We believe that the future of algorithmic information dynamics
and model driven approaches are bright,
and we would like you to be instrumental
in this change of vision towards this science
based on models and computation.
We believe that the way forward is
to complement statistical approaches and current approaches
with better algorithmic tools and methods.
There are many challenges in the area.
Some of them involve finding ways to connect
the discrete nature of computation
and the continuous nature of data and other areas,
such as differentiable programming and deep learning
that are currently heavily based on continuous functions,
but also with other model driven approaches,
such as differential equations.
One first major milestone achieved
is the numerical calculation of algorithmic probability by BDM,
and showing its stability and usefulness,
because our approaches produce actually real continuous values
from an empirical distribution
in contrast to integer values, as, for example,
from using lossless compression algorithm on Shannon entropy
in the estimation of randomness and algorithmic complexity.
So, one can use BDM to replace the cos function,
for example in deep neural networks
and still keep it differentiable, in principle.
But most work in this direction is yet to be done,
and we have projects in which we think you could get involved
if you wish, as we speak.
To tackle these tasks and exciting new areas of research,
we are creating working groups to which you can get associated.
There will be team leaders that
will be guiding the various research groups,
divided by research projects.
Among those leaders will be likely Narsis, Alyssa and I,
together with some other brilliant collaborators in different areas,
from our collaborating network.
Each project will aim at publishing one or more papers.
And I will be hoping to consider papers
for the journal 'Complex Systems'
assuming I'm not listed as an author.
And, if I'm listed as an author,
assuming I have contributed enough,
then I'm sure we will find the best journal for the paper.
If you're interested in getting involved,
join the online group on your screen.
You can even work on your own, as some people prefer.
And of course, you don't need to join any group
to conduct research on your own
on algorithmic information dynamics or using our tools.
Finally, I want to warmly thank you,
all of you, to have followed our course.
We know there is a long room for improvement
and this course will continue running
and hopefully getting better in the next years.
But you have been the first generation
and we are extremely grateful for it.
Our algodyne team thanks you again.
Thank you very much!