Building Gaia
After almost 6 months of silence in this blog (due to some other activities) I felt the need to share the actual state of my ideas on the
Artificial intelligence that learns from associated parallel streams of information
Last sunday (June 29th 2014) I started building Gaia. That's the working name I use for this AI.
As I'm a man, I cannot give birth to a living creature called human, but as a transformer of coffee and pizza into bits and bytes I can give birth to something else.
In my tag line in Google+ "Desire a self driving car and other smart companions (male, female and undefined)" one of the "undefined" will be this AI.
I don't like to refer to this AI with the dispassionate "it" so I'll use the term "she" when referring to Gaia.
Inspired by the "World of null-A" by A. E. van Vogt and to be clear that I very well know that this program is not a person I'll use the glyph she (being the non-human she).
Building philosophy and architecture
The most important axiom for building her is that I want to give her as less as possible "hard-wired" instructions on how to do something.I decided to use a "toolbox" like paradigm.
Typical elements in her toolbox will be:
- goals
- algorithms
- kinds of memory
- ...
Each of these tools can have discoverable parameters for which she can change the values. A tool has also a set of evaluation algorithms with their own parameters. She can choose and the evaluation algorithm (if there is more then one) and the evaluation parameters. Together they form the evaluation criteria.
She is free to choose whatever combination of goals, algorithms (with their parameters) etcetera she is going to use and evaluate the effectiveness (related to a goal) of it.
The outcome is stored in a special memory (hard coded for this one): the experience center.
 |
| Version 0.1 of Gaia's architecture. The components with a red border have been build on 2014-06-30. |
The effectiveness of a tool can vary over time. For example a tool might not be useful in her early stage of development but only later on.
Sensor channels
A sensor channel is composed of one or more Sensor Output Consumers. The channel allows the synchronization of the data streams.A video camera e.g. would be connected to one channel with 2 or 3 consumers: 1 for the video and 1 (mono) or 2 (stereo) for the sound.
A YouTube video might have an extra consumer for the subtitles. Even one for each language when in the future it will be possible to have the subtitles in different languages at the same time (blink, hint, blink to those who are concerned).
A sensor reading digital texts will have only 1 consumer in the associated channel.
The Sensor Output Consumers are initially all identical. None is specific for a particular kind of data.
She has to "train" them to make sense of the data stream. Using the mechanism described above.
To improve the overall effectiveness it is recommended (but not necessary) to keep the data flow between a sensor and an associated consumer homogenous (e.g. only streaming video or only streaming sound).
The reason why I opted for this solution is that I can define what kinds of data streams can be made available now. But I don't know what will be available tomorrow. A "smell" stream perhaps?
The Sensor Output Consumer tool should be flexible enough to cope with new kinds of data streams.
The data consumed
To avoid what is called "over fitting" in traditional AI, Gaia will see a particular stream of data only once. Depending on the tool used she will retain only a very small fraction of the first streams but this fraction should increase more or less rapidly afterwards.What has been done so far
To facilitate the architecture implementation to mature over time, (yes, this first implementation will not be the last), I started with an easy data stream: text.The data sources I use are free eBooks from the Gutenberg project. And my compliments for all the volunteers making the books available.
The books are in different languages. For now in English, French, German, Spanish and Latin.
I selected these languages because it allows me to identify what I would like to see what she has done with it and compare that with what she has really done.
And there were surprises.
After the first few testing rounds, what I saw in her memory was far away from what I expected to see.
The books from the Gutenberg project have all, at the end, a license section. These sections are almost identical for all books. She considered these as more important then the rest and it overshadowed everything else.
Note: the term considered should not be taken literally. She is far from being able to consider whatever.
This is typically a case of over fitting.
To cope with this I choose to keep only the text of the books and strip of the headers and tails.
Her implicit goal for now could be described as:
Fill my memory with frequent patterns of bytes
The Sensor Output Consumer tool she is using has 6 configurable numeric parameters and 1 boolean for 1 algorithm.
For the evaluation she has 3 extra parameters and 4 algorithms to choose from.
The numeric parameters are not constraint in any way. Providing a negative value for something that should be used as a counter will terminate the experience immediately for instance.
And the evaluation for each of the evaluation algorithms will be bad (with regards to the goal).
Fiddling around with her options on a 40 M data stream for less then a minute produces the JSON memory dump add the end of this post.
The existing "Pattern splitting" tool has not been used yet.
This tool splits patterns in memory into 2 other patterns if each of these exist. The original pattern will be removed and the 2 resulting patterns will have their count (firing rate) increased.
Doing so with isolated patterns will imply the lost of precious information. Because the patterns are sequentially related (sequence in time or space as you like) and this information will now be lost.
And I'm not yet fixed if I should make the evaluation criteria for this stage of her development. The availability of evaluation criteria allows her to use the tool.
Avoiding misunderstanding
In this post, as it will be the case in the next ones, I describe Gaia, the new AI, as if it were a person.This is a personal choice, that helps me thinking about what and how the AI should do things.
Not thinking about her as a bunch of algorithms, which in reality it is of course.
To be explicit:
Gaia doesn't have any perception yet on what she is handling. She only tries to find frequent patterns in a stream.
Even her perception of the term frequent is reduced to a function result (the count() function).
Her actual data memory is her infant memory. There are only isolated patterns without any associations / links between them.
I will soon add a new kind of memory that allows patterns to be connected in various ways.
Roadmap
There is a multitude of next steps on Gaia's development agenda. To mention a few:- provide video streams
- provide sound streams
- create her next generation memory
- develop other algorithms
- improve existing algorithms
- improve and extend the architecture framework
- make the code open source
- ....
What and when things will be done depends on a lot of factors, for which some of them are unfortunately out of my sphere of influence.
If you're interested stay tuned. I'll keep you informed in this blog about the progress on Gaia.
In the mean time relax and think about the implications (benefits and risks).
Financial support
If you like to support the development of this new kind of AI you can donate Bitcoins (or fragments of it) at
1HadqhKD5EdCDpTorgxP9P8akKqycEUkwk
When Gaia will be more mature and she will be able to interact with the environment she might also need to spent money. She is not there yet but Bitcoin donations for her can already been done at:
1PyyRd6k5kSuuErAnJNpvdoNCW5ZPgD9fb
I'm excited about this piece of software.
And you?
Ronald Poell
2014-07-02
First chapter Previous chapter
Edit 2014-07-02: corrected a forgotten she . When the glyph cannot be written use null-she.
Below you'll find her memory dump after running from scratch (empty memory and no previous experiences) on a 40 M stream of text.
In some places you will see the well known � character.
As she doesn't know what she is looking at (in this case a UTF-8 stream) she doesn't know yet about code points in UTF-8. And when a code point is not included in a multi byte character (the UTF-8 character is not well formed) the parser in e.g. a web browser will display the � character.
It will be interesting to see how long it will take her to get rid of them.
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