Lec 3-4 Flashcards

1
Q

When were the Golden Years of AI? And why is it called that way?

A

1955-1974. There was lots of funding, more promises and optimism.
Symbolic AI was the prevailing method.

The end goal was Artificial General Intelligence (building human like AI using PSS)

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2
Q

What a PSS?

A

Physical implementation of system that contains symbols
Expressions consisting of symbols and their relations
Processes/rules that manipulate these expressions and are able to create new ones.

— instantiation of a symbolic AI agent

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3
Q

What is the PPSH?

A

PSS has necessary and sufficient means for general intelligent action.

Human brains are also PSS and this would mean modern computers can exhibit general intelligent action.

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4
Q

A logical agent being an “automated theorem prover”, how do we automate the answering of a question?

We have a KB and query formula and we want to know whether the KB entails the query formula

A

If the inference rules and complete and sound, we would find either the formula or the negation. If not, the algorithm will never terminate. - FOL is only semi-decidable

Refutation-based methods use proof by contradiction

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5
Q

What are refutation-based proofs?

A

Proof by contradiction. Add the negation to the KB and show it is invalid .

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6
Q

What is satisfiability?

A

We say that a formula is satisfiable if there is a model such that the model semantically entails the formula. (A given interpretation)

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7
Q

What is validity?

A

The formula is valid if every model semantically entails the formula. Therefore, if there is one model that does not satisfy it, it is not valid.

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8
Q

What is a clause?

A

A formula of the form L1 V .. V Ln where Li is a literal . No other logical connectives. All variables are universally quantified.

An atom is a predicate, a literal is an atom or the negation. A clause is a disjunction of literals.

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9
Q

What is Conjunctive Normal Form?

A

Conjunction of clauses, a list of clauses

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10
Q

What is skolemization?

A

Process of getting rid of existential quantifiers:

Replace every existentially quantified variable X with a function.
- Preserves satisfiability but not validity!

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11
Q

What is resolution?

A

Resolution involves taking two clauses with complementary literals and producing a new clause. Complementary: Input has disjunction of S other has negation of S.

New clause is the resolvent of the inputs

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12
Q

When would we say a list of clauses is unsatisfiable?

A

When the resolvent is the empty clause

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13
Q

What are the things a term can represent in a formula?

A

A constant, variable or function

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14
Q

What is a unifier?

A

A substitution of A1 and A2 if A1[0] = A1[0] (syntactically)

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15
Q

What is a Most General Unifier (MGU)

A

A unifier ) is called a MGU if for any other unifier x of A1 and A2, there is a list of substitutions such that when substituting it comes down to the A1[x] = A1[0]sub = A2[0]sub = A2[x]

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16
Q

Resolution Theorem Proving

A

To prove that a formula is valid:
1. Take the negation
2. Put this in clausule form and collect clauses in C
3. Find a resolution derivation of {} using unification where needed
- might never terminate
4. If the negation is unsatisfiable, the formula is valid

17
Q

What is a horn clause? A definite or goal clause?

A

At most one positive literal. (With exactly one it would be a definite clause, without it would be a goal

18
Q

What is SLD resolution?

A

It’s resolution applied to a definite clause and a goal clause. The result is always a goal clause.

19
Q

What was the focus of McCulloch & Pitts?

A

What logical functions could be encoded in an ANN. - Mostly theoretical

Learning an ANN was not yet considered.

20
Q

What is Hebbian Learning?

A

Associative learning - like reinforcement learning. Cells that fire together wire together.

21
Q

What was Minky’s SNARC?

A

Stochastic Neural Analog Reinforcement Calculator.

First ever ANN, learned parameters through Hebbian mechanisms.

22
Q

What are perceptron neurons?

A

Use numerical input and weights, bias term instead of threshold value. Activated by x0 with constant value of 1

23
Q

What is the Perceptron Learning Task?

A

Given a dataset, we want to train a perceptron so that it learns the association in the dataset. Find weights to make this possible (if possible)

24
Q

What does it mean for something to be linearly separable?

A

There is a separating hyperplane such that A and B lie on different sides. Encoded in a neuron with weights w.

25
Q

Finite sets are linearly separable if

A

Their closed convex hulls are disjoint

26
Q

What is Rosenblatts Universal Representation Theorem?

A

For any function F: {0,1}^n -> {0,1} there is an MLP with a single hidden layer, whose input-output relation is F. (For binary maps)

27
Q

What is pattern matching?

A

For any x, a corner of an n-dimensional hypercube, there exists a hyperplane that cuts off this corner. Furthermore, there exists a neuron that activates only x->

28
Q

Summary of MLP’s

A

Networks with multiple layers. A layer consists of neurons that feed their outputs into neurons in the next layer. A hidden layer is not directly connected to network input/oputput.

They can encode/approximate arbitrary functions. Results tend to be theoretical; show existence but no method to find them.

29
Q

What is considered the start of the first AI winter?

A

1969 Minsky published the book perceptrons about their capabilities:

  • Single neurons can’t represent XOR (nor anything else not linearly separable)
  • feedforward networks can’t count (you need recurrent connections)
30
Q

What is the Lighthill Report?

A

1973: Critical look at promises and accomplishments of AI in preceding decades. Widely cited as reason for the withdrawal of funding.

‘In no part of the field have the discoveries made so far produced the major impact that was then promised’

31
Q

What period is considered the first AI winter?

A

1974-1980