[ml] Systems Theory

[Brian Morris]

I am interested in the Markov stuff for Reinforcement learning.
Looks like there is a ton of Python code out there for it.

Brian


On Tue, Jul 19, 2011 at 3:47 PM, Mike Schachter <mike at mindmech.com> wrote:

> Hey Kai, thanks for the elaboration. I'm interested. Since
> Idris and Alexi are planning on talking about some data
> hacking stuff this Wednesday, how does meeting up the
> Wednesday after that (7/27) to talk about systems stuff
> sound to you?
>
> One concrete implementation of agents I want to check
> out are markov decision processes, would be interested
> in looking into stuff regarding complex systems of MDPs
> and seeing what comes out of it.
>
>  mike
>
>
> On Tue, Jul 19, 2011 at 2:43 PM, Kai Chang <kai.salmon.chang at gmail.com>
> wrote:
> > This thread is to gauge interest in a talk/discussion on systems theory
> (in
> > its broadest sense) in relation to machine learning. Please respond if
> > you're interested, with topics, theories you would be interested in
> > discussing.
> > Modern machine learning is based on a set of algorithms that have proven
> > very successful for solving broad classes of problems based on empirical
> > data. ML grew out of artificial intelligence. AI takes much of its
> > inspiration from human intelligence, however attempts to replicate HI
> have
> > experienced many frustrations and fundamental setbacks. There is
> currently
> > not a theoretical framework to study general intelligence (strong AI,
> > self-awareness, creativity, etc). In biology and neuroscience, there are
> few
> > widely accepted theories of how the material substrate of the brain
> > generates conscious experience or solves many complex intellectual tasks.
> > Systems theory attempts to provide a framework within which generally
> > intelligent beings and other sources of complexity can be reasoned about.
> To
> > use an ML example, consider financial markets. There are trading
> algorithms
> > that capitalize on market inefficiencies on short timescales. The
> existence
> > of the algorithms is known to market participants, and algorithm-makers
> can
> > create new algorithms based on their experience with existing algorithms.
> > Firms, governments and people can adjust their behavior based on
> > observations of the algorithms' effect on the market. In this way, the
> > system is highly reflexive (economic systems are already, algorithmic
> > trading adds another layer of reflexivity). For instance, the May 2010
> Flash
> > Crash brought widespread media attention. Media attention beings new
> > dynamics and social forces into play. The flash crash itself could be an
> > example of weak emergence, where the combined properties of individual
> > algorithms create systemic instability.
> > The above situation can be considered a complex adaptive system.
> > More broadly, I'm interested in systems theory because it creates a
> > framework of discussion where expert knowledge from disparate fields,
> > philosophical, linguistic and intuitive concepts can be considered as
> > systems with relationships to each other. Everyone has a unique
> perspective
> > on the topic, insofar as we are all generally intelligent beings composed
> of
> > internally complex systems, interacting with external complexity, and
> > communicating with other generally intelligent beings.
> > Hopefully we would generate inspiration for new machine learning
> projects,
> > and gain a broader appreciation for machine learning's impact and
> potential
> > on systems familiar to us.
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> >
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