Philosophy and Artificial Intelligence 2021: Difference between revisions
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:Osama Khalil: Trolleyology: "Would you kill the fat man?" | :Osama Khalil: Trolleyology: "Would you kill the fat man?" | ||
'''''There Will Be No Singularity'': A Survey of the Argument''' | |||
:The Dreyfus case against the possibility of AGI | :The Dreyfus case against the possibility of AGI | ||
: | :The Landgrebe-Smith case against the possibility of AGI | ||
::Three Types of Impossibility: Technical, Physical, Mathematical | ::Three Types of Impossibility: Technical, Physical, Mathematical | ||
| Line 115: | Line 115: | ||
:Structure of the book: | :Structure of the book: | ||
::Part I: Properties of the Human Mind | ::Part I: Properties of the Human Mind | ||
:: | ::'''Nomological materialistic monism''' | ||
::::Alternative views on the mind-body problem | ::::Alternative views on the mind-body problem | ||
:: | ::'''Human and machine intelligence''' | ||
::::Primal intelligence | :::Capabilities | ||
:::Primal intelligence | |||
:::Objectifying intelligence | |||
:::Definitions of intelligence in AI | :::Definitions of intelligence in AI | ||
::::The Legg-Hutter definition (see Feb. 24, above) | ::::The Legg-Hutter definition (see Feb. 24, above) | ||
:::Defining ''useful'' machine intelligence | :::Defining ''useful'' machine intelligence | ||
:: | ::'''What is language?''' | ||
::::Language and intentions | ::::Language and intentions | ||
::::Speech as sensorimotor activity | ::::Speech as sensorimotor activity | ||
::::Language and dialect change | ::::Language and dialect change | ||
::The variance and complexity of human language | ::::The variance and complexity of human language | ||
:::Reading: [https://arxiv.org/pdf/1906.05833.pdf There Will Be No AGI] | :::::Reading: [https://arxiv.org/pdf/1906.05833.pdf There Will Be No AGI] | ||
::Conversation and contexts | ::'''Conversation and contexts''' | ||
:::Language production (explicit); language interpretation (implicit) | :::Language production (explicit); language interpretation (implicit) | ||
:::The Turing test | :::The Turing test | ||
| Line 136: | Line 137: | ||
:::Conversation flow and interruptions | :::Conversation flow and interruptions | ||
:::Social and ethical behaviour (see Feb. 26, above) | :::Social and ethical behaviour (see Feb. 26, above) | ||
::Can we build an AI by emulating the brain? | |||
:::David Chalmers on Brain Emulation | |||
::::Can we build an AI by some other method? | |||
:::David Chalmers on Artificial Evolution | |||
::::David J. Chalmers: [http://consc.net/papers/singularity.pdf The Singularity: A Philosophical Analysis] | |||
::::David J. Chalmers: [http://consc.net/papers/singreply.pdf The Singularity: A Reply to Commentators] | |||
==Tuesday May 18 2021 14:30 - 18:00 (Room A12) == | |||
==Tuesday May 18 2021 14:30 - 18:00 (Room A12) | |||
Student presentations | Student presentations | ||
| Line 151: | Line 152: | ||
:Peter Buttaroni: Adversarial Examples and the Deeper Riddle of Induction | :Peter Buttaroni: Adversarial Examples and the Deeper Riddle of Induction | ||
: | :'''The Limits of Mathematical Models''' | ||
:: | ::'''Models''' | ||
:::All science requires mathematical models | |||
: | :::Types of models 1: descriptive, explanatory, predictive | ||
:: | :::Types of models 2: qualitative, quantitative | ||
: | ::::All predictive models are quantitative | ||
:: | ::::Synoptic models | ||
::::Adequate models | |||
:::Computability | |||
: | :::All AI engineering requires mathematical models | ||
: | ::::Explicit and implicit mathematical models | ||
::'''Systems''' | |||
:::System elements and system interactions | |||
:::Systems are fiat entities: they are a product of delimitation | |||
:::System boundaries | |||
:::Relatively isolated systems | |||
:'''The Limits and Potential of AI'' | |||
<!-- Intentions and drivenness | |||
No emulation of animate drivenness | |||
Consciousness | |||
Searle’s wall | |||
The Lucas-Penrose argument against machine consciousness | |||
Bringsjord’s defence of machine consciousness | |||
Other approaches in philosophy of mind, computation and AI | |||
Computational theory of the mind | |||
Objectifying intelligence and theoretical thinking | |||
::Why machines will not master human language | |||
:::Neural Machine Translation | |||
:::Human language as a complex system | |||
:::Properties of the language system | |||
:::AI conversation emulation | |||
:::Challenges to machine conversation--> | |||
:::Initial utterance production | |||
<!--Modelling dialogue dynamics mathematically | |||
Mathematical models of human conversations | |||
Current state-of-the-art in dialogue systems | |||
Why conversation machines are doomed to fail | |||
Chapter 11 Why machines will not master social interaction 224 | |||
No AI emulation of social behaviour | |||
Some examples | |||
No machine intersubjectivity | |||
No machine social norms | |||
AI and legal norms | |||
No machine emulation of morality | |||
No explicit ethical agents | |||
No AGI in the kill chain--> | |||
:AI and the Mathematics of Complex Systems | |||
:[https://buffalo.box.com/v/AI-Limits-and-Impact Preliminary Slides] | |||
==Wednesday May 19 2021 14:30 - 18:00 (Room A21) First Dialogue with Jobst Landgrebe== | ==Wednesday May 19 2021 14:30 - 18:00 (Room A21) First Dialogue with Jobst Landgrebe== | ||
:AI and the Mathematics of Complex Systems | :AI and the Mathematics of Complex Systems | ||
:[https://buffalo.box.com/v/AI-Limits-and-Impact Preliminary Slides] | :[https://buffalo.box.com/v/AI-Limits-and-Impact Preliminary Slides] | ||
::Complex systems | |||
:::Comprehensive and partial models | |||
:::The scope of extended Newtonian mathematics | |||
:::Seven Properties of complex systems | |||
:::Examples of complex systems | |||
:::Human beings as complex systems | |||
::::Complex systems of complex systems | |||
:::Animate complex systems are organized and stable | |||
::'''Mathematical models of complex systems''' | |||
:::Multivariate distributions | |||
:::Adequate models for complex systems | |||
:::Predictive models of complex systems | |||
::::Why we ain’t rich | |||
:::::Example of a social fact | |||
::Approaches to complex system modelling | |||
:::Naïve approaches | |||
:::Consequences for AI applications | |||
:::Refined approaches | |||
::::Scaling | |||
:::Explicit networks | |||
::::Evolutionary process models | |||
::::Entropy models | |||
:::Complex system emulation requires complex systems | |||
==Thursday May 20 2021 12:30 - 16:00 (Room A12) Second Dialogue with Jobst Landgrebe == | ==Thursday May 20 2021 12:30 - 16:00 (Room A12) Second Dialogue with Jobst Landgrebe == | ||
:AI and the Ontology of Power | :AI and the Ontology of Power, Social Interaction and Ethics | ||
:[https://www.youtube.com/watch?v=EiBBS8ueyz4 Preliminary Video] | :[https://www.youtube.com/watch?v=EiBBS8ueyz4 Preliminary Video] | ||
| Line 179: | Line 241: | ||
:Julius Schulte: Explainable AI: How Disciplines Talk Past Each Other | :Julius Schulte: Explainable AI: How Disciplines Talk Past Each Other | ||
:Gabriel Carraretto: Backpropagation and the Brain | :Gabriel Carraretto: Backpropagation and the Brain | ||
: | :Michele Damian: Performance vs. Competence in Human–Machine Comparisons | ||
==Course Description== | ==Course Description== | ||
Latest revision as of 18:10, 3 February 2022
Barry Smith
MAP, USI, Lugano, Spring 2021
Schedule
Monday February 22 2021 14:30 - 17:15: Some examples of philosophical problems
Introduction to the class
What is computation?
What is a language
- The Turing Test and the problem of natural language production
What is consciousness?
What is will?
Can machines have a will?
What is intentionality?
Readings:
- John Searle: Minds, Brains, and Programs
- Jobst Landgrebe and Barry Smith: There is no Artificial General Intelligence
Tuesday February 23 2021 14:30 - 17:15 The Impossibility of Digital Immortality
Part One: Immortality
Transhumanism and Identity: Can we download the contents of our brains onto a computer and become immortal?
Why you cannot exist outside your body
Readings:
- Martine Rothblatt: Mind is Deeper Than Matter [TO BE SUPPLIED AT USI SITE]
- Scott Adams: We are living in a simulation
- AI and The Matrix
Part Two: Intelligence
The classical psychological definitions of intelligence are:
- A. the ability to adapt to new situations (applies both to humans and to animals)
- B. a very general mental capability (possessed only by humans) that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience
What are the essential marks of human intelligence?
For consideration in Wednesday's session: to what extent can artificial intelligence be achieved?
Readings:
- Linda S. Gottfredson. Mainstream Science on Intelligence. In: Intelligence 24 (1997), pp. 13–23.
Wednesday February 24, 2021 14:30 - 16:00: The Legg-Hutter Definition of 'Universal Intelligence'
(with Jobst Landgrebe)
Jobst Landgrebe is the founder and CEO of Cognotekt, GmBH, an AI company based in Cologne specialised in the design and implementation of holistic AI solutions. He has 16 years experience in AI field, 8 years as a management consultant and software architect. He has also worked as a physician and mathematician.
What is it that researchers and engineers are trying to do when they talk of achieving ‘Artificial Intelligence’?
To what extent can AI be achieved?
Problems with the Legg-Hutter Definition of Intelligence
Readings:
- Shane Legg and Marcus Hutter: Universal Intelligence: A Definition of Machine Intelligence
- Jobst Landgrebe and Barry Smith: Making AI Meaningful Again
Friday February 26 2021 16:30 - 18:00 AI Ethics
(with Jobst Landgrebe)
What is the basis of ethics as applied to humans?
- Utilitarianism
- Value ethics
On what basis should we build an AI ethics?
On why AI ethics is (a) impossible, (b) unnecessary
Readings:
- Moor: Four kinds of ethical robots
- Jobst Landgrebe and Barry Smith: No AI Ethics
- Crane: The AI Ethics Hoax
Monday May 17 2021 14:30 - 18:00 (Room A12) Some Philosophical Questions About AI
Student presentations
- Tommaso Soriani: Of (Zombie) Mice and Animats
- Maria Andromachi Kolyvaki: Statistical Learning Theory as a Framework for the Philosophy of Induction.
- Ismaele Affini: The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity
- Anita Buckley: The limits of machine intelligence
- Osama Khalil: Trolleyology: "Would you kill the fat man?"
There Will Be No Singularity: A Survey of the Argument
- The Dreyfus case against the possibility of AGI
- The Landgrebe-Smith case against the possibility of AGI
- Three Types of Impossibility: Technical, Physical, Mathematical
- Structure of the book:
- Part I: Properties of the Human Mind
- Nomological materialistic monism
- Alternative views on the mind-body problem
- Human and machine intelligence
- Capabilities
- Primal intelligence
- Objectifying intelligence
- Definitions of intelligence in AI
- The Legg-Hutter definition (see Feb. 24, above)
- Defining useful machine intelligence
- What is language?
- Language and intentions
- Speech as sensorimotor activity
- Language and dialect change
- The variance and complexity of human language
- Reading: There Will Be No AGI
- Conversation and contexts
- Language production (explicit); language interpretation (implicit)
- The Turing test
- Context horizon
- Social, spatial, temporal context
- Conversation flow and interruptions
- Social and ethical behaviour (see Feb. 26, above)
- Can we build an AI by emulating the brain?
- David Chalmers on Brain Emulation
- Can we build an AI by some other method?
- David Chalmers on Artificial Evolution
- David J. Chalmers: The Singularity: A Philosophical Analysis
- David J. Chalmers: The Singularity: A Reply to Commentators
- David Chalmers on Brain Emulation
Tuesday May 18 2021 14:30 - 18:00 (Room A12)
Student presentations
- Rwiddhi Chakraborty: The Myth of Hypercomputation
- Amir Sulic: Why general AI will not be realized
- Brian Pulfer: The Singularity and Machine Ethics
- Peter Buttaroni: Adversarial Examples and the Deeper Riddle of Induction
- The Limits of Mathematical Models
- Models
- All science requires mathematical models
- Types of models 1: descriptive, explanatory, predictive
- Types of models 2: qualitative, quantitative
- All predictive models are quantitative
- Synoptic models
- Adequate models
- Computability
- All AI engineering requires mathematical models
- Explicit and implicit mathematical models
- Systems
- System elements and system interactions
- Systems are fiat entities: they are a product of delimitation
- System boundaries
- Relatively isolated systems
- Models
- 'The Limits and Potential of AI
- Initial utterance production
- AI and the Mathematics of Complex Systems
- Preliminary Slides
Wednesday May 19 2021 14:30 - 18:00 (Room A21) First Dialogue with Jobst Landgrebe
- AI and the Mathematics of Complex Systems
- Preliminary Slides
- Complex systems
- Comprehensive and partial models
- The scope of extended Newtonian mathematics
- Seven Properties of complex systems
- Examples of complex systems
- Human beings as complex systems
- Complex systems of complex systems
- Animate complex systems are organized and stable
- Mathematical models of complex systems
- Multivariate distributions
- Adequate models for complex systems
- Predictive models of complex systems
- Why we ain’t rich
- Example of a social fact
- Why we ain’t rich
- Approaches to complex system modelling
- Naïve approaches
- Consequences for AI applications
- Refined approaches
- Scaling
- Explicit networks
- Evolutionary process models
- Entropy models
- Complex system emulation requires complex systems
- Complex systems
Thursday May 20 2021 12:30 - 16:00 (Room A12) Second Dialogue with Jobst Landgrebe
- AI and the Ontology of Power, Social Interaction and Ethics
- Preliminary Video
Friday May 21 2021 12:30 - 14:00 (Room A12) Concluding Survey
Student Presentations
- Giacomo De Colle: Mind Embodied and Embedded
- Rocco Felici: On Black Box Models in AI Ethics
- Julius Schulte: Explainable AI: How Disciplines Talk Past Each Other
- Gabriel Carraretto: Backpropagation and the Brain
- Michele Damian: Performance vs. Competence in Human–Machine Comparisons
Course Description
Artificial Intelligence (AI) is the subfield of Computer Science devoted to developing programs that enable computers to display behavior that can (broadly) be characterised as intelligent. On the strong version, the ultimate goal of AI is to create an artificial system that is as intelligent as a human being. Recent striking successes such as AlphaGo have convinced many not only that this objective is obtainable but also that in a not too distant future machines will become even more intelligent than human beings.
The actual and possible developments in AI open up a series of striking questions such as:
- Can a computer have a conscious mind?
- Can it have desires and emotions?
- Would machine intelligence, if there is such a thing, be something comparable to human intelligence or something quite different?
In addition, these developments make it possible for us to consider a series of philosophical questions in a new light, including:
- What is personal identity? Could a machine have something like a personal identity? Would I really survive if the contents of my brain were uploaded to the cloud?
- What is it for a human to behave in an ethical manner? (Could there be something like machine ethics? Could machines used in fighting wars be programmed to behave ethically?)
- What is a meaningful life? If routine, meaningless work in the future is performed entirely by machines, will this make possible new sorts of meaningful lives on the part of humans?
After introducing the relevant ideas and tools from both AI and philosophy, all the aforementioned questions will be thoroughly addressed in class discussions following lectures by Drs Facchini and Smith and presentations of relevant papers by the students.
Further Background Reading
- Max More and Natasha Vita-More (Eds.), The Transhumanist Reader: Classical and Contemporary Essays on the Science, Technology, and Philosophy of the Human Future, Wiley-Blackwell, 2013.
