W2 Flashcards
(38 cards)
What event is often considered the official founding of the field of Artificial Intelligence?
a) The invention of the Analytical Engine by Charles Babbage
b) Alan Turing’s work on the Turing Machine
c) The Dartmouth Conference in 1956
d) The development of the first neural network by Frank Rosenblatt
The Dartmouth Conference in 1956
In the Chinese Room argument, John Searle suggests that AI systems lack:
a) Cognitive processing capabilities
b) Understanding of symbolic logic
c) True understanding or consciousness
d) The ability to pass the Turing Test
True understanding or consciousness
Which of the following best describes the Turing Test?
a) A measure of a machine’s ability to understand natural language
b) A test to determine if a machine can perform mathematical calculations
c) An experiment to see if a computer can mimic human behavior undetectably
d) A method to train neural networks using supervised learning
An experiment to see if a computer can mimic human behavior undetectably
What was one of the main reasons for the decline of AI research funding during the AI Winter?
a) Lack of computing power
b) Excessive reliance on neural networks
c) The failure of promised AI advancements to materialize
d) Insufficient government interest in AI
The failure of promised AI advancements to materialize
What is a key difference between deep learning and symbolic AI?
a) Deep learning relies on explicit rules written by humans, while symbolic AI learns from large datasets.
b) Symbolic AI mimics brain architecture, while deep learning focuses on logical rules.
c) Deep learning uses layers of neural networks to learn from data, while symbolic AI is rule-based.
d) Deep learning was developed before symbolic AI.
Deep learning uses layers of neural networks to learn from data, while symbolic AI is rule-based.
True or False: Douglas Hofstadter believes that AI reaching human-level intelligence is an immediate and inevitable outcome.
False
True or False: The Chinese Room argument suggests that passing the Turing Test would confirm true AI consciousness.
False
True or False: The Uncanny Valley describes how humans become more comfortable with robots as they look more human-like, up to a certain point.
True
True or False: Deep Blue, IBM’s chess-playing computer, achieved general intelligence by beating the world chess champion Garry Kasparov.
False
True or False: In symbolic AI, all knowledge is encoded in human-readable symbols and rules.
True
How did the rapid advancements in AI at companies like Google influence Douglas Hofstadter’s views on AI?
Hofstadter became concerned that AI was advancing so fast it could trivialize human creativity and consciousness, reducing deep human qualities to mere algorithms.
Describe John Searle’s ‘Chinese Room’ argument and explain its implications for the idea of AI consciousness.
Searle argues that simply following programmed rules doesn’t equate to understanding, suggesting AI might mimic human responses without true comprehension.
How did the concept of ‘symbolic AI’ differ from ‘subsymbolic AI,’ and what was a key limitation of each approach?
Symbolic AI relies on predefined rules, making it inflexible with new data, while subsymbolic AI learns from data but lacks transparency in its processes.
Explain the Uncanny Valley concept and provide an example of how it might affect human interactions with robots.
The Uncanny Valley describes how robots that appear almost human can feel unsettling. For example, a lifelike robot may cause discomfort if it’s close to human appearance but not quite natural.
what are Neural Networks (a.k.a. PDP or Parallel Distributed Processing a.k.a. Connectionism)
computational models inspired by the structure and function of biological neural networks in the human brain. They are designed to process information in a manner similar to how neurons in the brain work—by distributing computations across interconnected nodes (neurons) in a parallel and layered manner.
*Based on an abstract view of the neuron
*Artificial neurons are connected to form large networks
*The connections determine the function of the network
*Connections can often be formed by learning and do not need to be ‘programmed’
Is the brain a computer in the Turing sense?
- brain can compute like a computer but it doesn’t work like a computer
what did McCulloch-Pitts (1943) say abt the Neuron
- The activity of the neuron is an “all-or-none” process
- A certain fixed number of synapses must be excited within the period of latent addition in order to excite a neuron at any time, and this number is independent of previous activity and position of the neuron
- The only significant delay within the nervous
system is synaptic delay - The activity of any inhibitory synapse absolutely prevents excitation of the neuron at that time
- The structure of the net does not change with time
how do Neural networks abstract strongly from the details of real neurons
- Neglecting Conductivity Delays: Neural networks ignore the time delays in signal transmission between biological neurons, assuming instantaneous connections.
- Neural network outputs are simplified to binary values (e.g., 0 or 1) or real-valued numbers (e.g., between 0 and 1), unlike complex analog signals in real neurons.
- Net input is calculated as the weighted sum of the input signals: Neural networks calculate the input to a neuron as a weighted sum of signals, unlike real neurons, which integrate inputs in complex spatial and temporal patterns.
- Net input is transformed into an output signal via a simple function: The net input is converted to an output using simple activation functions (e.g., sigmoid or ReLU), abstracting from the intricate biochemical processes of real neurons.
wehat is the treshold function
Weighted input activations are summed and if this ‘net input’ to the neuron exceeds 0, the output activation becomes 1
what are Perceptrons
It’s the simplest form of a neural network
- Perceptrons have 2 layers w
- If the weighted sum of the inputs meets a certain threshold, the neuron “fires” (outputs 1); otherwise, it outputs 0.
- Perceptrons learn by adjusting weights based on errors in the output, but this error correction is limited to linearly separable tasks
- Perceptrons cannot solve non-linearly separable problems, the XOR problem,
- and are restricted to single-layer networks with binary outputs.
*Two-layers
*binary nodes (McCulloch-Pitts nodes) that take values 0 or 1
*continuous weights, initially chosen randomly
what is Backpropagation
Definition: Backpropagation (“backward propagation of errors”) is a learning algorithm used for training multi-layer neural networks (i.e., networks with hidden layers). It was developed to overcome the limitations of perceptrons and enable learning in complex, multi-layer networks.
Purpose: Backpropagation enables the adjustment of weights across multiple layers by calculating the error at the output layer and propagating it backward through the network. This allows the network to learn non-linear relationships and handle more complex data patterns.
How It Works: In backpropagation, the network calculates the gradient of the error with respect to each weight, adjusting weights in each layer according to their contribution to the error. This process is repeated over multiple iterations (epochs) until the network reaches an optimal set of weights that minimize the error.
Advantage: Backpropagation allows multi-layer networks to handle non-linear problems like XOR, which single-layer perceptrons cannot solve.
Learning problem to be solved
*Suppose we have an input pattern (0 1)
*We have a single output pattern (1)
*We have a net input of -0.1, which gives an output pattern of (0)
*How could we adjust the weights, so that this
situation is remedied and the spontaneous output matches our target output pattern of (1)?
*Increase the weights, so that the net input exceeds 0.0
*E.g., add 0.2 to all weights
*Observation: Weight from input node with activation 0 does not have any effect on the net input
*So we will leave it alone
Perceptron algorithm
*weight change = some small constant X error X input activation
* error = “target activation - the spontaneous output activation”,
2 limitations of perceptrons and why are they an issue
- Only binary input-output values - no continous values
-
Only two layers - cannot represent certain logical functions (XOR) - An extra layer is necessary to represent
the XOR
