Home > Uncategorized > Weekly QuEST Discussion Topics and News, 20 Jan

Weekly QuEST Discussion Topics and News, 20 Jan

QuEST 20 Jan 2017

We want to continue our discussion of the Kabrisky lecture – What is QuEST? – and along that line we want to provide more specifics in our use in our Theory of Consciousness of the word ‘situated’ and the word ‘simulated’ and the idea of structural coherence (seems to be the embodied term and related to situated in the psychology literature).

As our use of the term are not consistent with some usage (for example in the Journal of Cognitive Engineering and Decision Making – article in Press by our QuEST colleagues Patterson and Eggleston) – over the last two weeks we have reconciled the terms – at least in the mind of Cap.

What we need to discuss this week is how to engineer systems that have the desired ‘situated’ / ‘simulated’ / structurally coherent nature using current approaches to machine learning and artificial intelligence.

We will initially emphasize applications in making ‘intuitive’ machines that act as interactive multi-sensory content curators.  We define intuition as the quale that humans experience as a result of a ‘sub-conscious’ computation’s outcome being attended to in working memory (thus becoming conscious of the result) without the details of the deliberation.  As intuition is by this definition a quale a machine that replicates that computation is an artificially conscious machine (we define consciousness as the generation of qualia – and when we say above a machine that replicates that intuition computation we are including the constraint that the reproduction includes instantiation of the key defining engineering characteristics of all qualia – situated / simulated / structurally coherent).

The second characteristic of intuition that is emphasized by our colleagues is the use of ‘recombinations’ of prior experiences / memories that can be used in the ‘sub-conscious’ computation.  In the QuEST models we use the word ‘simulation’ to capture this similar idea that the computation of all qualia is the generation of a representation that much of the content of the representation is inferred versus a recall of previously experienced episodes.

In summary:

Common ground –thanx to our 711th colleagues we’ve converged on a path that is comfortable with respect to using terms and our focus on engineering computational characteristics that currently are not emphasized in AI/ML solutions but appear to be key constraints in consciousness

In our QuEST world we define ‘intution’ – as the quale that is evoked in consciousness to provide an actionable conclusion to a computation that is being accomplished without conscious awareness of the details of the deliberation – ‘I think walking into this environment is not a good idea’ – ‘I’m not going to enjoy this class’ – ‘that boy is not the right match for my daughter’, ‘that truck rumbling down the hill out of control towards a gas station is a bad thing’ …

As all qualia – the representation is the key not only for the experience but for the computation of the quale – and in the literature (thanx to Robert, Bob, Anne — they’ve shown us how our QuEST developed tenets can be reconciled with their view of intuitive cognition) – it is clear that it has to instantiate being situated, simulated and be structurally coherent consistent with the tenets we’ve developed in QuEST over the last decade

So as a clear first step in our endeavor to make a fully conscious computer we seek making a computer with intuition – and I’m still convinced the best first step for this is in content curation – the computer that ‘feels’ you might be interested in this part of the multi-modality (audio, video, text, …) world versus that part of the sensory streams and based on your response to what it provides you (both estimating your conscious and subconscious representational states ~ the system is emotionally intelligent) changes the interaction (what it provides you next) – by the interaction it increases your emotional intelligence also

In this world that is going towards ubiquitous computing, virtual and augmented reality – this intuitive computer will always be on and learn from natural sources be multi-sensory and will reason (manipulate its own representation and do so to facilitate accomplishing tasks thus understanding) – and part of that manipulation will form new qualia via imagining unique combinations of existing qualia ~ chunking to facilitate gisting and a key means for abstraction – our use of the word simulation

 

The other item on the agenda is Cap has to give several talks coming up and generate some material on historical perspectives in neural science and also computational models associated with machine learning and artificial intelligence so we will have some discussion along those lines.

 

Specifically – in one recent study cap presented at it was concluded that:

Operationally AI, it can be defined as those areas of R&D practiced by computer scientists who identify with one or more of the following academic sub-disciplines: Computer Vision, Natural Language Processing (NLP), Robotics (including Human-Robot Interactions), Search and Planning, Multi-agent Systems, Social Media Analysis (including Crowdsourcing), and Knowledge Representation and Reasoning (KRR).  In contradistinction to artificial general intelligence:

  • Artificial General Intelligence (AGI) is a research area within AI, small as measured by numbers of researchers or total funding, that seeks to build machines that can successfully perform any task that a human might do. Where AI is oriented around specific tasks, AGI seeks general cognitive abilities. On account of this ambitious goal, AGI has high visibility, disproportionate to its size or present level of success, among futurists, science fiction writers, and the public.

We will want to pull on these threads with respect to the breakthroughs in deep learning and the promise of other approaches to include unsupervised learning, reinforcement learning …

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