if Emeril ization will have a profound
impact on our economy and if we want
future economic growth we have to
rethink our approach to education
memorization is a term coined by the
u.s. visionary and philosopher
Buckminster Fuller who used it who uses
the word to describe the tendency of
Technology to do more and more with less
and less until eventually you can do
everything with nothing and what sounds
like a visionary and philosophical
statement is actually something that we
have all experienced if we look back in
time and look at the first computers who
were huge machines that filled rooms
with limited storage and processing
capacity and if we then look into our
pockets and find a smartphone with
dramatically more power more computing
or processing power and storage that is
now able to integrate all these other
functions we see a concrete example of
ephemeralization I also think we are
reaching a new tipping point a new
tipping point in the development of
computing power which will allow us to
unlock a completely new set of use cases
and applications these use cases are the
simulation of the brain through neural
networks deep learning and ultimately
artificial intelligence now just to give
you an example how big these changes
have been in the last literally twenty
four to forty eight months
here's a chart that basically gives you
some statistics how long it takes to
train a machine learning algorithm in
this case LX net which is used for image
classification purposes the blue bar is
one of the traditional but high-end
processors and it takes this processors
43 days in the green bars you see the
already launched and soon to be launched
products from a company called Nvidia
who is able to approach the processor
design in a completely new way and to
deliver a 20-fold increase in training
performance which means we are able to
decrease training time from 43 days to 2
days only that means we can train
computers much much more much much more
scope and give them a better
understanding of what's happening in the
world they're already very concrete use
cases where we see this changes in
action just a few months back a computer
alphago bet the Korean beat the Korean
go world championship leaves it all in
four out of five games where even
industry insiders were surprised how
quickly this happened we're also seeing
the rise of intelligent assistants where
you can ask your iPhone or your
specifically Siri on your iPhone not
just simple questions anymore but also
more sophisticated questions to show you
photos from your last vacation in Yuda
in August
google has also announced a new
intelligent assistant where you can ask
what a jungle book is any good and it
will give you the answer without having
you to search for the information by
yourself
the ultimate goal or one of the ultimate
goals of artificial intelligence is the
self-driving car
and while the self-driving cars for many
of us here in Europe still sounds like
something out of a science fiction movie
if you visit the Silicon Valley then go
to Mountain View and then sit down on a
road at the Googleplex by the road wait
for 30 minutes and you see this cars
driving around you will realize the
future has already arrived William
Gibson said the future is already here
it's just not evenly distributed and I'm
asking myself what happens when
technology is evenly distributed what
happens to our economy
when self-driving cars make it to Europe
and intelligent assistants become even
more intelligent
how many travel agents do we still need
when your assistant can book a flight
for you
how many customer support agents do we
need when we can use machine learning to
extract the right answer from thousands
of pages of help center content and how
many employees do we still need in the
back office of a bank or an insurance
company that are making decisions that
soon can also be potentially automated I
think it's a very very difficult
question but I think we have to answer
it and I also believe we have to be
realistic and for me being realistic
means we have to admit that the demand
for certain shops will decrease and
specifically the demand for routine
knowledge work will be decreasing
we will need less travel agents and we
will need less employees working in a
Bock back office because there will be
automation there will be artificial
intelligence that will perform decisions
but there is also good news to this side
there will be an increasing demand in
another area
and future economic growth will come
from non-routine creative knowledge work
which is work that aims to design
solutions that solve problems in the
world this is already done today by
scientists researchers physicists
programmers and engineers and the good
thing is there is an unlimited number of
problems in the world so there will
always be work for somebody who can
solve real problems so if we accept this
premise I'm asking myself how can we be
prepared and again we need to be
realistic that nobody will hire you
anymore
because of what you know Google knows
everything right and it will know even
more it will give you even more precise
answers maybe not in two years maybe in
five years maybe in ten years maybe it's
not even going to be Google itself right
because maybe another startup will come
around and will be able to give you the
right answers at the right time
so if knowledge itself becomes less
important what we need to focus on in
order to get hired is the ability to
apply the knowledge you will get hired
because of what you're able to do with
that knowledge that's pretty
straightforward to write universities
and education to some degree follow this
principle already but looking at
universities all over the world that's
still the most common view we see we
have lecturers but sometimes great and
sometimes average qualities standing in
front of a class of students trying to
transfer their knowledge into their
minds following what I said before that
mode of education is not really the best
use
of anybody's time why not take the best
lecturers in the world and figure out a
way how we can make a more interactive
learning experience so they can take
over the part of pure knowledge transfer
and then we think about a way and a set
up and the new structure for
universities where students get a chance
to apply their knowledge and there are
actually universities who are really
driving pushing the envelope in this
area for example Stanford University
that is teaching a course that actually
goes back to the 60s and 70s where
industry partners give students problems
and students have to work on these
problems and come up with solutions what
you see here on this screen is a typical
classroom session where you see the two
professors actually three of them
sitting there working with the students
in a highly engaged manner over many
many months to really understand the
problems and design solutions the
outcome of this course has been quite
impressive it's not just number of
patents that the companies derive from
the work of the students in
collaboration with the university or the
number of products that companies
develop or students develop in the
course it also acts as a breeding ground
for startups that students themselves
id8 in this course and the skills that
they learn that help them afterwards
launching their own companies - that's
not just happening in the United States
we have transferred this system also to
the University of st. Gallen
where we teach a course on design
thinking where students spent more
around 10 months between 10 to 15 hours
a week in a highly engaged environment
under supervision by professors internal
and other external advice
icers to work on these problems and to
come up with solutions and I believe
that the characteristics we see in these
courses will be shaping the future of
Education which will be more about
problem-based learning about immersion
and about simulation now what do these
three things mean completely
problem-based learning means students
are challenged to apply their knowledge
to real-world problems and they need to
rephrase and rethink and reframe the
problems to identify new solutions and
maybe even realize that they have been
trying to solve the wrong problem all
along that's something that we typically
don't do because most exams still try
and force you to come up with the one
right answer and the only one right
answer shifting away from this mode
where we transfer knowledge and then we
examine whether you have successfully
acquired this knowledge will be a thing
of the past
what's also specific to this kind of
teaching and education environment is
immersion it's a real-time
decision-making over longer periods of
time as I mentioned before these courses
run over 10 months so if a student makes
a decision that turns out to have not
been the optimal decision they actually
have to deal with the consequences and
turn around the project and figure out a
new path forward but the last point that
is important is the aspect of simulation
that at the end of the day all this
happens in a safe environment where
students are free to experiment where
they can try out things they can fail
and then they have a chance to try again
and I can fail again and if they even at
the end of that course have continuously
be failing the course aims not to only
change on the outcomes they have
achieved but also on their style and
their ability to apply knowledge and to
try and solve these problems and I
believe that our education system needs
to go beyond the pure knowledge transfer
we need to find smarter ways to
facilitate that and we need to change
our universities into a real training
ground where students are have a chance
to train under the supervision of
professors to solve real-world problems
in an immersive environment while
experiencing a simulation of real life
because only if we go to this step only
if we go there then we don't have to be
afraid of the rise of technology but we
can actually embrace the opportunities
that technology brings to solve the
hardest problems in the world
thank you
you