AGI in Japan, 2016

While this year's AGI-related topics in Japan seem fewer than previous years (see my articles for 2015 and 2014), new players such as AIX Research Center at the University of Electro-Communications and Araya Brain Imaging came into the scene.  A public fund was given to a five-year project Comparison and Fusion of Artificial Intelligence and Brain Science.
SIG-AGI, a special interest group of the Japanese Society for AI (JSAI), held sessions at the JSAI 2016 Convention, three workshops, and an event at the 30th Anniversary of JSAI (index in Japanese).
The Whole Brain Architecture Initiative (WBAI)(NPO) held a symposium, five seminars for the general public, the second annual hackathon, and a session at ICONIP.

Below, I write the development of WBAI's activities as an insider.
WBAI has a specific approach for AGI using brain-like connectomic architecture and off-the-shelf machine learning algorithms.  Last year, it was seeking collaborators on this approach world-wide, as it could not find enough researchers domestically.  Besides, it was also in need of software infrastructure such as basic cognitive architecture and learning environments.  In April, Dwango AI Lab., an affiliated private research laboratory, held a hackathon on a new learning environment (LIS or Life in Silico) for intelligent agents.  It was a great success with around 150 participants.  At the time, the situation around A(G)I was rapidly changing overseas.  One thing was the advent of OpenAI, an NPO aiming to counter AGI development by commercial superpowers such as Google.  In 2015, people in WBAI and SIG-AGI were talking with people in the AGI community overseas about establishing a similar entity.  The endeavor was supposedly quenched by the inauguration of OpenAI.  Another thing happening overseas was impressive advances of DeepMind.  While WBAI had a rather long-term plan for promoting researches with the WBA approach, there was a concern that DeepMind would realize AGI in a nearer future in a somewhat WBA way (some details in my memo "A Scenario in which DeepMind will realize AGI by 2018").  So WBAI veered.
With the success at the hackathon in April, WBAI set eyes on the potential of 'hobbyist' engineers/researchers and held another hackathon on Deep Predictive Coding Networks (originally proposed by David Cox's group) in July and created a new development community called SIG-WBA (the course of events is documented here).  WBAI is now more focused on popularizing evolving A(G)I technologies.  Problems?  Recently, research institutes such as Google, OpenAI, and GoodAI have released learning environments for intelligent agents.  That's a good thing (also for WBAI), but it requires human resources to check them out.  More importantly, WBAI is yet to develop basic connectomic architecture to be the core of their approach to attract researchers and engineers anywhere.  (The official version of WBAI's policy can be found here, which, of course, continues to evolve.  To keep you updated, follow WBAI on Twitter ...)

Toward Simple Language (Verb) Learning

I kind-of finished with the simulation environment (the previous post).
The LU (caretaker) now 'instructs' the LL (language learner) with commands such as 'stay,' 'turn,' 'come,' and 'go to the blue item' (in Interlingua).

"Luca, turn right."

When LL 'follows' the instruction, LU smiles and says "Good" to give a reward.

"Good!"

LU also 'declares' (thinks aloud) its own actions before carrying them out.

"I go to the blue one."

---

Now I guess I should go for language learning.
Here is a tentative plan:
(For the time being, I'll forget about LU's declaration mode, which requires the recognition of LU's behavior on LL's part.)

1. The association of LU's words with LL's own actions
   As this should be done at a conceptual level, it requires:
• Generation of action concepts
• Generation of word concepts
LL may carry out three kinds of learning:
• Reinforcement learning of action under instruction
• Association between word concepts and action concepts
• Creating a language (syntactic) model linked with the concepts
2. Bubbling
   LL will utter based on the language model.
   • If the utterance matches its action, LU repeats it and gives a reward.
     This reinforces the bubbling.
   • If not, LU describes LL's action after saying "No."
     This alters the conceptual association and language model.

New architecture

I altered the agent architecture.  The previous one used competing production-like rules for control.  The current one uses simpler perceive-reward-action cycles.  In case conflicts arise, they will be resolved by the action selector.  This would be more compatible with regular reinforcement learning architecture.  Besides the abolishment of competing rules, the rest is the same as the old design.
BTW, two agents are now split to the Language Learner and the Language User (caregiver).  LL chooses its action randomly and LU calls LL when LL is not looking back to LU.  LU's call is displayed as a small rectangular balloon on it, containing LL's current name "Luca."  Agents also 'smile' when they are 'looking into each other.'  These are part of the plan in my verb learning experiments.

The simulator was made with V-Rep and python.
The code is found on GitHub (Version 2 snapshot: 2016-08-01).

Carrying items

I made the agents to carry items to a given direction by pushing them.
This is a trick to be carried out in verb learning experiments.
I thought I'd use reinforcement learning to make them do the trick and tried to use PyBrain, but failed to use it without learning the cause.  I got back to the old-fashioned programming and fixed the trick while keenly feeling my lack of sense in geometry.  The agent first goes to the opposite side around the item against the carrying direction, start pushing, and adjusts the direction when the item veers.


The simulator was made with V-Rep and python.
The code is found on GitHub (Version 1 snapshot: 2016-06-13).