by parent ducks.
False. Ducklings learn without any labeled data from parents.
What are the basic Boolean logical operations mentioned?
AND, OR, NOT
These operations are fundamental to digital computation.
In the McCulloch-Pitts model, what can x1 and x2 take on?
0 or 1
Formally, x1, x2 ∈ {0, 1}.
How is the output y of a neurode calculated?
By summing inputs and checking against a threshold θ
If sum ≥ θ, then y = 1; else y = 0.
What does the function g(x) represent in the neurode model?
The summation of the inputs
g(x) = x1 + x2 + … + xn.
What is the output of an AND gate in the neurode model?
1 if both x1 and x2 are equal to 1; otherwise, 0
For AND, θ must be set to 2.
What is the output of an OR gate in the neurode model?
1 if either x1 or x2 is 1; otherwise, 0
The threshold θ can be adjusted accordingly.
What does an inhibitory input mean in the context of a neurode?
An input can be multiplied by -1
This can result in the neurode outputting 0, regardless of other inputs.
What is the limitation of the McCulloch-Pitts neuron?
It cannot learn; θ must be hand-engineered
The neuron cannot adjust based on data.
What is the primary function of Rosenblatt’s perceptron?
To learn weights from data
This enables the perceptron to recognize patterns.
What is the significance of the term ‘perceptron’ according to Rosenblatt?
It refers to a class of models for perception and cognition
Rosenblatt regretted using a term that sounded mechanical.
What did Donald Hebb propose regarding biological neurons?
Connections strengthen when one neuron’s output consistently influences another
This is summarized as ‘Neurons that fire together wire together.’
What is Hebbian learning?
A mechanism for learning where neuron connections strengthen or weaken based on activity
It explains how biological neurons learn from experience.
What is the main difference between a perceptron and a McCulloch-Pitts neuron?
Perceptrons can learn weights and biases; MCP neurons cannot
Perceptrons use weighted sums and can handle non-binary inputs.
What does the bias term b do in a perceptron?
It adjusts the output threshold
It helps in shifting the decision boundary.
In a perceptron, what is the output when the sum is greater than 0?
y = 1
If sum ≤ 0, then y = -1.
What assumption does a perceptron make about the data it classifies?
There exists a clear, linear divide between categories
This is known as the linear separability assumption.
What happens as a perceptron learns from data?
It adjusts its weights and bias to find a separating line
The learning process involves minimizing classification errors.
What type of data points does the perceptron work with in the obesity classification example?
Body weight and height
Each data point is labeled as obese or not-obese.
What is a single perceptron unit?
An artificial neuron that takes multiple input values and produces an output based on learned weights and bias.
What does a perceptron predict?
Whether a person is classified as obese or not based on body weight and height.
What is the main challenge with perceptrons when the number of inputs increases?
A line is no longer sufficient to separate clusters in higher dimensions.
What is a hyperplane?
The higher-dimensional equivalent of a 1D straight line or a 2D plane used to separate data points.
How many input values did Rosenblatt’s Mark I Perceptron process?
400 input values corresponding to a 20x20-pixel image.
What kind of output signal did the Mark I Perceptron produce?
A signal used to discern patterns in the input image.
What is a key limitation of the perceptron?
It learns correlations between input values and output without understanding meaning.
What does identifying correlations in data imply about a perceptron’s reasoning ability?
It does not equate to thinking and reasoning.
What was a major accomplishment in building the perceptron device?
The mathematical proof that a single layer of perceptrons can find a linearly separating hyperplane.
What is necessary to understand the proof related to perceptrons?
An understanding of vectors and their role in representing data in machine learning.
True or False: The path from early perceptrons to modern AI technologies is straightforward.
False.
Fill in the blank: The Mark I Perceptron could learn to categorize letters of the alphabet encoded in _______.
[pixel values]
What type of machine was needed for some complex computations in the Mark I Perceptron?
An IBM 704.
What does minimizing prediction error involve in machine learning?
Improving the accuracy of predictions made by models.
