In Science-Fiction there is the concept of SI vs. RI, Sentient Intelligence and Restricted Intelligence. My guts feeling tells me we are close to build an SI via neural networks, it seems there are just some self-reflecting layers missing, but there are several reasons against building an SI, ethical ones as described by Prof. Thomas Metzinger, and agent-motivational ones as described by Prof. Nick Bostrom. This project, Tummi, is pretty much about building an RI, Restricted Intelligence, what we also used to call an expert-system.
In the previous post I stated there is no way around to build an artificial Ego to be able to draw new conclusions. Of course there is, but I doubt such a machine will be build in my lifetime.
The game of chess has about 10^50 possible positions and about 10^120 possible games. To compute the whole game tree to find the perfect play was, is, and probable will always be not computeable on classic computers. We have the Quantum-Computers in pipe, and it is yet unknown if such a perfect-play engine is possible on such a machine. Current computer chess engines on Von-Neumann computers use heuristics to find the best move via an in depth limited tree search, and they do this meanwhile on a super-human level.
So, we can view our process of thinking similar to engines playing chess, we use our mind to search the memeplex knowledge graph for answers and solutions, we search the known graph we call knowledge, and we search the unknown what we call intuition, creativity and alike.
So, yes, in theory we can build a machine, maybe an Hyper-Meme-Machine, which expands the whole possible knowledge graph at once and runs some kind of evalution on possible candidates of new conclusions. All without the need of an artificial Ego.
The question which remains open is if such an SKGS, speculative knowledge graph search, can be implemented in practicable manner on our classic computers nowadays or near future.
My intention with this blog was not to get into philosophy, but to document this project, Tummi. The further I walk this path, the further I realize that it is not about building just an oracle kind of machine, an question-answering-system, but to go deeper, to build an system which is able to think, reflect its thinking, and come up with new conclusions. Therefore we have to build some kind of Ego, "Cogito, ergo sum", there is no way around this, and that is the point where philosophy steps in. The human kind does not consist only of the mind, the Ego, I like to see it threefolded, it is the body, mind and soul which makes us as a whole. So, what do we create if we build an artificial Ego, able to think and reflect its thinking, but we are not able to map the other parts? Should we do this? Should we build such an limited Ego, knowing that there is more? I am for sure not the only one who ponders on this, so let me refer to Prof. Dr. Thomas Metzinger:
7. Warum wir es nicht tun sollten in German
7. Why we shouldn't do it on Google Translate
When I take a look at my list of Meme Machines we can classify these into three strands...
1. GOFAI - Good Old Fashioned AI
These are based on some kind of predicate logic and use languages like Prolog or LISP. START by MIT is one example.
2. Pattern Matching
One of its prominent examples are engines based on AIML, Artificial Intelligence Markup Language, like A.L.I.C.E. Up to now these AIML based chatbots achieved the best results in the Loebner Price competition.
3. Neural Networks
I guess it really took off with Google's BERT, the introduction of Transformers, in 2018, and now the race is up to create models with more layers and parameters to achieve better results in text comprehension, question answering (SQuAD) and summarization.
Here an overview of other meme machines...
*** updated on 2021-01-14 ***
2021 - Roadmap update for Epsilon I, II, III, IV
2020 - Roadmap for Tummi, Tummii and Tumiii.
2019 - Tummi v0001 pdf flowchart published.
2019 - Blog online.
2018 - Blueprint of an interlingual meme machine based on knowledge graphs
bootstrapped with human expert knowledge but able to parse content
2018 - Project reopened, Watson didn't make it.
2011 - Project canceled, IBM's Watson wins in Jeopardy.
2010 - First prototype with an simple ontology as knowledge graph.
2008 - Convinced that RDF/SPARQL offer enough flexibility for an meme machine.
2008 - Experiments with neural networks and RDF/SPARQL.
2005 - Experiments with AIML.
2004 - Inspired by Kiwi Logic's virtual agents.
2003 - Convinced that an meme machine could answer IT HelpDesk emails.
2001 - Experiments with OOP and meme replication.
2001 - Journey starts, inspired by 'The Meme Machine' by Susan Blackmore who
introduces the idea of artificial meme machines.
*** updated on 2021-12-20 ***
This blog is about Tummi, my attempt to create an artificial meme machine that is able to parse content in natural language and answer questions in natural language.
The last time I started such a hobby project it took me about 10 years to get into the techniques and understand the underlying principles. So maybe anno 2028 I will be able to judge if this blog was a foolish idea or not.
The name Tummi is derived from 'The Meme Machine' by Susan Blackmore and 'The Ultimate Machine' by Claude Shannon.