Wednesday, October 19, 2016

Machine consciousness

This is a draft of an abstract for a presentation on machine consciousness that I am working on:

Isn't Spacex trying to do too many things at once?

ISS resupply, F-9 reuse, Falcon heavy, manned Dragon, ITS.
It seems to me that if you try to do too many things at once either they all come in late or some of them fail or both.

This is an update of my 30 September 2011 blog.

Friday, October 14, 2016

Attention as response

One approach to handling/modeling attention, or at least one kind of attention, is to treat it as a response, either an innate response or a learned one. Things like turning toward a stimulus, increasing  the gain on a microphone, adjusting vision magnification, turning on and bringing to bear additional sensors, etc.

Thursday, October 13, 2016

How human-like should a robot be?

There are those who argue that if a robot is made as human-like as possible this will help the two relate to each other and understand each other. See, How to Build an Android by David Dufty, 2012 and Virtually Human by Martine Rothblatt, 2014. I tend to disagree. I think this just makes the robots creepy and harder to relate to. But it is true that body form and function influences the concepts the robot will develop and use. That will aid in robot-human understanding. I think something along the lines of  Softbank robotics' Pepper is a reasonable compromise.

Wednesday, October 12, 2016


I have argued that immortality is impossible. (see my blog of 15 October 2010) I had expected, however, that there was room for a considerable increase in human lifespan.  But Michael Ramscar of the University of Tubingen says that even at our current ages "Some things that might seem frustrating as we grow older are a function of the amount of stuff we have to sift through...and are not necessarily a sign of a failing mind. A lot of what is currently called decline is simply learning." (see The Myth of Cognitive Decline in Topics in Cognitive Science, 6, 2014, 5-42) Or, as Christian and Griffiths put it "what we call cognitive decline may not be about the search process slowing or deteriorating but at least partly an unavoidable consequence of the amount of information we have to navigate getting bigger and bigger." (Algorithms to Live By, Holt and Co., 2016, page103) I am not arguing that there are not things like alzheimers (my mother died with it).  What I am arguing is that it may not be possible to have the kind of immortality some people hope for.

Monday, October 10, 2016

AAPT conference

At the American association of physics teachers conference this past weekend James Laverty of Kansas State University presented the 3D-LAP scheme (3 dimensional learning assessment protocol) for assessing the value/importance of various physics test questions.  I noted favorably that the method employs a 3 dimension vector value:
1. Scientific and engineering practice
2. Cross cutting concepts
3. Disciplinary core ideas
I am not sure these 3 are exactly what I would have come up with but I am obviously in favor of vector value systems in general.
I also was interested in the scientific and engineering practices they identify (from A Framework for K-12 Science Education):
1. Asking questions and defining problems
2. Developing and using models
3. Planning and carrying out investigations
4. Analyzing and interpreting data
5. Using mathematical and computational thinking
6. Constructing explanations and designing solutions
7. Engaging in argument from evidence
8. Obtaining, evaluating, and communicating information
Since I believe that the process of science is simply the process of intelligent thought (perhaps refined and augmented in various ways) these are then all things that my artificial intelligence A.s.a. H. should be doing too. Said another way, Asa should be able to do science.
1. Asa defines and identifies cases that lead to low utility, i.e., problems.
2. Asa's hierarchical memory creates, stores, and uses spatiotemporal patterns, models of reality.
3. Asa examines the accuracy of its extrapolations experimentally and plans future behaviors.
4. Asa examines its case memory using interpolation, extrapolation, value assessment, etc.
5. Asa is computational and uses mathematical as well as logical reasoning methods.
6. Asa designs improved behaviors to cope with problems, i.e., low utility situations.
7. Asa reasons ("argues") from evidence.
8. Asa can communicate and output its case memory.
I would like to improve upon Asa's present ability to ask and answer questions.

Friday, October 7, 2016

Attention in AI

I remain dissatisfied with our ability to focus attention.  I believe that this will become more and more of an issue as we scale up applications of AI.  One idea that might be useful is the concept of thinking about something in the right way.  Perhaps specialist AIs can be built around groups/clusters of specialized concepts, knowledge, and algorithms. What would the right clusters be?  How would we modify/learn them over time, perhaps dependent upon environment?