Few important remarks about the keynotes and my point of view about the topics, having in mind the objectives of my thesis:
From keynote: Artificial Intelligence: Friend or foe?
By NicolasDemassieux.
·
Besides the definition of a
goal and find out a way to reach a goal, that path towards the problem
resolution has to be bounded to a specific and delimited scenario.
·
A.I. is designed to do only one
task the best way possible. If the problem specification changes or if there is
a change of the rules governing the problem, the whole learning process should
be restarted again.
·
Real problems of Artificial
Intelligence:
o Errors: what are their nature and what are the consequences of the
errors (i.e. false positives / false negatives).
o Algorithmic bias: The bias is lower if there is a richer data base
to learn from.
o Explanation of actions. At this moment, Artificial intelligence is a
black box. There is a trend to build an “explicative AI” in order to have an
explanation of the undertaken actions.
“Gold rules” for a reasonable Artificial
Intelligence:
·
Information of the purpose and
possible debate of it.
·
Involvement of the users.
·
Supervise the algorithms: it
would be great to have algorithms that verify other algorithms, for example, to
verify there is no bias.
Artificial intelligence can be used for
evil and good. It empowers “both sides of the coin”. We must leverage on it to
learn how to stop cyberattacks (among other use cases).
For the moment, there is no way to
express emotion from Artificial Intelligence. There is limitation in how to
interact with a system to do so. The machine can imitate emotion, but no more
from that.
Afternoon workshop: Sensors and data.
·
Automotive sector is embedding
more sensors into the vehicles. Not only internal sensors are needed, but also
environmental ones. It is migrating from just a providing status of a system,
into providing a service for the user: postural and biometric information.
·
Main architectural concept is
to connect all sensors to a local gateway. This gateway is then connected to
the cloud provider via a radio link.
·
Data should be shared, in order
to have social, environmental and economic benefits.
·
Considerations on the
transmission of data via mobile network depends on the nature of the data:
o Is it cold or hot data?
o Is it volatile or is it considered as long-duration information?
o Should it be stored on-board or in the cloud?
·
All of these questions should
be answered depending on the real-time process needs. The main objective is to
transform data into services. It has to be useful. That’s why the quality of
the data must be good in order to have a “good artificial intelligence”.
·
It is not new the consideration
to use A.I. to deal with the complexity of networks. Considering the topic of
my thesis, it is interesting to consider this approach to enhance the security
of network slices. Since the security issue related to slicing is broad and
involves several levels according to what problem to tackle, it is necessary to
understand not only the architecture but to know where to focus in order to
apply this technique properly.
·
A.I. as is well known, can be
used as a mechanisms to identify network attacks and prompt with insight on how
to stop them according to is fingerprint.
·
Approaches to security can be
considered as local (i.e. on the edge) or centralized depending of the data
involved, level of the affected infrastructure and scope of attack.
·
Since A.I. solves a particular
problem, and we would like to use it to identify multiple security problems,
different types of data traffic must be captured, using different scenarios. The
more diverse data, the better training information for A.I. engine.
·
Another interesting application
could be to have an A.I. “chaos monkey” that could be used to test the A.I.
traffic and attach identification system in a network. Would be useful for
pen-testing and evaluate the principal algorithm.
