Human and Machine Intelligence: Implications for the ... · future of education. Gallman & Weiss,...
Transcript of Human and Machine Intelligence: Implications for the ... · future of education. Gallman & Weiss,...
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Human and Machine Intelligence: Implications for the
future of education
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Gallman & Weiss, 1969Kendrick, 1961BEA, 2010
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Knowledge is a pattern of connections
New knowledge builds on (relates to) what is already known
Innovation requires openness for new connection forming, new creations, new mashups
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“a thousand threads that lead from the
locomotive to the very beginning of the
modern world” Rosen, 2010
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Metal workers:
cylinders
SteamWheels
Motion
Transportation need
Viability
Scientific progress
Entrepreneurship
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“The process may be more like stitching together known
parts than pioneering a complete route from scratch”W. Bryan Arthur, 2006
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Maria Popova
in order for us to truly create and contribute to the world, we have
to be able to connect countless dots, to cross-pollinate ideas from
a wealth of disciplines, to combine and recombine these pieces
and build new castles.
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Humanity in transition
Digital, networked, never-forget knowledge
Blurring lines between systems and roles created in physical space
Separation from what matters
Economic churn: Elites, NorthSouth, WestEast, UBI
Rise of attention to ancient wisdom/contemplative literature
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Humanity’s largest (last?) creation
A global data skin, an exoskeleton, an ecology
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IoT
Mobiles
Ocean sensors
Environmental monitoring
Satellites
Social media
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AI/ML/DL
AI: “the science and engineering of making intelligent machines” (McCarthy, 1956)
- General (human)
- Narrow (specific to a task – such as chess)
ML: “the ability to learn without being explicitly programmed” (Samual, 1959)
NN/DL: layers in a neural network, shares attributes of human brain
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“However, for some problems this human knowledge may be too expensive, too unreliable or simply unavailable. As a result, a long-standing ambition of AI research is to bypass this step, creating algorithms that achieve superhuman performance in the most challenging domains with no human input.”
AlphaGo Zero
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China’s ”social credit” system
Rewards and punishment for behavior
Access to hotels, jobs, schools, even dating sites
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"I feel like in the past six months, people’s behaviour has gotten better and better. For example, when we drive, now we always stop in front of crosswalks. If you don’t stop, you will lose your points. At first, we just worried about losing points, but now we got used to it.”
(BI, 2018)
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“ Infant humans didn’t only regurgitate; they created, made new meaning, shared feelings…He had discovered that human learning was communal and interactive. For a robot, the acquisition of language was abstract and formulaic. For us, it was embodied, emotive, subjective, quivering with life.”
Guardian, 2018
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What will AI do for education?
Automate mundane activities
Tutors/chatbots/student support
Improve communication
Higher quality contextual feedback
Personalized and adaptive learning
Learner profiles (personal learning graphs)
Social network expansion
Content and resource exploration
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What does it mean to be human in a digital age?
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The adjacent possible
Co-evolution with the environment
Everything is moving faster – pick faster, push harder
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We are disconnected from what matters
We are teaching an increasingly “in conflict with new technological capability” part of ourselves.
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Ages of Humanity
Physical
Mental
Being: this is our new research domain. This is the future of socio-technical and wellbeing research,
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We face a knowing problem
The challenge of the ephemeral and the impact of ambiguity on human psyche
Render our world sensible and our actions within it meaningful
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AI/HI integration
Distributed technological cognition: human and technological agents.
“Society’s techno-social systems are becoming ever fasters and more computer-oriented…can generate a new behavioral regime as humans lose the ability to intervene in real time”
Johnson et al., 2013 (Nature)
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Now facing a world of human/machine distributed cognition
The algorithms and robots are part of our cognitive system
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Shift to being attributes
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Technology creates problems that only more technology can solve
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“There is a growing cry for help from graduate students across the globe who struggle with significant mental health concerns”
“Graduate students are more than six times as likely to experience depression and anxiety as compared to general population”
Evans et. Al, 2018 (Nature)
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We are teaching an increasingly “in conflict with new technological capability” part of ourselves.
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Eudaimonia
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Positive psychology
“scientific study of what goes right in life, from birth to death and all stops in between”
Peterson, 2006
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Happiness index
OECD
Empathic schools
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"The traditional scientific method, which is based on analysis, isolation, and the gathering of complete information about a phenomenon, is incapable to deal with such complex interdependencies.”
Heylighen et al, 2007
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Two cultures: An intellectual or a scientific lens on the emerging focus on wellbeing
C.P. Snow, 1959
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The atom of learning?
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Quine web of belief
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InferredBeliefs
Observations
Deep-seatedBeliefs
Source: Jklagg, VT
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Traditional science is iterative
See by looking back
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Science as usual is fine, it’s needed
But it’s not going to embrace the opportunity before us
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Networks rather than science as usual
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Why?
Speed of change
Stable systems aren’t accommodating of new approaches
Need for impact on social systems
Criticality of wellbeing in cycles of dramatic transition
Integrative nature of being
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Knowledge generation: moving to networks
Then Now
Self-contained monolith Networked (Boeing 787)
Centralized Globally networked
Built for stability Agility
Efficiency as goal Flexibility
$$ profit Multiple-bottom lines focus
Employ for life Employ for need
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Time for a new approach
Need to urgently explore the human-artificial intelligence nexus
Interest in new attributes for business success (multiple bottom lines)
Computational advances that require humans to shift contributions
Development of PS as a field and revival of contemplative/being practices through a scientific lens
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Holistic change is possible
Merging science with vision/architecting a future
HAI: a distributed cognitive system
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Change
Systems of systems
Granularity of integration and granularity of impact
Learn from traditional systems of meaning and contemplative practices
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Need for sensemaking
It’s not access to information that defines our needs today. It is more complex – making sense and acting sensibly
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Knowledge development, learning, is (should be) concerned with learners understanding relationships, not simply memorizing facts.
i.e. naming nodes is “low level” knowledge activity, understanding node connectivity, and implications of changes in network structure, consists of deeper, coherent, learning
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Exploration
Learning is the exploration of the unknown…
… not just mastery of what is already known.
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Compelling QuestionsHabitable Worlds:
Are We Alone?
Contagion:
Can We Survive?
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Astr
on
om
y
Ch
em
istr
y
Geo
log
y
Ph
ysic
s
Bio
log
y
The questions we care about don’t fit in silos
Transdisciplinary
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Smart Courses
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