Re: W3C Cognitive AI - Re: COG ai definition?

Dave and all
please let this answer not be lost! If you post it somewhere meaningful, it
can be referenced
for the future, perhaps with sources/references
are you saying chunks are neurosymbolic representations?


On Mon, Sep 21, 2020 at 11:32 PM Dave Raggett <dsr@w3.org> wrote:

> Hi Amélie,
>
> Thanks for the pointer.  Chunks are a data structure for representing
> n-ary relationships, and are a common approach in Cognitive Science,
> although the precise details may vary across projects. Each chunk is a
> collection of properties that reference other chunks. Whilst a minimalist
> approach to chunks limits property values to chunk identifiers, it makes
> sense to look at more flexible approach. This includes support for a small
> set of data types for literal values, e.g. numbers, booleans, strings and
> dates, as well as to allow properties to have a list of values rather than
> being restricted to a single value. That simplifies the authoring of chunks
> and rules, although it doesn’t change the expressive power.
>
> Chunk rules are condition-action rules expressed in terms of chunks.  A
> rule has a conjunction of one or more conditions that are matched against
> the buffers for cognitive modules (i.e. cortical regions). Each module has
> a single buffer that can hold a single chunk.  This follows John Anderson’s
> work on ACT-R, and from a neuroscience perspective, the buffer state
> represents the concurrent firing pattern of a bundle of nerve fibres
> connecting to particular cortical regions. See Chris Eliasmith’s work on
> simulating pulsed neural networks and his concept of “semantic pointers”.
> This also relates to David Marr’s three levels of analysis: computational,
> algorithmic/representational, and implementational. The functional
> requirements at the computational are phenomenological and essentially
> independent of the implementation layer.
>
> One major difference of Chunks from Property Graphs and RDF is the
> combination of symbolic information (graph data) with sub-symbolic
> information (statistics). This is needed to mimic human memory and
> reasoning. It can also be related to Web search engines which track the
> graph formed by hypertext links and ranks pages in ways that model the
> expected relevance of a given page to a user’s query.   In a large
> cognitive database, memory recall should return the most relevant matches,
> unlike conventional databases which return all matches.
>
> Another distinction is between formal semantics and operational semantics.
> RDF and OWL are founded on description logics and part of the Aristotelian
> tradition in which formal rules are used to deduce the logical implications
> of the assumed facts.  Despite its mathematical appeal, a reductionist
> approach to formal semantics isn’t a good fit to the everyday semantics of
> human language, where concepts are informal and context dependent, as well
> as subject to a lack of certainty and completeness. The meaning of words
> are defined in dictionaries in terms of other words and common usage
> patterns. Moreover, as Philip Johnson-Laird has shown in his work on mental
> modals and human reasoning, we don’t rely on logic and probability, but
> rather by thinking about what is possible.
>
> Chunks and Property Graphs have in common that they rely on operational
> semantics in terms of the relationship between perception (data input),
> reasoning and actuation (data output).  Industry is showing a rapid uptake
> for Property Graphs as compared to RDF, so operational semantics are
> clearly good enough for many business needs, and allegedly easier to work
> with compared to formal approaches. One example that may help clarify this
> is the statement “most people like ice cream”.  This is beyond first order
> logic, as it involves counting, statistics, and fuzzy concepts such as
> “like”.  Despite that, it can be readily expressed as a simple graph, and
> used with rules and graph algorithms for reasoning.
>
> Sorry if this was too long an answer!  :-)
>
> Best regards,
> Dave
>
> On 21 Sep 2020, at 14:04, Amélie Gyrard <amelie.gyrard@trialog.com> wrote:
>
> Hello,
> Regarding the definition of Cognitive AI, this book can help:
>
>    - Artificial Cognitive Systems – A Primer [Vernon 2015]
>    <https://www.amazon.fr/Artificial-Cognitive-Systems-David-Vernon/dp/0262028387>
>
>
>    https://www.amazon.fr/Artificial-Cognitive-Systems-David-Vernon/dp/0262028387
>
> If we are are not agree with those ones, we can compare
> the different definitions and common keyphrases/terms.
>
>  I remember it explained the chunks as well.
> To me, chunks are similar to rules (from rule-based systems)<image.png>
>
>
>
> Le lun. 14 sept. 2020 à 08:01, Paola Di Maio <paola.dimaio@gmail.com
> <paola..dimaio@gmail.com>> a écrit :
>
>> Ron and all
>>
>> - since we are educating ourselves :-) -
>> I wonder if someone may know where the definition COG AI comes from
>>
>> I first started studying AI around the nineties, and got an MSC in 2000,
>> but we never used this term
>> we used KBS (knowledge based systems)
>>
>> here it says cognitive computing came about in 2014
>> ttps://
>> cognitivecomputingconsortium.com/definition-of-cognitive-computing/
>>
>> thank you!
>>
>>
>> On Mon, Sep 14, 2020 at 11:04 AM Paola Di Maio <paola.dimaio@gmail.com>
>> wrote:
>>
>>> Thank you Ronald for setting this up
>>> I should be able to make it
>>>
>>> For me, AI has always been cognitive AI - probably because I started
>>> learning AI
>>> from knowledge based systems (long ago), I never felt the necessity to
>>> call AI cognitive
>>> (i understand that given the spike of ML this disambiguation may be
>>> useful now)
>>> at the same time, I have been practicing all along for thirty years
>>> (unlabelled, and unaware perhaps
>>> that a discipline was forming )
>>>
>>> My suggestion is to try make the call a bit participatory, make sure
>>> that whoever is on the call
>>> can contribute to the call agenda and bring in their
>>> perspective/experience to whatever is the agenda goal
>>>
>>> Its good to learn but  to "éducate'' sounds as if people dont know about
>>> cogAI already, like a bit patronizing perhaps?
>>> what about co-learn :-)
>>>
>>> I am a constructivist by nature
>>>
>>> P
>>>
>>>
>>>
>>> On Mon, Sep 14, 2020 at 2:38 AM Ronald Reck <rreck@rrecktek.com> wrote:
>>>
>>>> Hello Cognitive AI Community group,
>>>>
>>>> Our first conference call is scheduled for
>>>> September 21, 2020 at 1 PM London time.
>>>> Contact information will be sent out later this week.
>>>>
>>>> The agenda is as follows:
>>>>
>>>> 1. Educate - gentle introduction to the topic of cog-ai
>>>>
>>>> 2. Outreach - Discuss how to extend reach out beyond
>>>> our current group. We seek to bridge the technical
>>>> clique mindsets as the topic is interdisciplinary.
>>>> It involves traditional AI (deep learning),
>>>> natural language processing, logic, pragmatics,
>>>> cognitive science, and semantic web.
>>>>
>>>> 3. Use cases - Understand and document business cases
>>>> especially around machine & human collaboration. This hopes
>>>> to drive funding.
>>>>
>>>> 4. AI ethics / explainability
>>>>
>>>> As we are still in the early stages, there is
>>>> much exciting work to be done, we need to consider
>>>> how to involve different orientations to incubate
>>>> a paradigm shift so that future intellectual effort
>>>> is exerted in the most effectively toward AI's ability
>>>> to enhance society.
>>>>
>>>> Please feel free to comment or make suggestions!
>>>>
>>>> -Ronald P. Reck
>>>>
>>>>
>>>>
>
> --
> *Amelie GYRARD*
> TRIALOG SAS, 25 rue du Général Foy, 75008 PARIS✆ +33 1 44 70 61 25
> ✉ amelie.gyrard@trialog.com
> www.trialog.com
>
>
> Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
> W3C Data Activity Lead & W3C champion for the Web of things
>
>
>
>
>