Human and Machine Intelligence: Implications for the ... · future of education. Gallman &...

Human and Machine Intelligence: Implications for the

future of education

Gallman & Weiss, 1969Kendrick, 1961BEA, 2010

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

“a thousand threads that lead from the

locomotive to the very beginning of the

modern world” Rosen, 2010

Metal workers:

cylinders

SteamWheels

Motion

Transportation need

Viability

Scientific progress

Entrepreneurship

“The process may be more like stitching together known

parts than pioneering a complete route from scratch”W. Bryan Arthur, 2006

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.

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

Humanity’s largest (last?) creation

A global data skin, an exoskeleton, an ecology

IoT

Mobiles

Ocean sensors

Environmental monitoring

Satellites

Social media

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

“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

China’s ”social credit” system

Rewards and punishment for behavior

Access to hotels, jobs, schools, even dating sites

"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)

“ 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

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

What does it mean to be human in a digital age?

The adjacent possible

Co-evolution with the environment

Everything is moving faster – pick faster, push harder

We are disconnected from what matters

We are teaching an increasingly “in conflict with new technological capability” part of ourselves.

Ages of Humanity

Physical

Mental

Being: this is our new research domain. This is the future of socio-technical and wellbeing research,

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

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)

Now facing a world of human/machine distributed cognition

The algorithms and robots are part of our cognitive system

Shift to being attributes

Technology creates problems that only more technology can solve

“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)

We are teaching an increasingly “in conflict with new technological capability” part of ourselves.

Eudaimonia

Positive psychology

“scientific study of what goes right in life, from birth to death and all stops in between”

Peterson, 2006

Happiness index

OECD

Empathic schools

"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

Two cultures: An intellectual or a scientific lens on the emerging focus on wellbeing

C.P. Snow, 1959

The atom of learning?

Quine web of belief

InferredBeliefs

Observations

Deep-seatedBeliefs

Source: Jklagg, VT

Traditional science is iterative

See by looking back

Science as usual is fine, it’s needed

But it’s not going to embrace the opportunity before us

Networks rather than science as usual

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

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

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

Holistic change is possible

Merging science with vision/architecting a future

HAI: a distributed cognitive system

Change

Systems of systems

Granularity of integration and granularity of impact

Learn from traditional systems of meaning and contemplative practices

Need for sensemaking

It’s not access to information that defines our needs today. It is more complex – making sense and acting sensibly

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

Exploration

Learning is the exploration of the unknown…

… not just mastery of what is already known.

Compelling QuestionsHabitable Worlds:

Are We Alone?

Contagion:

Can We Survive?

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The questions we care about don’t fit in silos

Transdisciplinary

Smart Courses