Summit George Siemens Becoming an AI University
Summary
TLDRGeorge Simmons discusses the transformative potential of AI in higher education, emphasizing the need for universities to adapt and integrate AI to remain relevant. He outlines key areas where AI can enhance learning, such as personalized education and administrative efficiency, while also highlighting challenges like ethical considerations and curriculum development. Simmons advocates for an 'AI-first' university approach, where AI is embedded in all organizational aspects, from infrastructure to research, to better prepare students for a rapidly evolving world.
Takeaways
- 🧠 George Simmons discusses the intersection of human and artificial cognition in knowledge processes and the role of AI in education.
- 🎓 Simmons is a co-founder, chief scientist, and architect of SNH use human system, which focuses on the impact of AI on learning and wellness.
- 🤖 He critiques the higher education sector's response to AI, suggesting a misjudgment and a need for more proactive engagement with AI technologies.
- 📚 Simmons highlights four main topic areas: literature on AI and learning, the intersection of human and machine cognition, current AI trends, and the implications for universities.
- 🌐 He emphasizes the importance of networks in human cognition and how AI is becoming a node within our cognitive networks, affecting how we learn and process information.
- 🔬 Research suggests AI's role in learning is not just as a tool but as a transformative agent that requires a re-evaluation of human learning theories.
- 🏫 The practical applications of AI in classrooms are primarily adaptive systems, personalization, profiling, prediction, and assessment, with tutoring emerging as a significant area.
- 🛠 Challenges introduced by AI in education include ethical concerns, curriculum development, and infrastructure questions.
- 🔑 The benefits of AI in education include personalized learning, improved administrative processes, and more effective assessment methods.
- 🔮 Looking forward, Simmons envisions an 'AI-first' university where AI is integrated into all organizational aspects, from infrastructure to research.
- 🌟 The future of education with AI involves addressing the ethical dynamic, building institutional AI capabilities, and fostering a multi-institutional collaboration for data sharing and learning.
Q & A
What is the main topic of George Simmons' discussion?
-The main topic of George Simmons' discussion is the significant misjudgment by higher education in utilizing AI for innovation and change within the university sector.
What are the four distinct topic areas Simmons plans to address in his talk?
-Simmons plans to address literature around AI and learning, the intersection between human and machine cognition, current trends in AI, and the implications of AI for universities, including potential changes and areas of priorities.
How does Simmons describe the impact of new technology on society's emotional and mental health?
-Simmons describes the impact as a 'spacing effect,' where new technology like social media initially connects people but can lead to disconnection and negative mental health effects due to algorithmic distortion and propaganda.
What is the role of AI in the cognitive network according to Simmons?
-According to Simmons, AI is not just a tool or resource but a node within our cognitive network, which has significant implications for education and how we understand and interact with knowledge.
What is the importance of considering AI as a mediating and transforming agent in human learning?
-Considering AI as a mediating and transforming agent in human learning is important because it necessitates a re-evaluation of our understanding of human learning and knowledge growth, taking into account AI's potential to change how we engage with and process information.
What are some of the practical applications of AI in classrooms as discussed by Simmons?
-Some practical applications of AI in classrooms include adaptive systems and personalization, profiling and prediction of student outcomes, assessment and evaluation, and tutoring.
What are the challenges introduced by AI in the educational landscape?
-Challenges introduced by AI in the educational landscape include ethical dynamics, ensuring the integrity and security of student data, curriculum development to incorporate AI effectively, and infrastructure questions related to AI deployment.
What does Simmons suggest is the key to successfully integrating AI into the university system?
-Simmons suggests that getting the data and related architecture right is key to successfully integrating AI into the university system, emphasizing the importance of institutional capability, leadership, policy, governance, and adaptive teaching methods.
How does Simmons view the future of AI in relation to human interaction?
-Simmons views the future of AI as one where AI will impact every aspect of university operations, potentially leading to an 'AI first' university that integrates AI into all organizational aspects, from infrastructure to research.
What are some of the emerging trends in AI that Simmons highlights?
-Some emerging trends in AI that Simmons highlights include the prevalence of AI in various technologies, the growth of open-source LLMs, the integration of AI with traditional robotics, the rise of wearable devices with AI capabilities, and the development of platform technologies that simplify AI development.
What is the significance of the term 'learner co-pilot' mentioned by Simmons?
-The term 'learner co-pilot' signifies the idea of AI providing personalized support to learners, acting as a guide or co-pilot in their educational journey, enhancing the learning experience by meeting individual needs beyond just cognitive aspects.
Outlines
🤖 AI's Role in Higher Education Transformation
George Simmons discusses the underutilization of AI in universities and its potential for innovation. He emphasizes the need for higher education to adapt and embrace AI to ensure student success and institutional relevance in a rapidly changing world. Simmons outlines four key topic areas for discussion: literature on AI and learning, the intersection of human and machine cognition, current AI trends, and the implications for universities. He also addresses societal challenges, such as emotional and mental health, and the impact of technology on social connection and well-being.
🧠 Rethinking Human and Artificial Cognition
The speaker explores the concept of cognition beyond the brain, highlighting the role of networks and AI systems in our cognitive processes. He argues that AI should be viewed as a node within our cognitive network rather than a separate tool or resource. Simmons discusses the importance of understanding AI's impact on learning, particularly in areas of metacognition, emotion, trust, and social practices. He also touches on the need to re-evaluate our understanding of human learning and knowledge growth in the context of AI integration.
📚 Practical Applications and Challenges of AI in Education
Simmons reviews the practical applications of AI in educational settings, such as adaptive systems and personalization, profiling and prediction, assessment and evaluation, and the emerging role of AI tutors. He acknowledges the benefits of AI in enhancing personalized learning and administrative efficiency but also warns of the significant ethical and methodological challenges that accompany AI integration. The speaker identifies research gaps and the need for a more holistic understanding of AI's impact on student learning and engagement.
🛠️ AI's Impact on University Operations and Infrastructure
The speaker discusses the transformative potential of AI across all university operations, from knowledge generation to communication. He identifies trends in AI development, such as the rise of generative AI, multimedia integration, and the growth of open-source AI tools. Simmons also addresses the implications of AI for university infrastructure, emphasizing the importance of data architecture and the need for universities to develop a robust AI strategy that includes ethical considerations and curriculum development.
🌐 The Future of AI in University Systems
Simmons envisions an AI-first university where AI is integrated into all organizational aspects, from infrastructure to admissions, teaching, assessment, curriculum, and research. He outlines six areas of focus for universities to consider when adopting AI, including building institutional AI capability, leadership and governance, adaptive and responsive teaching methods, and accelerating research through AI utilization. The speaker stresses the importance of getting data architecture right and the potential for multi-institution collaboration in AI development.
🏛️ Redefining Education in the Age of AI
In the concluding remarks, Simmons reflects on the broader implications of AI for education, suggesting a shift from teaching knowledge to developing human beings who can navigate complexity and engage with non-human forms of intelligence. He calls for proactive measures to anticipate and mitigate potential harmful effects of AI while leveraging its benefits to enhance learning and societal engagement.
Mindmap
Keywords
💡Artificial Cognition
💡AI in Education
💡Misjudgment
💡Adaptive Systems
💡Metacognition
💡Ethical Dynamics
💡Generative AI
💡Personalized Learning
💡AI-First University
💡Digital Ecosystem
💡Pedagogical Approach
Highlights
George Simmons is a co-founder, chief scientist, and architect of SNH use human system, focusing on the intersection of human and artificial cognition in knowledge processes.
Higher education has shown a fatigued response to AI's potential for innovation in the university sector over the last decade.
AI should be seen as a mechanism to ensure student success and keep universities relevant in a rapidly changing world.
Literature on AI and learning suggests that AI can enhance learning processes through personalized systems and adaptive learning.
The intersection between human and machine cognition is a co-creation process, not an antagonistic one.
AI's role in education is not as a tool but as a node within our cognitive network, affecting how we understand and engage with knowledge.
Research indicates that AI can help in complex problem-solving by integrating human and artificial cognition effectively.
Adaptive systems and personalization are the primary applications of AI in current educational settings.
Ethical dynamics, curriculum development, and infrastructure questions are significant challenges introduced by AI in education.
AI's impact on student focus and engagement shows that students rely on AI rather than learning from it, indicating a need for pedagogical adaptation.
The prevalence of AI in various sectors suggests that it will impact all aspects of a university's operations.
AI tools are beginning to unbundle the university system, offering knowledge generation and communication through various providers.
The concept of an 'AI first University' involves AI in all organizational aspects, from infrastructure to research.
Building institutional capability with AI is crucial, including understanding AI's functions and having the technical capacity to utilize it.
Adaptive and responsive teaching methods are becoming prevalent, with AI providing personalized support for learners.
The acceleration of research through AI utilization is changing the landscape of academic inquiry.
Data architecture and institutional infrastructure are critical for successful AI deployment in universities.
Multi-institution collaboration and data sharing are recommended for universities to learn from each other in the AI space.
Education is moving from teaching knowledge to developing human beings and helping them navigate complexity and engage with AI.
Transcripts
[Music]
he's George CS um I think they are
getting ready with all the last details
before taking the stage so let me
briefly introduce him uh he researches
how human and artificial cognition
intersect in knowledge processes he's
also a co-founder a chief scientist and
architect of SNH use human system that
is an organization building resources to
to respond to systems impact on AI on
learning and also Wellness I think we're
ready here now are you jge okay please
come to the stage welcome him George
Simmons thanks so much for joining
us uh good morning and uh appreciate the
opportunity to spend some time talking
about what I think is a significant
misjudgment on the part of higher
education over the last certainly
several years but likely going back well
over a decade and that is a somewhat
fatigued and even baguer response to AI
as a mechanism for changing and
innovating the university sector as a
whole so I'm going to talk through what
I think is happening and what I think we
need to do as universities to be more
responsive and more capable to utilize
AI as again a mechanism M for ensuring
our students are successful but also
ensuring that universities continue to
remain relevant in a pretty quickly
changing world I'm going to talk about
four distinct topic areas the bulk of
the talk I'm going to look at some of
the literature around Ai and learning
and this is just to give you a bit of a
sense on what do we know from literature
that works well in learning and learning
related processes I'm going to build a
little bit on what Charles was just
talking about which is the intersection
between human and machine it's a
co-creation process not necessarily
antagonistic process I'm going to talk
very briefly two slides worth about AI
specifically and I'm just going to
detail what it is that AI does and what
some of the current trends are that
we're seeing in AI I assume everyone in
the audience doesn't need the 500th what
is AI primer so I'm just going to talk
about what's happening right now
specifically around llms that have an
educational implication I'm going to
from there go a little bit about what
does this mean specifically from a
university lens and how universities
might change and then finally I'll
present sort of a six area of priorities
that universities need to pay attention
to if they want to start getting more
actively involved in the AI conversation
so to get
started we're at an interesting time in
history in that we've are sort of at the
tail end of an extended period of
emotional turmoil as a society uh we
have seen a significant increase in
escalation in areas of emotional need or
in areas of loneliness and mental health
impacts are certainly growing not only
limited to the effects of uh the
pandemic but just stats and indicators
prior to the pandemic that said hey as
people were not doing okay emotionally
and mentally some of the systems that
Society has created for us aren't
serving all of us equitably and that's a
significant Challenge and so there's
ways that we need to be better in how we
support and engage with Society r large
not just with individual learners but
what happens is each time we have a new
technology we introduce a bit of a
spacing effect and that spacing effect
means social media as an illustration
initially came on and it allowed us to
connect with people from around the
world but nowadays that connection is
actually producing disconnection and so
with initially gave us the opportunity
to do new things with new groups of
people suddenly became become at odds
and in conflict with new groups of
people so the way social media has been
deployed by itself was naive and
effective but once you make it available
for algorithmic Distortion and for
propaganda suddenly it becomes harmful
and actually disruptive to the system as
a whole and so we need to keep that in
the back of our minds because the
lessons of social media on mental health
and on society Wellness will be almost
insignificant compared to the threat and
the risk that AI will pose into the
public conversational sphere so each new
wave of Technology forces us to evaluate
the spaces that we occupy and how we
remain human in those environments and
that's one of the reasons I particularly
appreciate the uh the theme of the
education Forum here around that human
component in AI settings so when you
look at traditional learning literature
there's been a long period of
acknowledging that thinking and learning
doesn't just happen in our brains right
there's a range of theorists that from
embodied cognition to distributed
cognition to some externalization of
Concepts and ideas we're constantly
putting human knowledge into physical
things or objects or concepts in the
world and most established theorists and
philosophers would argue that you are
intelligent as a function of the
networks that you exist within and those
networks traditionally have been tools
and resources we've created such as
books and related artifacts but
increasingly now they're starting to
become systems that are AI compliant or
AI enabled so when I think of artificial
intelligence to me it's not a tool it's
not a resource that we use it is a a
node within our cognitive Network and
that has significant implications
educationally and that's because as a
species we don't exist in these systems
as isolated entities the best way to
describe it is we and not just as humans
but species all of life all of society
coexists and exists fundamentally as a
function of networks the idea of
individual is actually antithetical in
terms of growth opportunities in the
advancement of society all of our
capabilities are a byproduct of how
we're networked and connected so we did
a paper a while ago where we wanted to
understand if we bring AI into these
learning processes such as complex
problem solving what are the effects of
that you know what are the critical
components that are involved assuming
that you agree with me that networks are
the foundational underpinnings and so we
looked at essentially when you have
human and artificial cognition
intersecting in areas of metacogn
such as regulation and learning
management in affect related to things
such as emotion and trust and confidence
and the way that we connect with one
another with a sense of security and
confidence what does that look like or
if you then take and look at the
cognitive practices things like
remembering what's the importance of
memory when AI is at your fingertips or
which parts of memory remain relevant
when AI is at your fingertips because
one of the things that AI does in this
conversation uh is move capability
questions to a new plane it's not that
it makes those things irrelevant it
means that we are related to some of
those Core Concepts differently than we
perhaps have been in the past and
similarly with social practices and
collaboration and engagement and working
together so when you bring AI into this
process one argument that I've been
making to colleagues for years is that
every single thing that we know and
understand understand about human
learning and human knowledge growth
needs to be
re-evaluated with an understanding of AI
as a potential mediating and
transforming agent within that
ecosystem and so we looked at if you
take these two pieces and you bring them
together because that's what we
essentially see happening it's not that
we're saying AI is a tool off to the
side I'm arguing that AI is the first
injection of intelligence
in the human System since our neocortex
came online so it is an alien
intelligence it's not exactly like us
but it does certain things that can make
some stuff easier for all its criticisms
for its hallucinations for its biases AI
is a type of an intelligence that we can
co-thinkers a period of these little
blips of sudden crashes U there's a
research uh report that was put out by
Johnson where he said these systems
where AI is starting to make decisions
they're moving so fast that we are at a
point where there is an inability for
humans to intervene in real time meaning
its machines have taken over large swads
of those kinds of processes and what
it's done for us we can't participate in
real time so the human cognitive
function is to escalate which means we
move to a higher plane because we can't
do the granular performance at the same
level that AI meaningfully can and
that's produced work such as this paper
by rwan and all where they said we need
to start thinking about theories of
learning that don't just integrate human
to machine interactions it's machine to
machine interactions that we need to
think about because there are sads of
decisions in some high-risk areas
including medical and Military where AI
is making decisions often without a
human input layer uh brought in and so
to start thinking about complex problem
solving and the integration of human and
artificial cognition into this kind of a
landscape is critical so paper we did a
few years ago we looked at exactly this
question is what happens when you have
two types of intelligence that maybe
don't quite understand each other but we
know that meaningful integration between
the two is going to be important for
solving all the problems that Humanity
faces from homelessness to inequality to
climate change um how do we begin to
make those two play together and what is
that intersecting space where learning
and sense making and meaning making
happen meaningfully at that level so we
did a paper um in just last year
actually where we looked at the
literature that to date has looked at Ai
and uh its impact on the education
setting specifically what are people
doing with AI in classrooms in a
practical way not in a high flut and
future way that says oh we'll all have a
personal agent and we'll all be happy
and have a robot in our home but in a
practical way what's actually happening
in classrooms and so the number one set
of applications are ones that still
remain prominent which is adaptive
systems and
personalization that's been a holy grail
of education for decades but it says
rather than one student or one teacher
teaching 30 students everyone has a
onetoone relationship like was mentioned
previously this is the idea of blooms 2
Sigma where the inclusion of a tutor can
move a c student to an a student with
the right level of support and guidance
profiling and prediction was an
important one that came up as well a big
part of what universities haven't done
historically is to understand their
students you know what are their skill
sets what are their capabilities outside
of a grade and so it's this idea of how
can we better profile and then if we
profile predict which students will
succeed which students are at risk of
potentially dropping out assessment and
evaluation is another important one and
then interestingly uh tutors were right
at the bottom at least of this cluster
it wasn't a huge area of use this data
would obviously be very different if we
were to do this report again in a year's
time because one of the top adaptations
of the a growth of gener of AI has been
tutoring and adaptive systems of that
type so the benefits then are straight
forward personalized learning positive
influence on the education process um
better administrative activity from a
university level as well helping get
insight into how students are learning
and then also as a way of doing more
effective assessment but that doesn't
mean everything is all delightful
because there's some significant
challenges that are introduced with AI
in this landcape one probably top
Remains the ethical Dynamic how do we
ensure that AI helps not harms people
how do we preserve the Integrity how do
we preserve the uh the security of the
student student in this area of growing
Automation and increased technology a
lot of attention to curriculum
development how do we use AI well to
create courses and then a range of in
infrastructure questions that I'll
address uh once once I get a little
further toward the end the big research
gaps in the literature um are what you
would expect ethics keeps coming up top
of the list because that remains one of
the bigger unspoken challenges in the
University sector as a whole and not
just University across all of society a
lot more questions about methodology uh
this is a conversation I was having with
my wife on this as well recently which
is in education we've typically done we
take a concept and we develop a theory
around it Theory sometimes is the
byproduct of extensive research and then
we use that to guide and shape decisions
going forward but now we're at a
slightly different landscape in that we
can use large sads of data and rapidly
move that forward to try and gain
insight into students and student
performance when we started to look at
this more from an llm side there was
acceleration on a number of fronts but
the same questions remain profiling
prediction feedback remained key
concerns uh in the educational landscape
whether we're looking traditional AI or
emerging
AI we did a paper uh actually I think it
was this year um where one of the
outcomes was we looked at student focus
and student engagement when you bring AI
into the classroom setting and the
interesting thing we found was that
students don't necessarily learn from AI
they instead rely on AI which is an
interesting distinction uh it doesn't
have the same learning capability in all
settings as always it's a function of
pedagogical approach and pedagogical
models one of the big papers though that
I always refer to and this is an
important concept when we talk
methodology is that a lot of the
activity that happens in a classroom is
based on uh or that happens in research
is based on a setting that's
disconnected from reality and an Brown
did a fantastic paper uh you know was it
40 plus years ago where she looked at
this design experiment that the entirety
of a classroom is a learning ecosystem
for learning research rather than these
one-off experimental design settings and
that's exactly the kind of activity that
we try to do in digital spaces now
through the use of data and data
collection which we get from a range of
sources student Information Systems uh
instruments or survey instruments we
deploy Learning Management Systems we
can get a fairly holistic assessment or
lens of what a student is doing and
where she is in her overall learning
process so with that as a backdrop I'll
take the last 10 minutes to talk through
these final sections so if we look at
technology over the last few decades we
can say the open education movement
fundamentally taught us that we can
scale content with minimal cost
additions each new duplication of a web
page or a PDF is really insignificant
compared to the cost of duplicating a
new textbook a second thing that we
learn through open online courses or
mukes in some cases is that we can scale
teaching we can have 100,000 or 500,000
students take a course and it's much
less expensive from a lecture lens if
that's the primary pedagogy in that kind
of a setting an AI is at the early cusp
I believe of teaching us that we can
accelerate and scale interaction so the
connections that we have on sort of a
onetoone basis from a tutoring
perspective the significant Trends I
want to identify here though relate to
where is the current state of AI after
chat GPT and the growth of generative AI
the hype that we had in 2022 and early
last year we're starting to see some
very practical groundings of these
Technologies not least of which is the
prevalence of AI in everything from our
cars Toof software to the platforms we
use growing multimedia and multimodal
and also a lot of attention being paid
to open- Source llms or open source
software a lot of that's driven by meta
interestingly enough and a growing group
of uh organizations notably stuff like
misil and others that are really
promoting open llms there's also
attention being paid to very small llms
which you're going to see more and more
on your Android or on your iPhone
devices uh feed2 Fe three actually just
came out uh at the end of April as well
so we're starting to see them
accelerating similarly AI pairings
meaning AI with traditional Robotics are
starting to come together and I think
most of us in this room will have a an
aid driven robot in our homes within the
next 5 years doing routine related house
tasks a lot of attention now this is
maybe a little more relevant to some of
you who are running technical teams
there's been a significant acceleration
of platform technologies that make AI
development easy if you were to do
something with an llm 16 months ago or
12 months ago you needed a fairly High
technical capability but now in
environments like AWS or vertex you can
quickly run up a series of models test
and deploy uh with a team of one who has
fairly fundamental understandings of the
process um we're also seeing a lot of
I'll skip that one uh more and more
wearables wearable devices uh rayb bands
is an interesting one again meta driving
uh which is the ability to have your
glasses as you're walking see a scene in
front of you you in llama 3 which is
Meadows open llm uh you can ask it what
am I looking at what's this picture and
it will search and provide an answer
back to you uh audibly on your on your
uh glasses as well and then a lot of
tooling things which is a little Beyond
where we are today but tools like dspi
and laying chain that make this process
of managing multiple llm Integrations
much more effective so what are some of
the inte
implications of this well first of all I
think AI will impact roughly everything
that a university does there's no sector
that's not going to be challenged by it
and I do think it represents a systems
level challenge for the sector and I
don't think universities see that and I
don't think many of them are responding
as urgently as they should because if
you look at one of the main things we do
is we generate knowledge and we
communicate knowledge that's our role as
a university and AI plays in all of
those territories I mean here's just a
range of tools that are knowledge
adjacent generating Technologies some of
them have been deprecated you know like
Galactica was briefly put out but then
paused but there's a lot that you can do
with this growing Suite of AI tools that
intersect with human creativity and
human knowledge
capability the system itself as an
Enterprise is already in a process of
unbundling it's no longer a
self-contained system a lot of what we
offer is increasingly being done by a
range of providers and we're going to
start to see exactly the same effect
happening in AI tools if you're a leader
in a university you're going to get a
range of providers and technology
companies coming up to you selling you
AI technologies that do everything from
uh tutoring to content creation to
assessment to student recruiting to
chatbot engagement and so on so it's a
constant influx of new technologies and
new approaches and so the way that we're
going to adopt as a sector is really
going to be one of three a direct
response to a simple problem a platform
based response or as a transformational
angle or transformational opportunity
from a system preserving lens the first
or the second one it's about just taking
Ai and helping it solve a problem like
advising or providing better student
support or the idea of a learner
co-pilot you know Microsoft co-pilot and
others are already making that available
or doing things like adaptive feedback
um that's what universities such as ASU
and what you're seeing with University
of Florida they're taking this kind of
an approach where they're largely going
out and just finding a problem and
solving it with some function of AI if
you want system changing approaches
though you need to start thinking very
differently about your literacies about
developing personal learning graphs and
personal models of a learner that
transcends a course even transcends
their lives computed curriculum not
pre-structured textbooks but curriculum
that's generated based on what a learner
knows and integration of Labor Market
needs into that educational process as
well so we're talking about not doing
education as usual but doing education a
fundamentally different
way so the idea then is this
articulation of an AI first University
and an AI first University is one where
AI is involved in all aspects of the
organization from the infrastructure
through to admissions teaching
assessment curriculum and the research
process and I'll run through six of
those very quickly but you know one is
the infrastructure the pipeline the data
l so any AI deployment is fundamentally
a data challenge secondly it's about
building institutional capability with
AI like do does the organization know
what AI is and how AI performs and what
it does um thirdly there's a range of
questions that relate to the leadership
and policy and governance how does the
University enable AI experimentation how
does it protect University reputation
through effective AI
engagement adaptive and responsive
teaching methods as noted this is
already prevalent in the literature but
how do we begin to use AI in such a way
that it is focused onetoone support for
Learners how do we improve the
personalized experience so that each
individual is met at her needs not just
cognitively but metacognitively
affectively socially and so on so it's a
very nuanced uh response to individual
learner needs and then also the
acceleration
of research through the utilization of
AI doing a simple literature review uh
is now dramatically different through
the inclusion of tools like elicit
consensus or Iris
AI so I think at the end of the the
final several slides one of the critical
challenges I want to emphasize for
anyone that's initiating the AI
conversation is get the data and the
related architecture right more than
almost anything else this is a critical
challenge there are needs of building
capabilities institutionally what I mean
by that as being AI capable as an
organization and having the technical
capacity to train fine-tune models build
your own Bots those are expected but at
the in institution-wide concern of
infrastructure is critical um we just
released a paper for discussion uh
yesterday actually on a global data
Consortium where we tried to lay out how
should Universities at scale begin
collaborating and sharing data so that
you can learn from your your peers
rather than try and do everything on
your own so the university AI response
should be through multi-institution
collaboration and sharing across
operational data analysis data science
planes and then ultimately addressing
and driving impact so it's a critical
outcome uh we have a white paper that's
now out for review uh from the American
Council on education if anyone's
interested on
that um final points we're really
getting at this idea where most of
education has been about teaching people
knowledge related things you know the
epistemology question and I think we're
now moving to the ontology question like
who are we as human beings how do we
develop human beings how do we help
people become more engaged more
productive and more effective members of
society you know any of the kinds of
things that are here like what is it
that we should be teaching how should we
be teaching people and Learners places
of being in the world how should be
driving their capability to navigate
complexity to engage with nonhuman forms
of intelligence and then as a byproduct
of that to be sort of proactive engaged
and anticipating potential harmful
effects of AI as we go
forward thank you
[Applause]
[Music]
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