[ml] Systems Theory

[Mike Schachter]

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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