Tutorial at Artificial General Intelligence at Melbourne: 3rd part on Integrated information theory of consciousness

1. Tutorial 2: AGI and Consciousness
Integrated information theory
(IIT) of consciousness
Aug 15, 2017 @ibis, AGI from 13:30-15:30
Nao Tsuchiya, Monash University
Ryota Kanai, Araya Co., Japan

2. IIT’s features that are potentially interesting to AGI
1. IIT is a theory for consciousness (Tononi 2004, 2008, 2012, Oizumi et al 2014, 2016 PLoS
Comp, 2016 PNAS)
a. IIT seeks what ‘physical’ substrates can support phenomenological consciousness
i. AI can be conscious
b. IIT provides explanation of potential reasons why some biological organisms have evolved
brains to generate higher level consciousness
i. Implies utility - robustness through degeneracy, many functions/information with limited #
of units, possibility to continuous learning and stability
2. IIT sees “outputs” highly limited consequences of inner consciousness of the
system
a. A very different functionalist viewpoint, where input-output is treated as the goal
b. No output, no dynamic system can be conscious
c. Consciousness can be completely decoupled from environment
i. Dreams (hallucination, imagery) and brain stimulation

3. Quick outline of IIT’s construction (Axiomatic
approach) (Oizumi et al 2014 PLoS Comp, Tononi et al 2016 Nat Rev Neuro)
1. IIT identifies 5 phenomenological axioms (or desired/essential) properties of
consciousness
Existence, informativeness (=causal power), integration, composition,
exclusion
2. IIT tries to translate the properties into mathematical formulation
2. To the extent that above formulation can explain various facts that we
ourselves know between brain - consciousness relationship, we will need to
believe what IIT would predict about unimaginable cases (Tsuchiya 2017
Philosophy Compass)
- Including Artificial Consciousness

11. Structure of integrated information = conscious
experience (Haun … Tsuchiya 2017 eNeuro, in press)
N units can specify up to [a power set of N] distinct integrated information
- Compositionality
- Advantages: necessarily much bigger, richer space than output behaviors
- Simultaneous determination (integration) of both positive and massively
negative concepts

12. Structure of integrated information = conscious
experience (Haun … Tsuchiya 2017 eNeuro, in press)
N units can specify up to [a power set of N] distinct integrated information
- Compositionality
- Advantages: necessarily much bigger, richer space than output behaviors
- Simultaneous determination (integration) of both positive and massively
negative concepts

15. Information processing Intrinsic information
structure

16. IIT’s features that are potentially interesting to AGI
1. IIT is a theory for consciousness (Tononi 2004, 2008, 2012)
a. IIT seeks what ‘physical’ substrates can support phenomenological consciousness
i. AI can be conscious - Recurrency/Feedback are necessary
b. IIT provides explanation of potential reasons why some biological organisms have evolved
brains to generate higher level consciousness
i. Implies utility - robustness through degeneracy, many functions/information with
limited # of units, possibility to continuous learning and stability

18. Evolutionary origins/advantages of
high-integrated information systems
Albantakis et al 2014 PLoS Comp

19. Albantakis et al 2014 PLoS Comp

20. Albantakis et al 2014 PLoS Comp

21. Albantakis et al 2014 PLoS Comp

22. IIT’s features that are potentially interesting to AGI
1. IIT is a theory for consciousness (Tononi 2004, 2008, 2012, Oizumi et al 2014, 2016 PLoS
Comp, 2016 PNAS)
a. IIT seeks what ‘physical’ substrates can support phenomenological consciousness
i. AI can be conscious
b. IIT provides explanation of potential reasons why some biological organisms have evolved
brains to generate higher level consciousness
i. Implies utility - robustness through degeneracy, many functions/information with limited #
of units, possibility to continuous learning and stability
2. IIT sees “outputs” highly limited consequences of inner consciousness of the
system
a. A very different functionalist viewpoint, where input-output is treated as the goal
b. No output, no dynamic system can be conscious
c. Consciousness can be completely decoupled from environment
i. Dreams (hallucination, imagery) and brain stimulation

23. Future directions
IIT:
Need accurate, but faster algorithms
Neuroscience:
Validation of IIT (Haun et al 2017 eLife, ….)
Testing prediction on ourselves - cyborg, implantation, etc
Physics:
Theory of causation (Hoel et al 2013 PNAS, 2016 Neuroscience of
consciousness), optimal spatio/temporal scale
AI (animats)
Learning + evolution
AGI and Artificial Consciousness --- and discussion on what happens after?