Thursday, December 25, 2014

Friday, December 19, 2014

Asa H with multiple memory systems

Since the beginning of the Asa H project I have employed multiple similarity measures and learning algorithms (often times integrated into one single program).  I am now playing with versions of Asa H that make use of more than one sort of memory/database/knowledge representation simultaneously.

Further use of simple parallel processing with Asa H

The Asa H architecture consists of a hierarchical memory assembled out of clustering modules and feature detectors.  I have done some feature extraction outside of the main Asa H program using an autoassociative neural network trained on the various cases from the Asa H casebase.  The hidden layer of the autoassociator network provides the feature detectors.  These networks require considerable time to train by backpropagation.  A half dozen or so networks can be trained in parallel on individual computers.

Wednesday, December 17, 2014

A chatbot passes a minimal Turing test

Prof. Kevin Warwick of University of Reading reports that on 7 June 2014 following a 5 minute chat a russian chatbot named Eugene Goostman convinced 33% of a panel of judges that it was a 13 year old boy.  This was as much luck as expertise but the scores on these tests, and natural language processing capability in general, have been slowly improving over the years.  I'm told that several online software agents have left internet users thinking that they were real humans.

Thursday, December 11, 2014

Sensors for mobile robots

One of the biggest obstacles in AI robotics is providing adequate sensor arrays.  I have acquired one or more:

ultrasonic sensor
sound sensor
force sensors
EOPD sensor
touch sensors
magnetic field probe
temperature sensors
color sensor
light sensor
current sensor
voltage sensors
digital compass
IR seeker

Most of these are for use with Lego NXT hardware. I have several multiplexers so I can use many of these at once. Some of these sensors are used to define concepts for Asa like north, south, east, and west for example.

Tuesday, December 9, 2014

Intelligences ask questions

It has been suggested that the difference between animal intelligences and human level intelligence is that humans ask questions spontaneously. Many AI programs ask questions but what about the spontaneous asking of questions and what about learning to ask questions?
My AI Asa H learns by observing and copying behaviors that it sees.  It sees agents like myself ask questions.  Under similar circumstances Asa will then also ask questions.
Prairie dogs emit warning calls when they see evidence of a human nearby.  Similarly, at the lowest level of complexity a Lego NXT robot (either real or in simulation) may emit a beep whenever it detects a red ball.  A robot with an Asa H brain could see this and learn the same call. This easily occurs in simulation (though it would be difficult with Lego hardware).
In a multirobot experiment coordination of action may involve a robot asking for help (to push a heavy load for example). See, for example, Multirobot cooperation..., Kolling and Carpin, ICRA 2006, pg 1311.  The primitive call could be understood as a question; "Will you help?" Again, Asa H would learn to ask this same question when it encountered a similar situation.

Thursday, December 4, 2014

Asa H natural language processing

I can now give Asa H a nearly 1000 word vocabulary. Enough for a telegraphic speech capability. These words are associated with concepts that are defined at various different levels in the Asa H hierarchical memory.

Wednesday, December 3, 2014


Ryle noted that good philosophical thinking required a good theory of categories.  Various philosophers in their arguments cite the category errors of their opponents.  But it is important to realize that the boundary of any given category is fuzzy and that categories evolve and change. (Just as all language evolves and changes.)  One of the things that I do in my experiments with Asa H is to follow how Asa's categories are formed and then evolve over time. (Watching thinking as it occurs in Asa's language of thought.) Running Asa H on a set of parallel computers the category activity is typically what is transmitted from one computer to the next, and from one level of the Asa H hierarchy to the next. (See my blog of 26 Aug. 2013 for an example of the simplest way this can be done.)

Monday, December 1, 2014

Do mathematical entities like numbers really exist?

Rather than Platonism (mathematical realism) I believe that numbers (and addition and subtraction and multiplication...) were developed by abstraction from earlier (physical) machinery that once existed in the real world.  Cockshott, et al, (Computation and its limits, Oxford Univ. Press, 2012) describe how this may have occurred on pages 11 through about 27. Some of my experiments with Asa H explore how abstractions are formed. It's easier to follow what's going on inside Asa than it is to understand the inner workings of a neural network program.  It's harder to follow what's going on inside Asa as compared to deductions in an expert system, however.

Higher order mathematical operations can then be composed out of addition, subtraction, and multiplication as is done with computers.

Scientism with values

I have argued in favor of a brand of scientism.  But, since I do not believe science is (or can be) value free, it is a scientism with a system of values.  (see my blogs of 20 Sept. 2013, 25 Oct. 2011 and 1 Sept. 2012)
This, as well as "scientific pluralism" (see my blogs of 8 Sept. 2011, 17 Aug. 2012 and "Changing what science is and how it's done", R. Jones, Trans. Kan. Acad. Sci., 116, 1/2, pg 78, 2013), counters some of the criticisms of the more traditional varieties of scientism. (see for example Scientism, Tom Sorell, Routledge, 1991)