Conceptualization as a Basis for Cognition

What do we mean by 'understanding'?  Can we check that off if it means we can perform some specified tasks?

Conceptualization as a Basis for Cognition — Human and Machine

A missing link to Machine understanding and Cognitive AI

By Gadi Singer  in TowardsDatascience

 While most contemporary discussions and classifications of AI capabilities center around what a system can do, I believe the path to higher intelligence and machine cognition relies on what a system can know and understand. Using rich AI knowledge representation frameworks and comprehensive models of the world can increase an AI system’s ability to transform information into deep knowledge, understanding, and functionality. To pursue this path to better AI, it is essential to understand what “understanding” really means for the human brain. Doing so allows for implementing frameworks that enable machine learning to parallel human understanding by integrating modeling and conceptualization with data and task generalization.

Conceptualization: The Basis for Human Thought

“Concepts” are the most basic building block in human thinking. Concepts serve as ontological roots for objects that we think about. Concepts represent a persistent set of essential attributes of an object class, which can change and expand with experience. Existing concepts can be abstracted or linked through analogy to additional domains and object classes. Examples of concepts include |dog|, |democracy|, |white|, and |uncle|. Physical or mental objects can be stored as a concept and accrue more data and attributes over time (e.g., |my dog Lucky| and |snow white| versus |off-white|). Even if the referent is invisible or abstract, like |love|, it can still be stored as a concept. Our understanding of the world relies on concepts, attributes of concepts, and relationships between concepts. We use concepts and facts composed of concepts and the relations between them to construct our world model. ... '

Nature of Human Trust in Machines

This topic came up in a recent discussion of AI.  Past evidence had said that in certain contexts people trust AI better than humans,  simplistically because the machines have no ulterior human motives.   But it came up that human goals could also be installed into them by humans.  I like the idea of classifying trust, had not seen that before.   Not also the inclusion of sensors,  how, why and when do we trust sensors?   And how does the inclusion of collaboration change the dynamic of trust?

New Models Sense Human Trust in Smart Machines 
Purdue University News

Purdue University researchers are using new "classification models" to assess the extent of humans' trust in intelligent collaborative machines. Purdue's Neera Jain and Tahira Reid created two types of "classifier-based empirical trust sensor models," which use electroencephalography (EEG) and galvanic skin response to gauge levels of trust. Forty-five research subjects wore wireless EEG headsets and a device on one hand to measure these factors. A "general trust sensor model" used the same set of psychophysiological features for all subjects, while the other model was tailored for each participant; the models had respective mean accuracies of 71.22% and 78.55%. Said Jain, “A first step toward designing intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real time.” ... " 

Merging Brains and Machines

An odd debate.  But regardless we will have to collaborate.   We have been doing this for some time, but the the details of how this is done is getting more important. ....

Merging our brains with machines won’t stop the rise of the robots
February 26, 2017

Tesla chief executive and OpenAI founder Elon Musk suggested last week that humanity might stave off irrelevance from the rise of the machines by merging with the machines and becoming cyborgs.

However, current trends in software-only artificial intelligence and deep learning technology raise serious doubts about the plausibility of this claim, especially in the long term. This doubt is not only due to hardware limitations; it is also to do with the role the human brain would play in the match-up.

Musk’s thesis is straightforward: that sufficiently advanced interfaces between brain and computer will enable humans to massively augment their capabilities by being better able to leverage technologies such as machine learning and deep learning.

But the exchange goes both ways. Brain-machine interfaces may help the performance of machine learning algorithms by having humans “fill in the gaps” for tasks that the algorithms are currently bad at, like making nuanced contextual decisions. ...   " 

Tiniest Legos are Ready to Work for us

In CACM:    The robot moves slowly along its track, pausing regularly to reach out an arm that carefully scoops up a component.  ...  The tiniest Lego: a tale of nanoscale motors, rotors, switches and pumps ... Inspired by biology, chemists have created a cornucopia of molecular parts that act as switches, motors and ratchets. Now it is time to do something useful with them.    ... "   Full article pointed to in Nature.