- From: Paola Di Maio <paoladimaio10@gmail.com>
- Date: Tue, 20 Jul 2021 19:38:04 +0800
- To: ProjectParadigm-ICT-Program <metadataportals@yahoo.com>
- Cc: W3C AIKR CG <public-aikr@w3.org>
- Message-ID: <CAMXe=SpgYKzE_RhCRquVy7zSs7aGMbKBgMo_5XS3qVDAeBidDw@mail.gmail.com>
Thank you for the insights it would be good to get CG activities designed around these points <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=icon> Virus-free. www.avast.com <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=link> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> On Sat, Jul 17, 2021 at 3:21 AM ProjectParadigm-ICT-Program < metadataportals@yahoo.com> wrote: > The way out of the "mess" of representation is to look at what linguists > and philosophers say about natural language representation and "formal" > representation. > > Six things here are key, (1) observation and coupled with it sense > perception, (2) neural processing of perceived data, (3) encoding for > storage,(4) retrieval of stored information for recognition, (5) > adaptation of stored information and (6) cognition. > > Neuroscience, in particular neural circuits and systems, cognitive and > behavioral neuroscience and computational neuroscience have made great > strides in the study of the biological underpinnings of cognition and > related processes. > > The emerging picture is of a highly complex functionality where the brain > can create structures of up to 11 dimensions for storage (Source: Blue > Brain project). At the cellular level quantum effects can come into play, > and for short term memory, long term memory and memory search, biological > processes involving genes activated tor trigger release of biochemical > compounds, even triggered snapping of DNA residing in the nuclei or other > parts of brain cells at precise breakpoints, all create a very complex > system of interacting foci in the brain each contributing to network > activity that can lead to recognition/comparison with stored data, > (adapted) storage, cognitive processes leading to action etc. > > Our (western) concepts of knowledge representation are based on formal > mathematical systems and logical systems. Godel, Turing, Church and Chaitin > have pointed out the limitations of mathematics, logic, information science > and computability and according paradigms for knowledge representation. > > The dichotomy of mathematical abstractions (which may represent abstracted > internalized perceived real world objects and their properties), and real > world sense perceived objects is subject to quantum effects and a fiercely > debated field of philosophical inquiry. > > It should be clear that knowledge representation based on purely > mathematical and logical formal systems will not do. > > Graph theory, knowledge graphs, category theory and the theory of complex > adaptive systems are useful tools in describing some aspects of the > complexity of processes, where the emphasis is less on the objects and > their properties and more on the interrelated processes. > > I recommend we take the point of view of seeing learning (to adapt) as the > key ingredient. Sentience, sapience and consciousness, and perception and > cognition are currently not formally describable. > > The "learning" viewpoint makes the most sense, both biologically but also > in the artificial intelligence domain. If we can somehow add free will, > i.e. (the axiom of) choice, causal reasoning to the now existing mix, we > may be able to advance the field of artificial intelligence. > > There are however three major hurdles to overcome, (1) to create > explainability, (2) bias, (3) and ethical issues. > ' > > Milton Ponson > GSM: +297 747 8280 > PO Box 1154, Oranjestad > Aruba, Dutch Caribbean > Project Paradigm: Bringing the ICT tools for sustainable development to > all stakeholders worldwide through collaborative research on applied > mathematics, advanced modeling, software and standards development > > > On Wednesday, July 14, 2021, 11:04:57 PM ADT, Paola Di Maio < > paola.dimaio@gmail.com> wrote: > > > This is an interesting talk relevant to this CG as cognitionm AI KR and > Neuroscience are converging > https://www.youtube.com/watch?v=OAmB5SOS2LQ > > Neural representation is a key neuroscientific concept meant to bridge > brain and mind, or brain and behavior. But what is meant exactly by a > “neural representation”? Conventionally, a neural representation is a > correspondence between something in the brain and something in the world, a > “code”. The encoding view of representations faces two critical issues, > empirical and theoretical. Empirically, I will show that neural codes do > not have the properties required to naturalize mental representations. > Theoretically, it raises the problem of “system-detectable error” > (Bickhard): if the brain sits at the receiving end of the code, then how > can it know if the representation is wrong? As John Eccles has concluded, > the logical implication is dualism – there must be a “decoder” that > translates brain properties to world properties. Consequently, a number of > authors have argued that representations are not only homuncular but also > unnecessary: adapted behavior results not from calculations on an internal > copy of the world, but from coupling between body and world – “the world is > its own best model” (Brooks). Anti-representationalism introduces crucial > concepts missing from the conventional view (embodiment, autonomy, > dynamicism) but it struggles to explain some aspects of anticipation and > abstraction. I argue that the problem with representation is to think of it > as a “thing” that can be manipulated and observed (like a painting), which > collides with the dynamical nature of brain activity. I suggest to shift > the focus from the encoding properties of brain states, a dualistic > concept, to the representational properties of brain (and body) processes, > such as anticipation and abstraction. > >
Received on Tuesday, 20 July 2021 11:39:56 UTC