[Noisebridge-discuss] [ml] I am interested in starting an optimization group at noisebridge (BetterBridge? TrollSearch?)

Sat May 28 23:56:58 UTC 2011

I did cognitive science at UVA, and there the approach was quite different.
The curriculum seemed heavily influenced by MIT, with more focus on
linguistics, pyschology and philosophy. Not sure what the approach at CMU is
like.

At Stanford, the emphasis seems to be on optimization, classification, etc.
The problem requires a fitness function or other well-defined metrics to
test against. More related to math and statistics than cognition in general.

For broader questions of cognition and learning, there are several other
traditions. Logicians and linguists: Wittgenstein, Godel and Saussure.
Phenomenologists: Heidegger, Merleau-Ponty, Marx. Philosophers: Hume,
Nietzsche, Deleuze. Buddhism also has very nuanced views on the subject.

These traditions share doubts about symbolic formalism and rationality as
sufficient means of describing human intelligence and experience.

For example, we have overwhelming evidence that neurons serve as building
blocks in networks with the capability to learn broad classes of problems.
But we still have no foundation to demonstrate that neurons produce our
phenomenal experience (the rich, inner, subjective world).

Anyways. Crutcher, I'll drop by next week. I got really busy last time ML
was doing a group project. I'd be interested in hearing about these other
applications of metaheuristic search too!

On Fri, May 27, 2011 at 8:14 PM, David Faden <dfaden at gmail.com> wrote:

> West = http://www-stat.stanford.edu/~tibs/ElemStatLearn/ ?
>
> What's the East Coast book?
>
> How would you classify http://aima.cs.berkeley.edu/ ?
>
> Interesting to hear about this division. Thanks.
>
> On Fri, May 27, 2011 at 7:03 PM, Brian Morris <cymraegish at gmail.com>wrote:
>
>> I am very much wanting the more doing / project as-a-group thing.
>>
>> I probably won't make it to the ML meeting this coming week tho (most 3/4
>> weeks I am there)
>>
>> There are two perspectives of what Machine Learning is, might call for
>> lack of better terms the Stanford perspective and the Carnegie-Mellon
>> perspective (from the location of the authors of two popular texts), or East
>> and West if you like.
>>
>> I generally take the East side and Mike the west,  but also would like to
>> work on more general problems with a group, maybe  less Statistical Learning
>> ... and / or problems which have orderings rather than actual or precise
>> numerical values (ordinal variables), so that optimization is possible even
>> if formulas cannot be given or numerical data is either too imprecise or
>> simply unavailable. [Like if you have labels like letter grades, but not
>> numerically based, how best to assign / design content ?]
>>
>> Maybe applies to some problems in knowledge management, natural language /
>> linguistics problems, policy decision making (how to maximize job
>> satisfaction for instance), intelligence analysis.
>>
>> Kinda short on specific problems, might have to Troll Search them.
>>
>> By the way, what's the best way to produce a table of contents rather than
>> an index (given say a bunch of text scraped off the Web) ?
>>
>> On Fri, May 27, 2011 at 1:27 PM, Crutcher Dunnavant <crutcher at gmail.com>wrote:
>>
>>> The ML group seems to have grown up quite a bit since the last time I
>>> paid attention; and I think I should start participating; as the page lists
>>> many things I'd like to learn and play with.
>>>
>>> I am specifically suggesting a group-project oriented group; rather than
>>> a research group or class. Something that would yield finished projects;
>>> something where we collaborate on a common code base and problem set, beat
>>> it to death, publish it (5mof?); and move on to the next one.
>>>
>>> There are many applications of metaheuristic search outside machine
>>> learning; and I don't want to hijack a group which looks healthy.
>>>
>>> On Fri, May 27, 2011 at 12:00 PM, Mike Schachter <mike at mindmech.com>wrote:
>>>
>>>> Hi Crutcher,
>>>>
>>>> I'd be interested in black box optimization. The machine learning
>>>> group meets up on Wednesdays at 7:30pm in the Church classroom:
>>>>
>>>> https://www.noisebridge.net/index.php?title=Machine_Learning
>>>>
>>>> Just speaking for myself, I'd be happy to see you share time/space
>>>> with the ML group to talk about optimization, as it's a core part of
>>>> machine learning.
>>>>
>>>> We don't have anything going on next week, and you're welcome to
>>>> come in to talk about stuff, I'd be happy to discuss optimization with
>>>> you!
>>>>
>>>>  mike
>>>>
>>>>
>>>>
>>>> On Fri, May 27, 2011 at 10:10 AM, Crutcher Dunnavant <
>>>> crutcher at gmail.com> wrote:
>>>> > I am very interested in starting a black box optimization search group
>>>> at
>>>> > noisebridge. This field is called "metaheauristics"; but the name is a
>>>> > stupid historical artifact (so says everyone in the field).
>>>> > Optimization is, given a function f(x), searching for the x which
>>>> yields the
>>>> > best f(x). Black box optimization is a sub-field of optimization where
>>>> you
>>>> > can't analyize the function f to determine what values of x are likely
>>>> to be
>>>> > good; so you have to search the space for them.
>>>> > The following algorithms are ALL metaheuristic optimization:
>>>> > Hill Climbing (aka. Gradient Assent/Descent)
>>>> > Genetic Search
>>>> > Genetic Programming
>>>> > Ant Colony Systems
>>>> > Particle Swarm Optimization
>>>> > I've recently read a fabulous undergraduate text on the subject, very
>>>> > approachable, called "Essentials of Metaheuristics".
>>>> > The book in question is available from Lulu and Amazaon:
>>>> > http://www.cs.gmu.edu/~sean/book/metaheuristics/
>>>> > or you can just download the PDF.
>>>> > http://www.cs.gmu.edu/~sean/book/metaheuristics/Essentials.pdf
>>>> >
>>>> > If you aren't sure what I'm talking about, read the first chapter or
>>>> two. If
>>>> > you have a background in programming, you should be able to follow it
>>>> > trivially.
>>>> > What I want TrollSearch to do: Build Shit
>>>> > Let's find interesting problems; and build search algorithms over
>>>> them. This
>>>> > can apply to evolving good fit 3d models for the printer; making
>>>> techno; or
>>>> > identifying penii.
>>>> > I'd like TrollSearch to look much more like SpaceBridge than like the
>>>> Python
>>>> > Class.
>>>> > Please comment in-thread if you are interested.
>>>> > --
>>>> > Crutcher Dunnavant <crutcher at gmail.com>
>>>> >
>>>> > _______________________________________________
>>>> > Noisebridge-discuss mailing list
>>>> > Noisebridge-discuss at lists.noisebridge.net
>>>> > https://www.noisebridge.net/mailman/listinfo/noisebridge-discuss
>>>> >
>>>> >
>>>>
>>>
>>>
>>>
>>> --
>>> Crutcher Dunnavant <crutcher at gmail.com>
>>>
>>> _______________________________________________
>>> Noisebridge-discuss mailing list
>>> Noisebridge-discuss at lists.noisebridge.net
>>> https://www.noisebridge.net/mailman/listinfo/noisebridge-discuss
>>>
>>>
>>
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