Embed code
also thanks to digits with one for having me it's another to um be able
to present here what we do in the area of artificial intelligence at cisco
so my plan for this talk is to tell you first of all
why does come is interested in artificial intelligence um i guess
it's not that kind of thing you'd expect from um
classical togo company um second how um
um what is our procedure for for bringing artificial intelligence or customers
and the third um name some examples of products that we've there we are
developing and that we've developed um recently that to use artificial intelligence
so let's get started
so just ionisation it's um one of the things that
i think power is um artificial intelligence and it's
nothing you know it started probably with the uh
first microchips and has seen a really
huge progress since then so there was the first wave when the smart phones
arrive which um brought a huge change
there is a currently um where at the top of
the um coyote and rejoice asian waves so
um it's very easy to have computing
resources um they're very very cheap
very easy to set up in the cloud and so on
and um we're right at the beginning of the
third wave which is the artificial intelligence wave
uh which means that machines will be learning and make or
are already starting to learn and make attachments decisions
um am i think huge quantities of data that
um previously was impossible to dig into
um and i guess there also be a
lot of robotics breakthroughs um and other
areas and we don't know yet what the fourth we will be but we are very excited find out
'kay so digits lies they shouldn't and all these
waves have changed a lot about our business
so one of the things that has come made a lot of money one on where
um or were text messages and this is
something that has disappeared practically overnight
so companies like what tap um are i don't know
any other of your favourite messaging apps have
taken over and no one uses s. m. s. anymore or at least very few people
no everything is turning into software softer is eating the world
and some famous people agree with this among which market reason maybe you've heard of him
um so says come has the ambition to follow
this um this trend and um of
course it's also kind of necessary in order to to stay alive as a company
buses commons turning from a classical togo company into um um softer company
um and i guess this starts to give a first explanation of why we're interested in a i
oh here i'm showing um and events that is organised basis come and maybe some of you
know 'cause it's um become relatively begins with concerts comes after a happen last month
along with business also customer um behaviour changes so people expect
different things from us than they did before higher expectations
for example um like i said the advent of smart
phones and um orgy networks and so i'm made
uh the mobile that data volume increase dramatically
um people are watching lots of video even him on there
are mostly on their smart phones um people are using
catchup t. v. so no longer live t. v. but
um recording functions and net flicks and so on
and very very few people um are using the
still the fixed a phone network so
again another very clear signal that we need to change and we believe a i
the change that we need to incorporate and here we uh
i highly agree with a ah it's apples video
game cock was set that a. i has to be horizontal so we need to put a i into
basically everything that we built because people expect software to
behave intelligently you can no longer build a
that i'm stupid product and get away with it and this brings us also to our
core belief in the um yeah in machine learning robots was gone which is
um that in the future our product will be stupid without a high
and therefore worthless so we obviously we can tell the right so hopefully
this gives a good motivation of y. says compares about a i
and now let me tell you how we plan to bring a i to our customers so
maybe just as a side remark i i'm in the
enterprise customer um departments are talking about um
other companies not not private customers alright so um
what we do is build digital enterprise solution
and we have lots of resources already to build on um
there are so this department the different resolutions consist
of seven hundred specialist employees we're building um
enable or as we call them some sort of basic softer building blocks that week and then
put together based on customer needs we have um
an advantage citation change h. m. m.
tell you about more in the next slide we have tools um
and we are used to working in um in um
in a pretty high temple so really fast really i doubt that's on
oh here is our unique selling proposition at the digital and resolutions um first of all
over time we have been able to build and ten
competence that means we have infrastructure we're able to
a store and process data out both firms is
coming from our other business customers we um
um we're competent in software development in different platforms and so on
and what enabled us to do this was our a very comprehensive
three hundred sixty degrees portfolio including both standard based solutions
those things that i don't know product stuff to products from other
companies that we provide consultants for um but also customisable
so where we talk to the customary find out they have a problem
it cannot be solved by the standardised solution so we need to
um sit together and build something as specifically for that customer
and finally all of this would so certainly not be successful
without um i tried methodology and we have this as
well most importantly um we believe in the co creation
process so like i said we we sit together
or with our customers with around and fries customers and we find out what problems they have in order to
to find the best solution for them instead of just
selling um at the standard thing that we have
and this is um also uh i guess a
key component in a human centre design philosophy
uh where you try to find out what users want and
um uh how to best um fill their needs
we also work according to agile methodology i guess everyone does today so is it even worth mentioning but anyway
um so we plan to you um integrate a i into this
whole um overview that we already have in dealing with customers
and hope that will be successful and it doesn't look so you'll see when i present the product
right the first of all what exactly do we understand with a high
and i start with an example from the moment they're born into this
world children start learning things about it for example a here
a chocolate what is struck what hoping that the kids but their hands in a day this married on their face they
tasted and so on to the the experience directly with all their senses then finally they learn what chocolate is
and a i is is very similar in the sense that um
it enables machines to gradually learn things about the world
that we humans know so this is them a more generic definition of a i
of course there are also um i think nowadays mostly uh use cases of
right now our artificial intelligence where you have a very specific use case let's say uh you
write an email and you write a time in dayton there um and the the
program is able to extract isn't put in a calendar
is already an example of narrow artificial intelligence
but artificial intelligence is an umbrella term and is just means that computers act
intelligently similar to how we humans i'm
acting different situations and it includes
um various either um feel it's
like um expert systems
so these are i guess in the seventies and eighties what the the
state of the art um of artificial intelligence at that time where
um experts were writing rules for computers to behave in a certain way based on their knowledge
um obviously the more a recent um
important area artificial intelligence is machine learning and
particularly deep learning where computers can um
become intelligent through statistics on large quantities of data
oh this has uh in order to to do something like this
and i uh lots of things are needed and they're
different areas in which you can apply it
there's first of all perceptions so obviously a a i.
systems must interact with humans so we they
need to be able to um perceive festival speech
vision and so on there is um
natural language processing so just because they and they are able to perceive the speech
doesn't mean they understand what the person saying says what natural language processing dies
um there's reasoning there's planning and of course that somewhere up there's there there's alter products
and machine learning which i view as a subset of artificial intelligence is also
huge so in order to achieve all these things that i've shown here
there are lots of different methods there's um unsupervised learning there's
um supervised learning semi supervised learning neural
networks um him support vector machines
clustering and so on there's also reinforcement learning a bit it's
um let's related to the rest of the the the tools that
we have at our disposal in terms of methods that say
but either ingredients are just as important so first of all
processing power is very very important
data obviously because computers learn from data as
i've said we statistical methods and algorithms
so i think there is my why a eyes seeing such a huge a surgeon
in popularity is because in all of these area there has been tremendous
uh progress and i'm past couple of years
so you pressing power already a bit longer than that we've we've um
um for quite awhile gone from computers looking like this to having um
to having a enormous computational power in our pockets or in our briefcases
um data as i've said uh with a smart phones getting popular
the amount of data generated by people has also increased exponentially
and finally algorithms so i guess where the the latest thing greedy and just to
um become due to arrive into a very advanced state
um there's tons of low that you also hear about i guess after the stock
um microsoft is also opening um various of their
a. i. algorithms to the public face but
the same i was recently heard about by to a releasing
something in the area so there's lots going on
and this means that people like us we're we're as i
said we're a softer slash local company we we
don't have the ambition to compete with will or face
bookends one but me i'm a wants to
to uh um enable our customers to benefit
from um from all these things
as soon as possible and to do so in a way that suits them best
obviously there is a there are also lots of misconceptions with all the hype surrounding yeah
i so um machine learning is expensive it's a real time activity everything can be
done with with people earning these are all the kinds of things that we have to
sort of fight against um when we start working with people on a i
is the expectations are sometimes quite um misaligned with reality
and so let me show you some examples of what we do
some products that we've built um in the recent future in the
recent past sorry or that we're building at the moment
our products are based loosely on this um framework
that i'm showing here so we have um
obviously have data you have lots of data from system itself
we can uh also take advantage of of public data
at that anyone's disposal and we may may also get customer data
depending on the customer um we have a perception liar
where um first of all um there is speech text um then there's
perception in the sense of compute of of the teaching computer is what the
data represents and what they should do with it so we have a
um model training um component
and then finally we have a bunch of micro services um in the
area of natural language i think an understanding for the moment um
uh but extending to other areas as well so we have named entity recognition topic
recognition sentiment analysis summarisation a trend detection and
so on and so we um
have the services uh as part of an a. p. i. and the goal is to use this tape yeah
in order to build you their room highly customised solutions
are really sitting down with the customers and
i'm finding out what they want and building it for that we also
have sort of a more standardised set of products where we
uh that we use for example to showcase the different micro services
so for example this customer feedback is something that everyone is interested in
and um we do have a a product where we gather
um data for example first this come from social media
and analysts it by using these uh different services
and finally we also have um the the plan
of integrating with either a a softer products
that can highly benefit from from artificial intelligence example service now um
which is the customer desk software a. c. p. sales force
and so on and you'll see an moment why i think these
are appropriate um standards after products to be looking at
so our first um
interesting book um product that we've still using using or artificial
intelligence it's them is the customer desk um product
it helps service agents give faster and better service and how does
it do this by using a i obviously called mom all
and we um provide basically
realtime recommendations um
of solutions to the service agents to solve the customer's problem
well basically the agents um and has a custom on the phone
that explains the problem um the agent right sin incident ticket
and that in the background um our artificial intelligence service is looking
for is similar to get that is already been sought
and or for several similar tickets let's say and presents um
a list of um candidate solutions to the agent
and the agent can and uh choose the best solution for the particular case and in this way solve
the problem a lot faster than if they had to go and look through this manually um
either well the customers on the phone which would i guess would be quite um anoint
for the customer or at a later time and then get back to the custom
oh this is what the um the interface looks like him in this this
has been integrated in this or is this after of space gone
basically it's the this is the part where we we enter the
to the new ticket and this is where the magic happens
so showing a plastic it's with pro with solutions and highlighting what
um the main topic of this the current ticket is
and um there's also functionality that um looks
at the various incidents coming and and
notifies if there is something that looks like a major incidents so it's a lot
of people calling within a very short period of time about exactly the
same thing this means it's probably um the underlying causes probably similar and it's
the major incident to look at um at at all the incidence together
though this um oh is something like a site that
has been integrated into our hours just come
service desk a softer but it's definitely a reasonable
and and probably advantageous to integrated also in
in other service tax stuff for like service now there's mentioning previous
the second product that it developed is i'm also not
and in this product our goal is to use artificial intelligence for customer experience
and also to to showcase our various micro services
the question or this the problem that this product solves is
um i think customer feedback or uh providing an analysis of customer beat
feedback to the right people in order to improve customer experience
and i guess here you have a screen shot of an earlier version of the project of the product
it's basically a product where we um it with some of the
the two biggest functionalities are sentiment analysis where we look
at at a piece of feedback and say is this negative feedback with this positive feedback and obviously this of
it's a very a um and sought after functionality in
in terms of customer feedback or reviews in general
and the second sat functionality is being able
to recognise in the customer feedback entities
uh named entity recognition searches um names of people this
might be the customer already age and that they
talk to if they are uh referring to a customer interaction um it might be an address
or a and r. o. another company a phone number or a date anything
um and this helps pack the um the the feedbacks
um according to what they're about and then
providing an overview of a set of feedbacks through these um keywords that we extract
it's also useful when um when we went to a sort of
democracy tight feedback and to provide access to customer feedback
to everyone at the company which is what we've done and says come with this product in that case um
what needs to be then is uh in order to comply
with data protection laws is an amazing customer information so
so we also do this wasn't with named entity recognition so in order for
for every employee of says come to be able to look at
customer feedback and internalise it and find out what they yeah at their
individual level can do better in order to improve the customer experience
we need to make this data anonymous we cannot go around and and um
mm open it's a incident tickets from the hotline to everyone
oh this is worked out pretty well i'm in the the
product has been quite successful instead of says come
and now the plan is widths sonar to make
it more widely available and perhaps um also
tell it to other companies that might be interested in that is what it looks like
we can select language um obviously we're very
interested to have um great performance on
swiss languages a german french italian maybe not amend it actually they are
um so for this uh which it is something that we're still working on gathering data for this
languages and i'm coming up with trained models that
work best on this particular case um
i'm back to sonar we can filter um according to sentiment
um we can filter according to the data source so
um we're using for example peter and face book from social meet yeah but we also have internal sources
like i said the hotline is one example but we could also
think of uh for example um online online chat um
software that step we have one that's just come website where you can go into live with an agent
and then down here you see sort of summaries of what's the the
feedback um i says in the time frame that you selected
and then there is another visualisation is based on the analysis that we're doing
we also have topics again based on the time frame that we select you can extract
um a different topics the the most important
topics that the feedbacks um are mentioning
and finally is see the uh actual raw data as though the feedbacks of the people
and you can go all look more into that if that's what you're interested in
though this um sums up the products that that we have so far
um for the future so we have customer experiences we have customer support
we we have a um a product in progress for human
resources where you we can an allies um employee surveys
and and come up with summaries a lot faster than than the current
uh way of doing it manually um we have a um
product planned for uh the legal domain um as
a a big company as a big service
company they're obviously lots of contracts with other
companies and very often this number is
so high that it's very uh tedious to to an lies to conduct manually and have a good
overview of this so this again one very good use case artificial intelligence and so on
and just to finalise i would like to say i'm
against his come we're uh we're uh an
former tell co oh it turning into a service company so we're not starting
bad big with artificial intelligence but on the other hand everyone started small
well all of these big sed a big currently very big companies have started i'm
relatively small in the beginning and let it thanks a lot for your attention

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Conference program

Keynote
Jean-Baptiste Clion, Coordinator DevFest Switzerland
26 Nov. 2016 · 9:40 a.m.
How to convince organization to adopt a new technology
Daria Mühlethaler, Swisscom / Zürich, Switzerland
26 Nov. 2016 · 10:14 a.m.
Q&A - How to convince organization to adopt a new technology
Daria Mühlethaler, Swisscom / Zürich, Switzerland
26 Nov. 2016 · 10:38 a.m.
Animations for a better user experience
Lorica Claesson, Nordic Usability / Zürich, Switzerland
26 Nov. 2016 · 11:01 a.m.
Q&A - Animations for a better user experience
Lorica Claesson, Nordic Usability / Zürich, Switzerland
26 Nov. 2016 · 11:27 a.m.
Artificial Intelligence at Swisscom
Andreea Hossmann, Swisscom / Bern, Switzerland
26 Nov. 2016 · 1:01 p.m.
Q&A - Artificial Intelligence at Swisscom
Andreea Hossmann, Swisscom / Bern, Switzerland
26 Nov. 2016 · 1:29 p.m.
An introduction to TensorFlow
Mihaela Rosca, Google / London, England
26 Nov. 2016 · 2:01 p.m.
Q&A - An introduction to TensorFlow
Mihaela Rosca, Google
26 Nov. 2016 · 2:35 p.m.
Limbic system using Tensorflow
Gema Parreño Piqueras, Tetuan Valley / Madrid, Spain
26 Nov. 2016 · 3:31 p.m.
Q&A - Limbic system using Tensorflow
Gema Parreño Piqueras, Tetuan Valley / Madrid, Spain
26 Nov. 2016 · 4:04 p.m.
How Docker revolutionized the IT landscape
Vadim Bauer, 8gears AG / Zürich, Switzerland
26 Nov. 2016 · 4:32 p.m.
Closing Remarks
Jacques Supcik, Professeur, Filière Télécommunications, Institut iSIS, HEFr
26 Nov. 2016 · 5:11 p.m.
Rosie: clean use case framework
Jorge Barroso, Karumi / Madrid, Spain
27 Nov. 2016 · 10:05 a.m.
Q&A - Rosie: clean use case framework
Jorge Barroso, Karumi / Madrid, Spain
27 Nov. 2016 · 10:39 a.m.
The Firebase tier for your app
Matteo Bonifazi, Technogym / Cesena, Italy
27 Nov. 2016 · 10:49 a.m.
Q&A - The Firebase tier for your app
Matteo Bonifazi, Technogym / Cesena, Italy
27 Nov. 2016 · 11:32 a.m.
PERFMATTERS for Android
Hasan Hosgel, ImmobilienScout24 / Berlin, Germany
27 Nov. 2016 · 11:45 a.m.
Q&A - PERFMATTERS for Android
Hasan Hosgel, ImmobilienScout24 / Berlin, Germany
27 Nov. 2016 · 12:22 p.m.
Managing your online presence on Google Search
John Mueller, Google / Zürich, Switzerland
27 Nov. 2016 · 1:29 p.m.
Q&A - Managing your online presence on Google Search
John Mueller, Google / Zürich, Switzerland
27 Nov. 2016 · 2:02 p.m.
Design for Conversation
Henrik Vendelbo, The Digital Gap / Zurich, Switzerland
27 Nov. 2016 · 2:30 p.m.
Q&A - Design for Conversation
Henrik Vendelbo, The Digital Gap / Zurich, Switzerland
27 Nov. 2016 · 3:09 p.m.
Firebase with Angular 2 - the perfect match
Christoffer Noring, OVO Energy / London, England
27 Nov. 2016 · 4:05 p.m.
Q&A - Firebase with Angular 2 - the perfect match
Christoffer Noring, OVO Energy / London, England
27 Nov. 2016 · 4:33 p.m.
Wanna more fire? - Let's try polymerfire!
Sofiya Huts, JustAnswer / Lviv, Ukraine
27 Nov. 2016 · 5 p.m.
Q&A - Wanna more fire? - Let's try polymerfire!
Sofiya Huts, JustAnswer / Lviv, Ukraine
27 Nov. 2016 · 5:38 p.m.
Closing Remarks
Panel
27 Nov. 2016 · 5:44 p.m.