Comparisons of the different models of the mind, by Marvin Minsky (MIT), Steven Pinker (Harvard), and Jeff Hawkins (Palm)
These are old slides from 2006 when I was doing my PhD, but since AI is a hot topic again, I thought it would be an interesting share.
The books I compare are "The society of mind" (Marvin Minsky, 1988), "How the mind works" (Steven Pinker, 1999), and "On intelligence" (Jeff Hawkins, 2004).
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxrobertad6
Chapter 7
Thinking and Intelligence
Figure 7.1 Thinking is an important part of our human experience, and one that has captivated people for centuries.
Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the
20th-century sculpture The Thinker by August Rodin, and Shi Ke’s 10th-century painting Huike Thinking all reflect the
fascination with the process of human thought. (credit “middle”: modification of work by Jason Rogers; credit “right”:
modification of work by Tang Zu-Ming)
Chapter Outline
7.1 What Is Cognition?
7.2 Language
7.3 Problem Solving
7.4 What Are Intelligence and Creativity?
7.5 Measures of Intelligence
7.6 The Source of Intelligence
Introduction
Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such
as when you’re driving? How does a person who has never seen or touched snow in real life develop an
understanding of the concept of snow? How do young children acquire the ability to learn language with
no formal instruction? Psychologists who study thinking explore questions like these.
Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person
to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of
intelligence? What does an IQ test really measure? These questions and more will be explored in this
chapter as you study thinking and intelligence.
In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this
chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider
thinking and briefly explore the development and use of language. We will also discuss problem solving
and creativity before ending with a discussion of how intelligence is measured and how our biology
and environments interact to affect intelligence. After finishing this chapter, you will have a greater
appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Chapter 7 | Thinking and Intelligence 217
7.1 What Is Cognition?
Learning Objectives
By the end of this section, you will be able to:
• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it
possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The
brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet,
you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one
facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses
the processes associated with perception, knowledge, problem solving, judgment, langu.
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxmccormicknadine86
Chapter 7
Thinking and Intelligence
Figure 7.1 Thinking is an important part of our human experience, and one that has captivated people for centuries.
Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the
20th-century sculpture The Thinker by August Rodin, and Shi Ke’s 10th-century painting Huike Thinking all reflect the
fascination with the process of human thought. (credit “middle”: modification of work by Jason Rogers; credit “right”:
modification of work by Tang Zu-Ming)
Chapter Outline
7.1 What Is Cognition?
7.2 Language
7.3 Problem Solving
7.4 What Are Intelligence and Creativity?
7.5 Measures of Intelligence
7.6 The Source of Intelligence
Introduction
Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such
as when you’re driving? How does a person who has never seen or touched snow in real life develop an
understanding of the concept of snow? How do young children acquire the ability to learn language with
no formal instruction? Psychologists who study thinking explore questions like these.
Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person
to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of
intelligence? What does an IQ test really measure? These questions and more will be explored in this
chapter as you study thinking and intelligence.
In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this
chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider
thinking and briefly explore the development and use of language. We will also discuss problem solving
and creativity before ending with a discussion of how intelligence is measured and how our biology
and environments interact to affect intelligence. After finishing this chapter, you will have a greater
appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Chapter 7 | Thinking and Intelligence 217
7.1 What Is Cognition?
Learning Objectives
By the end of this section, you will be able to:
• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it
possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The
brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet,
you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one
facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses
the processes associated with perception, knowledge, problem solving, judgment, langu ...
A book review on the book of John Adair,titled Effective decision making presented by Dr. Helal Uddin Ahmed, Bangladeshi doctor works in psychiatry, BSMMU, Bangladesh.
Summary of the Persuasive Technology 2009 conference, presented at the Mini-UPA (Boston UPA chapter) conference on May 26, 2009 by Carolyn Snyder, PT 09 attendee.
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB
Comparisons of the different models of the mind, by Marvin Minsky (MIT), Steven Pinker (Harvard), and Jeff Hawkins (Palm)
These are old slides from 2006 when I was doing my PhD, but since AI is a hot topic again, I thought it would be an interesting share.
The books I compare are "The society of mind" (Marvin Minsky, 1988), "How the mind works" (Steven Pinker, 1999), and "On intelligence" (Jeff Hawkins, 2004).
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxrobertad6
Chapter 7
Thinking and Intelligence
Figure 7.1 Thinking is an important part of our human experience, and one that has captivated people for centuries.
Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the
20th-century sculpture The Thinker by August Rodin, and Shi Ke’s 10th-century painting Huike Thinking all reflect the
fascination with the process of human thought. (credit “middle”: modification of work by Jason Rogers; credit “right”:
modification of work by Tang Zu-Ming)
Chapter Outline
7.1 What Is Cognition?
7.2 Language
7.3 Problem Solving
7.4 What Are Intelligence and Creativity?
7.5 Measures of Intelligence
7.6 The Source of Intelligence
Introduction
Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such
as when you’re driving? How does a person who has never seen or touched snow in real life develop an
understanding of the concept of snow? How do young children acquire the ability to learn language with
no formal instruction? Psychologists who study thinking explore questions like these.
Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person
to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of
intelligence? What does an IQ test really measure? These questions and more will be explored in this
chapter as you study thinking and intelligence.
In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this
chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider
thinking and briefly explore the development and use of language. We will also discuss problem solving
and creativity before ending with a discussion of how intelligence is measured and how our biology
and environments interact to affect intelligence. After finishing this chapter, you will have a greater
appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Chapter 7 | Thinking and Intelligence 217
7.1 What Is Cognition?
Learning Objectives
By the end of this section, you will be able to:
• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it
possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The
brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet,
you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one
facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses
the processes associated with perception, knowledge, problem solving, judgment, langu.
Chapter 7Thinking and IntelligenceFigure 7.1 Thinking .docxmccormicknadine86
Chapter 7
Thinking and Intelligence
Figure 7.1 Thinking is an important part of our human experience, and one that has captivated people for centuries.
Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the
20th-century sculpture The Thinker by August Rodin, and Shi Ke’s 10th-century painting Huike Thinking all reflect the
fascination with the process of human thought. (credit “middle”: modification of work by Jason Rogers; credit “right”:
modification of work by Tang Zu-Ming)
Chapter Outline
7.1 What Is Cognition?
7.2 Language
7.3 Problem Solving
7.4 What Are Intelligence and Creativity?
7.5 Measures of Intelligence
7.6 The Source of Intelligence
Introduction
Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such
as when you’re driving? How does a person who has never seen or touched snow in real life develop an
understanding of the concept of snow? How do young children acquire the ability to learn language with
no formal instruction? Psychologists who study thinking explore questions like these.
Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person
to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of
intelligence? What does an IQ test really measure? These questions and more will be explored in this
chapter as you study thinking and intelligence.
In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this
chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider
thinking and briefly explore the development and use of language. We will also discuss problem solving
and creativity before ending with a discussion of how intelligence is measured and how our biology
and environments interact to affect intelligence. After finishing this chapter, you will have a greater
appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Chapter 7 | Thinking and Intelligence 217
7.1 What Is Cognition?
Learning Objectives
By the end of this section, you will be able to:
• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it
possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The
brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet,
you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one
facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses
the processes associated with perception, knowledge, problem solving, judgment, langu ...
A book review on the book of John Adair,titled Effective decision making presented by Dr. Helal Uddin Ahmed, Bangladeshi doctor works in psychiatry, BSMMU, Bangladesh.
Summary of the Persuasive Technology 2009 conference, presented at the Mini-UPA (Boston UPA chapter) conference on May 26, 2009 by Carolyn Snyder, PT 09 attendee.
A quick overview of the seed for Meandre 2.0 series. It covers the main motivations moving forward and the disruptive changes introduced via the use of Scala and MongoDB
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0Xavier Llorà
One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.
Large Scale Data Mining using Genetics-Based Machine LearningXavier Llorà
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them.
This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...Xavier Llorà
Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.
Linkage Learning for Pittsburgh LCS: Making Problems TractableXavier Llorà
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...Xavier Llorà
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...Xavier Llorà
Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Searle, Intentionality, and the Future of Classifier Systems
1. Illinois Genetic Algorithms Laboratory
Department of General Engineering
University of Illinois at Urbana-Champaign
Urbana, IL 61801.
Searle, Intentionality, and the
Future of Classifier Systems
David E. Goldberg
Illinois Genetic Algorithms Laboratory
University of Illinois at Urbana-Champaign
Urbana, IL 61801
deg@uiuc.edu
2. 1980 v. Now
Remember thinking how cool LCSs
were.
Just apply them to gas pipelines
and voila, all AI problems of
Western Civilization would be
solved.
Started to ask John for examples
of successful application.
Found out that I was in the
middle of an interesting idea, not
a working computer program.
John H. Holland (b. 1929)
3. Roadmap
Are we happy with LCSs?
What’s Searle got to do with it.
Revisiting the Chinese room.
Art Burkes had it right.
Designing a conscious computer.
Searlean program for LCSs:
Computational consciousness not impossible.
–
From consciousness to intentionality.
–
Intentionality and beyond.
–
What are we missing?
What should we do?
4. Are We Happy With LCSs?
Have made progress:
Increasingly competent, solve hard problems
–
quickly reliably and accurately.
Principled manner.
–
But don’t seem very intelligent:
Do what we tell them.
–
Not autonomous in any serious sense.
–
Our discussions are largely technical.
–
Are we focused on right problems?
–
5. What’s Searle Got to Do With It?
Mill Prof of Philosophy of Berkeley.
Philosopher of language and mind.
Early work took off from Austin’s work on
speech acts.
Searle is Darth Vader of artificial
intelligence.
Notorious Chinese Room argument has
always puzzled me.
In many ways, Searle is high philosophical
priest of emergence.
John R. Searle (b. 1932)
Rejects dualism & materialism.
Couldn’t understand how he could miss
possibility of more than mere systactical
translation.
6. The Chinese Room Argument
Strong AI is not possible on a computer.
Monolingual English speaker in a room with
Chinese writing (story)
–
2nd Chinese symbols (questions)
–
Instructions in English for relating first set of symbols
–
to second.
3rd set of Chinese symbols (answers)
–
English speaker does not understand Chinese even
if answers are indistinguishable from those of
Chinese speaker.
7. Cracks in the Chinese Room
Mind, Language & Society,
Basic Books, 1998, p. 53.
“When I say that the brain
is a biological organ and
consciousness a biological
process, I do not, of course,
say or imply that it would
be impossible to produce an
artificial brain out of
nonbiological materials.”
8. More Searle
“The heart is also a biological organ, and the
pumping of blood a biological process, but it is
possible to build an artificial heart that pumps
blood. There is no reason, in principle, why we
could not similarly make an artificial brain that
causes consciousness.”
Searle was complaining about direct approach to
intelligence.
Without consciousness and intentionality there
cannot be intelligence.
How do we create an intelligent, conscious being?
9. Arthur Burks Had Interesting Take
Robots and Free
Minds, University of
Michigan, 1986.
“Tonight I will
advocate the thesis: A
FINITE
DETERMINISTIC
AUTOMATON CAN
PERFORM ALL
NATURAL HUMAN
FUNCTIONS.”
10. Chapter 5: Evolution and Intentionality
“The course of biological evolution from cells
to Homo sapiens has been a gradual
development of intentional systems from
direct-response systems.”
“The [intentional] system contains a model of
its present status in relation to its goal and
regularly updates that model on the basis of
the information it receives. Finally, it decides
what to do after consulting a strategy, which
has value assessments attached in to various
alternative courses of action.”
11. CS-1 Had Bio/Psycho Roots
CS-1 had reservoirs for
hunger and thirst (Holland
& Reitman, 1978).
Schemata processors
paper had reservoirs, too
(Holland, 1971).
CS-1 worked in maze
running task.
But design was Lockean.
Tabula rasa for everything
except rule firing,
apportionment of credit,
and rule discovery.
Is this enough?
Thesis: Can’t take shortcut
around consciousness and
intentionality.
12. So You Want a Conscious Computer
What does this mean?
Consciousness is complex, emergent
phenomenon.
How can we design it?
Don’t throw pieces together and hope for
the best.
My experience: Emergent phenomena
emerge when (a) key elements are present
and (b) system tuned properly.
Consider more Searle.
13. Shooting for C Not Crazy
Shooting for GA competence was crazy.
Have accomplished.
How:
Considered essential elements.
–
Built qual/quant theories of how they worked.
–
Designed until limits of performance achieved.
–
Can do the same for
consciousness/intentionality!!
14. Searle’s Greatest Hits
Mind as biological phenomenon.
Function of consciousness.
Features of consciousness.
How the mind works: Intentionality.
The good stuff comes from intentionality:
Language & other institutional fact.
What are we missing?
15. Mind as Biology
Consciousness is the primary feature of
minds.
3 features of consciousness:
Inner: in body and in sequence of events.
–
Qualitative: certain way they feel.
–
Subjective: first person ontology (does not
–
preclude objective epistemology).
Enormous variety of consciousness: smell a
rose, worry about income taxes, sudden
rage about driver, etc.
16. Functions of Consciousness
What does it do? What is survival value?
What doesn’t it do for our species?
Consciousness is central to our survival.
All actions a result of conscious thought
followed by action.
17. Consciousness, Intentionality, & Causation
Represent world, and act on representations.
Intentional causation: Not billiard ball causation.
Not all consciousness intentionally causal, but much
is.
Should be best understood; are we not in touch
with it always? Descartes’s error.
Yet difficult to describe: Can describe objects,
moods, thoughts, but not C itself.
Problems:
Not itself an object of observation (consciousness
–
observes but is not observed).
Tradition of separating mind/body: dualism.
–
18. Features of C
Ontological subjectivity.
1.
C comes in unified form. Thinking and feeling go on
2.
at same time in same field of C: Vertical & horizontal.
C connects us to world (Tie to intentionality).
3.
C states come in moods.
4.
Always structured.
5.
Varying degrees of attention.
6.
C is situated.
7.
Varying degrees of familiarity.
8.
Refer to other things
9.
Always pleasurable or unpleasurable
10.
19. How the Mind Works: Intentionality
Primary evolutionary role of C is to relate
us to environment.
Cannot eliminate intentionality of mind by
appealing to language; already
intentionality of the mind.
Searle: Urge to reduce it to something else
is faulty.
DEG: As designers we need to reduce it to
something and then find conditions of
emergence among those things.
20. Intentionality as Biology
Thirst, hunger as basic, causing desire to
drink or eat.
Once this granted, camel nose under the
tent, intentions based on other sensory.
Isn’t reality “confirmed” by our “success” in
achieving intentional goals over and over
again.
21. Structure of Intentional States
Intentionality as way mental states are directed at
objects & states of affairs.
Can be directed at things that don’t exist?
How can this be?
Distinguish between type of intentional state and
content.
Content: rain; Types: hope, believe, fear rain.
Structural features:
Direction of fit
–
Conditions of satisfaction
–
22. Direction of Fit
Term: from Austin, foreshadowed by Wittgenstein,
examples Anscombe.
Anscombe’s lists:
Shopping list: Beer, butter, bacon. Husband matches
–
world to list.
Detective’s list: Follows shopper, “beer, butter,
–
bacon,” matches list to world.
Not all intentional states like this: e.g. when you
are sorry, assume match between mind and world.
Intentional state is null.
23. Conditions of Satisfaction
Beliefs can be true or false.
Goals can be achieved or not.
Easier to understand in terms of speech acts.
Have 5 illocutionary points or types:
Assertive: commit to the truth.
–
Directive: direct hearer to do something.
–
Commissive: speaker promises to do something.
–
Expressive: speaker expresses opinion about state of
–
the world.
Declarations: speaker creates something with
–
utterance.
24. Intentional Causation
Intend to move body body moves:
Example of intentional causation.
Differs from billiard ball or Humean causation.
Self-referential: intend to move body, body moves
because I intended then intentional causation.
Critical to distinguishing natural versus social
sciences.
Intentional explanations not deterministic: Could
have done otherwise gap is free will.
25. Good Stuff from Intentionality
Searle goes on to talk about language and
institutional facts (money, college degrees,
etc.).
Disappointment with LCS is it can’t get to
the good stuff.
Can’t do language.
Can’t form contracts.
Can’t create new institutional fact.
26. Construction of Social Reality
Need to clarify observer-independent &
observer-dependent features of the world.
Need 3 new elements:
Collective intentionality.
–
Assignment of function.
–
Constitutive rules
–
27. Observer Independent v. Dependent
Many features of the world independent of
our observations of them: observer
independence.
Many observer dependent: Something a
characteristic because of observer
judgment, but not relative to others.
OI vs. OD more important than mind-body.
DEG aside: Isn’t it dualism in the back door
though?
28. Collective Intentionality
Need the notion of “we intend together.”
Attempts to reduce to individual intention are
complex.
Existence of biological organisms with collective
intentionality suggests CI is a primitive.
DEG aside: Are social insects intentional in Searlean
sense? Could be that social affiliation is primitive,
certain behaviors hard wired. Then, CI results from
(a) naming the group, (b) attributing intention to it
(as-if intentionality), and (c) treating the as-if as
real.
29. Assignment of Function
Use of objects as tools:
Monkey uses stick to get banana.
–
Man sits on rock.
–
Physical existence facilitates function, but
function is observer relative.
All function assignment is observer relative.
30. Constitutive Rules
How to distinguish between brute facts and
institutional facts.
Types of rules:
Some rules regulate: “Drive on right side of road.”
–
Some rules regulate and constitute: Rules of chess
–
both regulate conduct of game and create it.
Constitutive rules have form: X counts as Y in C.
“Move two and over one” counts as a knight’s
move in Chess.”
31. Simple Model of Construction of Social Reality
Strong thesis: All institutional reality explained by 3
things:
Collective intentionality.
–
Assigned function wall keeps people out
–
physically, but low fence or boundary marker keeps
people out by convention.
Constitutive rules.
–
Money example: Evolution from valuable
commodity to fiat currency.
Institutional reality powerful: X counts as Y in C can
be iterated and stacked forming powerful network
of institutional facts.
32. What Are We Missing?
Do not have C-machines.
Searle’s 10:
Ontological subjectivity.
1.
C comes in unified form.
2.
C connects us to world.
3.
C states come in moods.
4.
Always structured.
5.
Varying degrees of attention.
6.
C is situated.
7.
Varying degrees of familiarity.
8.
Refer to other things
9.
Always pleasurable or unpleasurable
10.
33. Unity Missing
Can argue that we have vertical unity in
message board.
Do not have horizontal unity.
My first proposal recommended
modifications to permit time series.
Modifications to rules.
Modification to the boards.
34. Moods & Pleasant/Unpleasant Missing
This is big.
Emotions are “engagement with the world”
(Solomon).
Necessary for judgment and values.
Don’t want a simulation.
Emotions:
Physiological component
–
Judgmental component
–
35. Other Things Missing
Attention
Gestalt structure
Situatedness & familiarity
Refer to other things (may have this)
36. What Should We Do?
Stuff we’ve gotten right: Sensors, association,
models (anticipation), learning
Can’t continue to work on same thing.
No serious architectural changes proposed to LCS.
Why?
Need:
Emotions: As judgments, source of values, and
–
arbiter of attention.
Multiple boards: As source of difference and
–
similarity. Main hope of quality of consciousness &
unity.
Center of intention rooted in “biological needs.”
–
37. How Do We Break This Down?
Tough problem.
If C is complex building block,
what are minimal essential
elements to achieve.
How do we know we’ve achieved
it (first person ontology, third
person epistomology)?
Sets of tests and experiments.
What theory needed to set
parameters of C?
Not unlike approach that cracked
innovation
38. Summary & Conclusions
Have accomplished quite a bit in classifier
systems.
Many of our questions are technical.
Deeper questions about whether we’re
attacking the right questions.
Need consciousness and intention to get
the “good stuff” of intelligent behavior.
Wrestling with Searle’s categories not a bad
place to start.