Philosophy and Ethics of AI Quiz

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Questions and Answers

Which of the following statements highlights a limitation of deep learning?

Deep learning excels at high level cognitive processes.

Deep learning cannot inherently distinguish causation from correlation. (correct)

Deep learning can fully integrate prior knowledge into its processes.

Deep learning provides clear reasoning processes that can be easily understood.

What is implied by the black box problem in machine learning?

Machine learning systems can explain their reasoning in symbolic language.

The reasoning processes of machine learning systems are opaque to even their designers. (correct)

Users can easily modify the reasoning processes of machine learning systems.

Machine learning systems always provide understandable results.

Which issue is related to the problem of commonsense reasoning in machine learning?

The ability to check all possible exceptions logically.

The reliance on assumptions that may not be consistent across different contexts. (correct)

The clarity of reasoning processes in common-sense situations.

The need for non-monotonic logics that are always computationally feasible.

What does the problem of generalization indicate about trained networks?

Trained networks can fail on new tasks even if they are similar to the original task. Signup and view all the answers

Which statement best describes high level cognitive processes in relation to machine learning?

Machine learning struggles with planning and causal reasoning. Signup and view all the answers

What is one of the main focuses of the Philosophy and Ethics of Artificial Intelligence course?

Superintelligence and the value loading problem Signup and view all the answers

Which of the following best describes early logic-based approaches to AI?

They involve reasoning with symbolic formulas that represent various human-like attributes. Signup and view all the answers

Gödel’s incompleteness theorem demonstrated which of the following?

Some true statements in mathematics cannot be proven. Signup and view all the answers

What is the significance of logical languages in AI?

They facilitate reasoning about reality through inference rules. Signup and view all the answers

What was a key issue mentioned regarding the major AI paradigms?

Each paradigm has inherent limitations. Signup and view all the answers

What does practical reasoning enable AI to do?

Determine appropriate actions to achieve defined goals. Signup and view all the answers

What triggered a mathematical crisis at the beginning of the 20th century?

Discovery of various paradoxes. Signup and view all the answers

Which of the following is NOT a topic covered in the Ethics and Morality section of the course?

The history of programming languages Signup and view all the answers

What is the criterion for determining if an argument X is adjudged to be in favor of its claim?

If argument X is in a public extension based on prior communications Signup and view all the answers

What form of reasoning is facilitated through dialogue according to the content?

Distributed reasoning based on public semantics Signup and view all the answers

In a grounded dialogue game, what does it mean for Ag1 to win?

Argument X claiming p is true under grounded criteria Signup and view all the answers

What constitutes the framework in identifying valid arguments in the dialogue?

Contents of arguments publicly exchanged between agents Signup and view all the answers

What does the dialogue demonstrate about the reasoning of a claim α?

That α can be inferred from the arguments exchanged during the dialogue Signup and view all the answers

What do defeasible rules allow for in classical logic?

Inferences to be modified with new evidence Signup and view all the answers

In model theory for classical logic, what does the domain of individuals refer to?

A collection of constant symbols representing individuals Signup and view all the answers

Which type of logic incorporates modalities such as possibility and necessity?

Modal Logic Signup and view all the answers

What is the purpose of deontic modalities in Deontic Logic?

To classify statements as obligatory, permitted, or forbidden Signup and view all the answers

In the context of BDI logics, what does 'BDI' stand for?

Beliefs, Desires, Intentions Signup and view all the answers

Which statement summarizes the nature of preferred interpretations in non-monotonic logic?

They allow for certain interpretations to be favored over others. Signup and view all the answers

What does the unary function symbol 'father_of(sanjay)' represent in model theory?

The identity of Sanjay's father Signup and view all the answers

How does the concept of modalities interact with statements in modal logic?

They qualify statements with potential circumstances. Signup and view all the answers

What criterion determines if an argument X and its claim α are favored in an s dialogue game?

X is included in an s extension of the argument framework derived from publicly communicated contents. Signup and view all the answers

What advantage does the non-monotonic reasoning described provide compared to traditional reasoning methods?

It aligns more closely with human reasoning processes. Signup and view all the answers

What is the main advantage of using machine learning over traditional logic-based approaches?

Machine learning allows for automatic extraction of data. Signup and view all the answers

Which aspect of reasoning does the content emphasize as crucial when dealing with complex issues?

The integration of multiple agents' knowledge in public dialogue. Signup and view all the answers

What is the role of deep learning in machine learning?

It involves the use of multi-layer neural networks. Signup and view all the answers

How should reasoning be formalized according to the content?

As a publically visible activity involving multiple participants. Signup and view all the answers

Why is the joint reasoning of AI and humans considered important in the context provided?

It ensures that AIs make ethical decisions. Signup and view all the answers

Which limitation of logic-based approaches does machine learning effectively address?

The brittleness of the system. Signup and view all the answers

What does the 'symbol grounding problem' refer to?

The reliance on designer-defined symbolic elements rather than real-world data. Signup and view all the answers

How does deep learning enhance the learning process of a model?

By learning the optimal feature placement independently. Signup and view all the answers

Why are hand-crafted representations seen as a barrier to autonomy in AI?

They cannot adapt to changing environments. Signup and view all the answers

What is a significant characteristic of successful machine learning models?

They often utilize artificial neural networks. Signup and view all the answers

What limitation does machine learning have compared to traditional logic-based systems?

Machine learning requires extensive computational resources. Signup and view all the answers

Study Notes

The Philosophy and Ethics of Artificial Intelligence

This course covers the philosophical and ethical aspects of Artificial Intelligence (AI)
The course will cover topics such as consciousness, reasoning, communication, ethics, morality, algorithms, applications in medicine and war, superintelligence, and the value loading problem.
The course will also cover AI and human society, and review/revision.

Overview of Course

The course will cover Introduction to AI
The course will cover the concepts of intelligence and consciousness
The course will cover Reasoning and Communication
The course will cover Ethics and Morality
The course will cover Algorithms and their implications
The course will cover situations where AI is used for good or bad, like AI in Medicine, AI in War and other applications.
The course will contain the topic of Superintelligence and the value loading problem.
The course will cover the topic of AI and Human Society and will also contain a review/revision session.

Reasoning and Communication Paradigms

Course will contain the major AI paradigms
The course will cover the limitations of AI paradigms.
The course will cover argumentation and communication as a form of artificial intelligence.
The course will cover topics around argumentation and epistemology (This is not part of the assessed material)

Sources

The course will reference the Stanford Encyclopedia of Philosophy
P. M. Dung's work on acceptability of arguments, logic programming and n-person games in Artificial Intelligence
S. Modgil's work on dialogical scaffolding for human and artificial agents in Artificial Intelligence and Cognition.

Logical (Symbolic) Approaches to AI

Early AI logic-based systems involved using symbolic representations of beliefs, desires, goals and states of affairs.
Using logical language and inference rules is analogous to natural language.
The use of logic in mathematics will be examined.

Logic and the Formalisation of Mathematics

There were mathematical paradoxes at the beginning of the 20th century.
Research to prove truths in mathematics is from given sets of axioms.
Systems must be complete (provable) and consistent.
Gödel's incompleteness theorems showed that this was impossible.
Axioms such as ∝∨¬∝ and inference rules such as α∨β|α are used to deduct complex statements.
Euclid proved truths of Euclidean geometry using 5 basic axioms.

Logic and the Formalisation of Mathematics (Example)

A triangle is used to show the use of logic in mathematics.
In the example the set of edges and corners of a triangle are symbolically represented.
Axiomatic system with basic truths (axioms) are given to create a symbolic representation.
Rules of logical inference are used to prove a conclusion.

From Logic in Mathematics to Logic in AI

Proof system and model (triangle) are defined.
If a deduced statement is true about the model, the proof system is sound.
If all true statements about the model can be inferred, the proof system is complete.
Good old fashioned AI (GOFAI) used a similar methodology to mathematics.
Given a model, encode axiomatic truths, which should be consistent with beliefs, and design a proof system to derive further truths.

Logic in AI

Given an agent's goals and beliefs, the agent acts resulting in a change in the world
Then new facts are obtained and the knowledge base is updated
Agents can act and sense the world to continue this process.
How can logic achieve all of cognition?
Turing computation can be formalized using predicate logic.
Intelligence could be formalized using logic.

Monotonic and Non-Monotonic Logics

Logic in mathematics describes static models/worlds
In AI, models can change
Classical Logic is monotonic.
Real-world beliefs involve exceptions.
Rules of thumb can be used and withdrawn when exceptions arise
Non-monotonic logics are required to handle this.
Non-monotonic logics include defeasible rules, such as 'birds usually fly' as a default rule.

Non-monotonic Logics

AI uses non-monotonic logics to handle exceptions in rules.
Non-monotonic logics use defeasible rules of the form "birds typically fly".
The idea is that birds normally fly, unless there is evidence to the contrary.

Model Theory/Semantics for Logics

Model theory defines interpretations for first-order/predicate classic logic.
The interpretation includes a domain of individuals (constants) in the language.
N-ary tuples of individuals determine truth of n-ary predicates..
Mappings from sets of individuals define n-ary functions.

Modal logics expand propositional and predicate logic with modality operators.
A modality qualifies a statement, such as 'Sanjay is usually happy'
Alethic modalities deal with truth, including possibility and necessity.
Deontic modalities involve obligation, permission, and prohibition, relating to ethics.

Beliefs, Desires and Intentions (BDI)

BDI logics describe agents' mental attitudes.
Based on philosopher Michael Bratman's work on practical reasoning.
Beliefs are about the world, including the agent itself.
Desires are motivational states (e.g., to become rich).
Goals are actively pursued desires (must be consistent).
Intentions are plans to achieve goals.

Machine Learning Approaches to AI

Logic-based initially did not yield impressive results
Recent developments incorporate new algorithms, networks, and hardware.
Google DeepMind's AI systems like AlphaZero have achievements.
These include translation, language recognition, autonomous vehicles, computer vision, medical image interpretations, text generation, and deep learning robots.
A new era of success in AI is happening, with notable uses being evident.

Machine Learning- A Very Brief Review of Fundamentals

Programs can learn from experiences, improving task performance over time.
Supervised Learning uses input and output data to build a model.
Semi-Supervised Learning uses incomplete or partially labelled data.
Unsupervised Learning finds structure or patterns in the data.
Reinforcement Learning learns from feedback (rewards/penalties) in a dynamic environment.

Limitations of Logic-based GOFAI

Requiring many rules to be encoded for tasks is problematic.
Systems are brittle, and a small error leads to significant problems.
Representation and grounding have problems as rules and data must be explicitly coded.

Limitations of Machine Learning

Machine learning needs a massive dataset.
Learning from small datasets should be considered
Generalization/transfer is a problem; trained networks can perform poorly on related tasks.
High-level cognition, such as planning, reasoning, and analogy, remains a struggle.

Limitations of Machine Learning (cont.)

Black box problem: It's hard to understand why and how machine learning methods reach a conclusion.
Lack of symbolic/language-like explanations makes it hard for machine and human reasoning to be combined.
Systems struggle with common sense reasoning, which is a fundamental challenge in AI.

Problem of Commonsense Reasoning

Commonsense reasoning is a fundamental problem for machine learning and logic-based AI.
It is complex and difficult for systems to understand reasoning effectively.
Non-monotonic logics can address these issues theoretically but are complex and computationally demanding.

Argumentation and Communication

A new approach to non-monotonic reasoning, argumentation.
Argumentation is more human-like than previous approaches.
It can handle computational limitations in formalizing/executing reasoning tasks.
Integrating human and machine reasoning is done via dialogues.
This is important for understanding how AI can be ethical.

Non-monotonic Reasoning as Argumentation

Non-monotonic reasoning is about dealing with conflicting beliefs, desires or goals.
Choosing rationally amongst these is a core component of intelligence.
Argumentation and debates are examples of non-monotonic reasoning.
Proofs are analyzed as arguments with grounds/premises that support the claim.
A1 and A2 are examples of arguments, which can be 'attacked'.

Dung's theory of Argumentation

Formalizes non-monotonic reasoning as argumentation.
Define arguments and how they are attacked/supported to create an Argument Framework.
Rules are used to define a legal labelling that is valid.
A grounded/preferred labelling determines the winning arguments.

Argument Game Proof Theories

This is a way to implement Dung's theory.
The PRO and OPP play a game to evaluate an argument.
The winner determines whether an argument is in the extension.

Single Agent Non-monotonic Reasoning as Argumentation

Non-monotonic reasoning in a single agent is described as a proof theory based on an argument framework constructed from the KB.
Argumentation is used to determine if a claim is valid, which can be part of a larger inference process that could involve multiple agents.

From Single Agent Reasoning to Distributed Reasoning via Dialogue

Adaptation of argument games is employed for developing dialogical models of distributed reasoning.
The 'public' aspects of communications and reasoning are defined.
So that the claim to be demonstrated will be implied from the contents communicated during the dialogue, not just the private knowledge of an agent involved in the dialogue.

Argumentation and Communication (cont.)

This area of research offers a way to improve the reasoning of AI and human understanding.
It also offers a way to make the reasoning processes more public and visible.
This process can provide models for ethical reasoning that are more robust than current models, in particular within AI.

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Description

This quiz explores key concepts and limitations related to deep learning and artificial intelligence, focusing on philosophical and ethical considerations. Test your knowledge on topics such as the black box problem, commonsense reasoning, and cognitive processes in AI. Delve into the foundational issues that shape our understanding of machine learning and its implications.