GEF Madrid 2024: Conversation: Becoming an AI University / GEF AI Platform
Summary
TLDRGeorge Simons, a co-founder and chief scientist of SNH use human system, discusses the intersection of human and artificial cognition in education. He addresses the challenges and opportunities AI presents to higher education, emphasizing the need for universities to adapt and integrate AI to remain relevant. Simons outlines key areas where AI can transform learning, teaching, and research, urging a proactive approach to ethical considerations and data infrastructure.
Takeaways
- đ§ The speaker, George Simons, researches the intersection of human and artificial cognition in knowledge processes and is involved in building resources to respond to the impact of AI on learning and wellness.
- đ George criticizes higher education's response to AI, suggesting it has been fatigued and even antagonistic, and calls for universities to be more proactive in utilizing AI to ensure student success and remain relevant.
- đ He discusses four main topic areas: literature on AI and learning, the intersection of human and machine cognition, current trends in AI, and the implications for universities.
- đ€ AI is not just a tool but a node within our cognitive network, which has significant implications for education and how we understand and facilitate learning.
- đŹ Research indicates that AI can affect complex problem-solving and learning processes, including metacognition, emotion, and trust, necessitating a re-evaluation of educational theories and practices.
- đ The benefits of AI in education include personalized learning, improved administrative processes, and more effective assessment methods.
- đ§ However, there are challenges such as ethical concerns, curriculum development, and infrastructure questions that universities need to address when integrating AI.
- đ A literature review reveals that AI applications in education are primarily focused on adaptive systems and personalization, profiling and prediction, and assessment and evaluation.
- đ ïž The current trends in AI include the prevalence of AI in various platforms, the growth of open-source LLMs, and the integration of AI with traditional robotics and wearable devices.
- đïž To effectively incorporate AI, universities should focus on building institutional capability, improving personalized learning experiences, and accelerating research through AI utilization.
- đ The speaker advocates for a global data consortium to facilitate multi-institution collaboration and sharing of data to enhance AI capabilities across universities.
Q & A
What is the main topic of George Simons' discussion?
-George Simons discusses the intersection of human and artificial cognition in knowledge processes, the role of AI in higher education, and how universities should adapt to remain relevant in a rapidly changing world.
What is the significance of the speaker's role as co-founder, chief scientist, and architect of SNH use human system?
-As a co-founder, chief scientist, and architect of SNH use human system, the speaker is involved in building resources to respond to the systems' impact on AI, learning, and wellness, highlighting his expertise in the field.
What are the four distinct topic areas that George Simons plans to cover in his talk?
-The four topic areas include literature around AI and learning, the intersection between human and machine learning, current trends in AI, and the implications for universities, as well as priorities for universities to consider in the AI conversation.
How does George Simons view AI in the context of human cognition?
-George Simons views AI not as a tool, but as a node within our cognitive network, suggesting that AI is an integral part of the networks that make up human intelligence.
What is the current state of AI in education according to the literature mentioned by George Simons?
-The literature suggests that AI in education is primarily used for adaptive systems and personalization, profiling and prediction, assessment and evaluation, and to a lesser extent, tutoring.
What are some of the challenges introduced by AI in the educational landscape?
-Challenges include ethical dynamics, ensuring AI helps rather than harms people, preserving student integrity and security, curriculum development, and infrastructure questions.
What does George Simons suggest as a critical component for universities initiating the AI conversation?
-George Simons emphasizes the importance of getting data and related architecture right, building institutional capability with AI, and considering multi-institution collaboration for sharing data.
What is the concept of an 'AI first University' as proposed by George Simons?
-An 'AI first University' is one where AI is involved in all aspects of the organization, from infrastructure to admissions, teaching, assessment, curriculum, and the research process.
How does George Simons relate the impact of social media to the potential impact of AI on society?
-He draws a parallel between the initial positive connections provided by social media and its eventual negative effects, suggesting that AI could have a similar trajectory if not managed properly.
What are some of the significant trends in AI that George Simons identifies?
-Significant trends include the prevalence of AI in everyday technologies, the growth of multimedia and multimodal AI, the promotion of open source LLMs, the rise of very small LLMs, AI pairings with traditional robotics, and advancements in platform technologies for AI development.
Outlines
đ€ The Integration of AI in Higher Education
George Simons, a co-founder and chief scientist of an AI-human system, discusses the underutilization of AI in higher education. He emphasizes the need for universities to adapt and innovate using AI to ensure student success and institutional relevance. Simons outlines four key topic areas: literature on AI and learning, the intersection of human and machine cognition, current AI trends, and the implications for universities. He also addresses the societal and emotional impacts of technology, cautioning against the potential negative effects of AI on mental health and societal well-being.
đ§ Embodied and Distributed Cognition in Learning
This paragraph delves into the concept of cognition extending beyond the brain, with theories of embodied and distributed cognition suggesting that intelligence is a function of the networks we exist within. Simons argues that AI should be viewed not as a tool, but as a node within our cognitive network, affecting educational practices. He discusses the impact of AI on metacognition, emotional regulation, and learning management, and the necessity to re-evaluate human learning and knowledge growth in the presence of AI. The rapid decision-making capabilities of AI systems and the challenges they pose to human intervention are also highlighted.
đ Practical Applications and Challenges of AI in Education
Simons reviews the practical applications of AI in classrooms, focusing on adaptive systems and personalization to provide a one-to-one learning experience. He also touches on profiling and prediction to better understand student capabilities and assess their progress. However, he points out the ethical challenges, curriculum development needs, and infrastructure questions that arise with AI integration. The research gaps in the literature are identified, with ethics being a prominent concern, and the need for new methodologies in educational research is emphasized.
đ The Evolution of AI and Its Educational Implications
The paragraph discusses the evolution of AI and its impact on education, noting the growth of generative AI and the practical applications emerging from it. Simons identifies trends such as the prevalence of AI in various technologies, the rise of open-source LLMs, and the integration of AI with traditional robotics. He also mentions the importance of platform technologies that simplify AI development and the potential for AI to impact all aspects of a university's operations, from knowledge generation to student recruitment.
đ ïž Transforming Universities with AI: An AI-First Approach
Simons proposes an AI-first approach for universities, where AI is integrated into all organizational aspects, from infrastructure to admissions, teaching, and research. He outlines six areas of focus for universities to consider when engaging with AI, including data architecture, building institutional AI capability, leadership and policy, adaptive teaching methods, and accelerating research through AI. The importance of getting data and architecture right is stressed, along with the need for multi-institution collaboration and sharing data to learn from peers.
đ Reflections on the Future of Education and AI
In the final paragraph, Simons reflects on the broader implications of AI for education, moving beyond teaching knowledge to developing human beings who can navigate complexity and engage with non-human intelligence. He emphasizes the importance of proactively addressing the potential harmful effects of AI and suggests that the focus should shift to understanding how AI can help individuals become more engaged and effective members of society.
Mindmap
Keywords
đĄArtificial Cognition
đĄAI in Education
đĄAdaptive Systems
đĄMetacognition
đĄGenerative AI
đĄEthical Dynamics
đĄPersonalized Learning
đĄAI-First University
đĄData Architecture
đĄPedagogical Models
đĄComplex Problem Solving
Highlights
George Simons discusses the intersection of human and artificial cognition in knowledge processes and his role as a co-founder, chief scientist, and architect of SNH use human system.
Simons points out the misjudgment of higher education in its response to AI, highlighting the need for universities to be more responsive and capable in utilizing AI for student success and relevance.
He outlines four topic areas to be discussed, including literature on AI and learning, the intersection of human and machine, current trends in AI, and the implications for universities.
Simons emphasizes the importance of understanding AI not as a tool but as a node within our cognitive network, with significant educational implications.
Discusses the importance of re-evaluating human learning and knowledge growth with AI as a mediating and transforming agent in the ecosystem.
Presents research on the effects of AI in complex problem-solving and the critical components involved in the intersection of human and artificial cognition.
Simons talks about the rapid pace of AI decision-making and the inability for humans to intervene in real-time, necessitating a reevaluation of human cognitive functions.
He identifies the need for new theories of learning that integrate not just human-to-machine interactions but also machine-to-machine interactions in high-risk areas.
Simons reviews the practical applications of AI in classrooms, focusing on adaptive systems, personalization, profiling, prediction, assessment, and evaluation.
Discusses the benefits of AI in education, such as personalized learning and better administrative activity, while also acknowledging the challenges introduced by AI.
Ethical dynamics, curriculum development, and infrastructure questions are highlighted as significant challenges in the integration of AI in universities.
Simons emphasizes the importance of getting data and related architecture right as a critical challenge for universities initiating AI conversations.
He suggests that AI will impact every aspect of a university, from infrastructure to admissions, teaching, assessment, curriculum, and research.
Simons proposes the concept of an AI-first university, where AI is involved in all organizational aspects, and discusses six areas of priorities for such universities.
The importance of multi-institution collaboration and sharing data across operational and data science platforms is stressed for a university AI response.
Simons concludes by emphasizing the shift in education from teaching knowledge to developing human beings and preparing them to engage with non-human forms of intelligence.
Transcripts
thing he is George cens um I think
they're 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
state welcome him George
Simons 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 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 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 L 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 writ
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
what 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 can compare 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 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 and the advancement of
society all of our capabilities are a
byproduct of how we're Network 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 metacognition 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 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
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 on line
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 uh 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
it's 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 granual level
performance at the same level that AI
meaningfully can and that's produced
work such as 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 machino 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 a 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 one
toone 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
straightforward 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 landscape 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 in this area of growing
Automation and increased technology a
lot of attention to curriculum devel
velopment 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 was having with
uh 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 swads 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 a
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 that happens in research is
based on a setting that's disconnected
from reality and an Brown did a
fantastic IC 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
oneoff 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 a 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 to software to the platforms we use
growing multimedia and multimodal and
also a lot of atten 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 fe2 fe3 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 scen 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 Lang chain
that make this process of managing
multiple llm Integrations much more
effective so what are some of the inte
inte implications of this well first of
all I think AI will impact roughly
everything that 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 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
leg so any AI employment 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 uh 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 is 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 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 on ology
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 we be
driving their capability to navigate
complexity to engage with non-human
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
[Applause]
you thank you very much George wonderful
intervention yes for the next guest
thank you thank you very much again um
now we are moving forward H and I'm
going to switch switch again into
Spanish
much gracias
[Music]
ra
comp for
ET
for
for for
[Music]
spe
for
spe
Q
Sol
WhatsApp Instagram teams
can
for for
Sal
[Applause]
ra
for for
Ai and critical
thinking
for for
3:30 PM please we'll be back here thank
you very much for everything
[Applause]
Voir Plus de Vidéos Connexes
Summit George Siemens Becoming an AI University
What any university should be thinking right now about AI
David Edwards at UNESCOâs Digital Learning Week 2024
Cara Mudah Optimalisasi Artificial Intelligence untuk Pembelajaran
Mustafa Suleyman on The Coming Wave of AI, with Zanny Minton Beddoes
GEF Madrid 2024: Navigating AI Legal Frontiers
5.0 / 5 (0 votes)