Texai

As reported by Popular Science, here is a new video from MIT’s CSAIL lab demonstrating their small autonomous robotic helicopter. The video shows a compact yet very able platform, integrating many sensors for flight dynamics and environment mapping. The MIT team won the 2009 International Aerial Robotics Competition. Watch the whole video to see the mapping illustrations. The technical paper that fully describes their work is: Autonomous Navigation and Exploration of a Quadrotor Helicopter in GPS-denied Indoor Environments.

This paper has a diagram showing the multi-level hierarchical control system that is structured as I’ve come to expect from competition-winning autonomous vehicles. The lowest sub-symbolic level maintains the helicopter’s pitch and roll within a 1 ms feedback control loop using accelerometers and gyroscopes. The next higher sub-symbolic level operates a 100 ms feedback control loop to maintain pose and obstacle avoidance with respect to the helicopter’s surroundings using a laser rangefinder and stereo cameras. The final and topmost level is symbolic, performing trajectory planning with three second temporal granularity.

It appears that the symbolic processing can be performed by the helicopter’s on-board lightweight processing power. In contrast, certain heavy computational tasks, which I suppose includes sub-symbolic realtime perception of stereo images, are performed by a wireless networked laptop computer cluster. I think that this research points to how domestic and workplace robots will be organized. Mobile robots which have a local operating environment need not carry around heavy unshared computer processors. Rather, when wireless bandwidth permits, a networked computational resource can occupy a fixed location, and offload heavy sub-symbolic computational tasks from the mobile robot.

I bring this research to your attention because it demonstrates how robust symbolic processing can be grounded to the real world by a hierarchy of increasingly less abstract sub-symbolic levels, and because I believe that this architecture is generally applicable to AGI.

On the AGI-list, Mike Tintner said:

I’m quite sure you [another list member] haven’t the teeniest hope of building a linguistic AGI as you and others envisage . The reality check I’d suggest is – can you/your machine understand or construct *two* consecutive sentences? (Can you IOW [in other words] understand a *text* rather than individual, isolated sentences?) No current method can – or ever will. Basically because understanding or constructing a text is not just a matter of putting together symbolic words, but constructing an imaginative model/scenario of what is going on in and behind the text – and extensively “reading between the lines.” Linguistic processing that works will therefore require not just one but a whole series of truly massive creative leaps. My advice to any systembuilder  – don’t go near it if you value your sanity. One creative leap at a time.

By happy coincidence, Mike had steered the worthwhile discussion thread directly into a development approach I’ve recently adopted for Texai.

Continue Reading »

While testing the initial Texai dialog for the acquistion of English noun plural word forms, I found what turned out to be a two and a half year old design error in the Texai lexicon.  The error was manifested as a loss of upper or mixed case word form information, especially with regard to associated word senses.  For example, the word ‘in’ most commonly has word senses that correspond to its use as a preposition.  However, the lexicon also associated  ‘in’ with the word sense for the US state of Indiana, which should be ‘IN’. The full magnitude of the Texai lexicon error did not become clear to me until after I wrote and applied a dozen long-running fix programs.

My design error started years ago when I incompletely adapted an open source  relational data model for WordNet.  For ease of searching, WordNet provides an index into its synonym sets, i.e. synsets, in which all word forms appear in lower case.  My mistake was using this lower case data element as the uninflected word form.  Correcting the design error required redoing the Texai lexicon from two of its original components: WordNet and Wiktionary.   Although the CMU Pronouncing Dictionary was also a source in the original Texai lexicon merge, I omitted it this time around because there are no immediate plans to incorporate the Sphinx automatic speech recognition system for voice input.  Words in the CMU dictionary are all given in upper case, so I would have a somewhat complex program to link them up to the now properly cased Texai lexicon. When redoing the lexicon from scratch, I took advantage of extracting from the latest 3.0 version of WordNet, and also extracting from the latest Wiktionary XML dump.   I substituted another open source Java  API for WordNet- JAWS, whose data model was closer to what I need for Texai.   After two plus years of dialog system development, I have a better idea of what the object model of the lexicon should be, and I consequently simplified it during the re-population process.

Continue Reading »

The online release of the rudimentary version of the Texai English bootstrap dialog system is essentially ready for limited Beta testing. When I return from vacation at the end of this month, I’ll be able to provide the agile support required for beta testing.

Only a few usability issues are remaining to be fixed before the online release of the Texai bootstrap English dialog system. Most of them are illustrated by the very first workflow item:

hello stephenreed
* Plural word form for any of these nouns ‘or’?
or (ProperNoun)
OR (Noun)
(y) The plural is ‘ors’.
(n) There is no plural.
(s) Skip this item.
(d) What are the definitions of this word?
Or provide the plural in quotes.
n

You are probably confused by this. My wife and sponsor, BethLynn Maxwell certainly was. Although I plan a help page to explain the dialog format to novice volunteer mentors, it needs to be clear and self-explanatory.

Here is what the workflow item is attempting to communicate.

Continue Reading »

On the AGI-list, this question was asked:

Of the present day used systems, which products do you consider good business cases of applied AI? I’m looking for concrete product examples (not just wide technologies), in either consumer, or business everyday usage, with high profitability, and/or impact factor.

Having read the proceedings and indeed attended several of the annual conferences of the Association for the Advancement of Artificial Intelligence, I easily formed a relevant list of topics to search. Here are the pertinent results from those searches.

• voice recognition products from Dragon
• autonomous industrial robots
• machine vision
• automated scheduling
• constraint satisfaction from Microsoft
• route planning
• symbolic computation
• machine translation
• automatic landing systems
• recommendation systems
• logistics optimization
• stock trading
• pattern recognition
• spam filters

So where is AI currently used in a business context? Obvious answer: many places.

On the AGI-list, Richard Loosemore pointed to the New Scientist article Disorderly genius: How chaos drives the brain. Richard uses one point of the article to advance his theme:

My arguments have always been top-down (complexity has to be there
because the overall features of the system don’t seem to be
implementable without it), so the way I interpret these low-level
results is that the brain has evolved to have all that self organized
criticality down at the bottom BECAUSE that is the easiest way to sort
through all of the different designs, to find ones that give optimum
high level behavior.

But to me, its content confirms an approach that I’ve taken with Texai to tackle AGI complexity.

Continue Reading »

On the AGI-list, Eric Burton said:

What has OpenCyc accomplished that qualifies as step to AGI? Nothing.
The consensus on this list is that OpenCyc should be thrown out: it is
a distraction

During my employment at Cycorp, John DeOliveira and I lobbied successfully for an open edition of the Cyc knowledge base. John and I are still listed as OpenCyc administrators at SourceForge and John not only authored the OpenCyc web site, but subsequently founded the independent Cyc Foundation after leaving Cycorp. Because I was an OpenCyc developer, because I am no longer working for Cycorp, and because I am taking a somewhat different approach with Texai, I believe that I’m well qualified to describe OpenCyc, and argue that its accomplishments are steps toward AGI.

Continue Reading »

Texai needs to keep separate track of what it’s volunteer mentors have taught it, because those facts may be in conflict. I use context to maintain each person’s belief state. There are default contexts that represent the beliefs of Texai. For example, when Texai is taught the plural word form of some English noun, the corresponding new morphological rule object is persisted into a unique context that represents that mentor’s beliefs. Subsequently during parsing an example utterance, morphological rules are selected from the mentor’s belief context first. Only if no applicable rules are found, is the default context searched for applicable rules.  When a sufficient number of mentors agree on the correctness of a particular morphological rule, then that rule is promoted from the mentor’s belief contexts to Texai’s default context

Continue Reading »

Alan Turing was born on June 23, 1912. In 1950 he published his paper Computing Machinery and intelligence in which he proposed his famous test to answer the question “Can machines think?”. As a plausible mechanism for developing a computer capable of thinking he recommended:

Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain.

Turing then elaborated:

We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with? Even this is a difficult decision. Many people think that a very abstract activity, like the playing of chess, would be best. It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English.

The latter is the approach of the Texai project. Lexical knowledge and skills will be acquired so that the system can bootstrap itself into ever increasing capabilities, as taught to it by volunteer mentors. Turing’s requirement for sophisticated sense organs is reduced by (1) a focus on English text rather than English speech, and by (2) the hypothesis that the development of sub-symbolic processing can be postponed and that sufficiently intelligent behavior can be achieved almost entirely by symbolic processing plus spreading activation, which is much simpler and needs dramatically less computing power.

Celebrating this notable date, I expect to release before midnight a online, rudimentary version of the Texai bootstrap dialog, that will enable volunteers to register and to teach Texai plural noun forms.

[UPDATE] I’m postponing the release for a few more days.  Today’s testing revealed that storing each volunteer mentor’s contributions in a separate belief context required small changes throughout the existing code.