1) The document discusses Araya Inc.'s research into understanding and creating consciousness by developing generative agents capable of information generation. 2) It proposes that consciousness arises from an agent's ability to generate counterfactual predictions about future and past states, detached from the present environment. 3) Various approaches are described for creating generative agents, including recurrent autoencoders and generative control of agents using latent space representations to select actions. The ultimate goal is to directly connect artificial and biological systems to investigate whether qualia can emerge in artificial systems.

1. Creating Consciousness
Ryota Kanai
(Araya, Inc.)

2. Araya, Inc.
Our Mission
We want to understand consciousness by creating
it.
Our Research
• Integrated Information Theory (Oizumi, Kitazono)
• Intrinsic Motivation (Biehl, Magrans)
• Free Energy/ VAEs (Yu, Tamai)
• Coarse Graining (Chang)
• Meta-learning (Guttenberg)
• Wasserstein distance (Amari, Oizumi, Mizutani)

3. Research Themes
• Integrated Information
Theory (Oizumi, Kitazono)
• Intrinsic Motivation (Biehl,
Magrans)
• Free Energy/ VAEs (Yu,
Tamai)
• Coarse Graining (Chang)
• Meta-learning (Guttenberg)
• Wasserstein distance
(Amari, Oizumi, Mizutani)
We want to understand consciousness by creating
it.
Our Mission

4. YHouse:
yhousenyc.org

5. AI and Society:
aiandsociety.org

6. YHouse:
conresnet.org

7. • Consciousness is relevant for all kinds of cognition.
– Learning and Memory, Perception, Thoughts, Action, Decision
Making, Emotion…
• We don’t understand understanding
– What does it mean to understand?
– What does it mean to feel something?
– What is consciousness for?
Why should we care about consciousness?

8. 1. Spontaneous behavior (e.g. Intention, Curiosity)
2. Generalization (e.g. One-shot learning, Creativity, Thought)
3. Explainability (e.g., Metacognition, Language)
Current issues in AI
Current challenges in AI
A huge gap from current AI to AGI
We need to understand consciousness for AGI

9. Four kinds of machine
consciousness
• MC1. Machines with the external behaviour associated
with consciousness.
• MC2. Machines with the cognitive characteristics
associated with consciousness.
• MC3. Machines with an architecture that is claimed to be a
cause or correlate of human consciousness.
• MC4. Phenomenally conscious machines.
Gamez (2008)

10. • How can we prove that it has phenomenal
experience?
Proving Consciousness
The dual problem of machine consciousness
Creating Consciousness
• How can we create a machine that reproduces
causal structures and functions of consciousness?

11. Functions of consciousness
• Depiction. The system has perceptual states that ‘represent’ elements of
the world and their location.
• Imagination. The system can recall parts of the world or create
sensations that are like parts of the world.
• Attention. The system is capable of selecting which parts of the world to
depict or imagine.
• Planning. The system has control over sequences of states to plan
actions.
• Emotion. The system has affective states that evaluate planned actions
and determine the ensuing action.
E.g. Aleksander (2005)
Example: Aleksander’s Axioms

12. Consciousness for bridging a temporal gap
Clark & Squire (1998)
• Delay conditioning occurs without awareness
• Trace conditioning requires awareness (and hippocampus)

13. Online and memory guided action
Perceptual judgement
Visually guided action
Milner et al. (1991)
Memory guided action depends on perceptual system

14. V1 hypothesis
• V1 is not conscious because it does not project to
prefrontal cortex.
• The rationale from biological usefulness of awareness
– A: To produce the best current interpretation of the visual scene
– B: To make the information available for the system that plan
and execute voluntary motor outputs.
Crick & Koch (1995)

15. A New Hypothesis on
Consciousness
A Counterfactual Information Generation Hypothesis
Agents with the ability to generate predictions, including
counterfactual future and past, of their own state in the
environment have consciousness.

16. Implementing
counterfactuals

17. Active inference
Friston et al. (2010) Biol Cybern
The motivation: Biological agents need to minimise the entropy of their
sensory states to resist a tendency to disorder.
(Ergodic Assumption)
(Entropy of sensory states)
This implies that agents should minimise surprise over time.
(Average over space = Average over time)

18. Active inference
c.f. series of work by Friston
Generative model
Active inference
Perceptual Inference

19. Active inference with counterfactuals
c.f. series of work by Friston
Instead of just minimising the entropy of sensory states, more recent formulations
suggest minimising the joint entropy of sensory and hidden states.
We can decompose this into:
(Entropy of Sensory States) + (Conditional Entropy of Hidden States)

20. Counterfactual predictions
Next eye positions are chosen to
minimise uncertainty of hidden
states

21. Counterfactual predictions in a robot
Bongard , Zykov & Lipson (2006)

22. • Intention
– Counterfactual predictions of the consequences of future actions.
• Non-Reflexive Behaviour
– Mental simulation detached from the present external stimuli.
• Perceptual Presence (Seth 2014)
– Coherent sensory motor contingency.
Summary
A model of the world, and sensory motor contingency are
prerequisites for the ability to simulate the world internally.
Function of Consciousness = The ability to represent
events disconnected from the present environment.

23. What does it mean to generate information?
input
encoding decoding
output
VAE: µ x σ  z
Info Processing Info Generation
For individual layers, the analogy represented here is not obvious.
Hidden: z

24. Link to predictive coding
Prediction error
Info. Processing
Prediction
Info. Generation
input
Hidden: z
ConsciousUnconscious
DecodingEncoding
GenerationProcessing
DecompressionCompression
PredictionPrediction Error
FeedbackFeedforward
Feedback generates information, hence consciousness.

25. Feedback and visual awareness
Super et al. (2001)

26. Super et al. (2001)
Late activity (feedback) is associated with subjective visibility.
Feedback and visual awareness

27. Optogenetics evidence
Manita et al. (2015) Neuron

28. Creating an agent with
information generation capability

29. Creating generative agents

30. Generative control:
 'What is the distribution of possible futures?'
Distribution includes actions → search for actions that lead to desirable
futures
 Can change reward function on the fly
 Reward function can easily depend on things like uncertainties and
surprisals
 Free long-term planning/coherency
Latent
Space
Future
Sensory
State
Future
Action
Pattern
Latent
Space
Latent
Space
Latent
Space
Latent
Variable
Future
Sensory
State
Future
Action
Pattern
Future
Sensory
State
Future
Action
Pattern
Future
Sensory
State
Future
Action
Pattern
Future
Sensory
State
Future
Action
Pattern
Generative Model
Generative control

31. Repeated re-encoding allows an auto-encoder to better match the data distribution
(Arulkumaran, Creswell, Bharath 2016)
Recurrent Autoencoder

32. Generative Control of Cartpole
Learn action+sensor generative model, maps latent variable → prediction
for next 16 frames. Gradient descent in latent space to pick action

33. Ultimate method: Direct connection
• Qualia seem to be shared among the brains through direct
connections.
• We can connect brains directly to AI to check the qualia in
AI.
The Hogan sisters seem to share qualia
via direct connections in the thalamus.

34. Final thoughts
• Information generation, not processing
– We should pay more attention to the generative aspect of the brain.
• Qualia are not counterfactual
– Perception of the present is also generated.
– Corresponding bottom-up signals may help generation of more detailed, clear
contents.
• Intrinsic definition of information generation
– IIT-like definition?
• Phenomenal consciousness
– If we create the causal structure of the third-person aspect of consciousness,
the first-person aspect of consciousness should be generated (Biological
Naturalism).

35. Thank you
Ryota Kanai
kanair@araya.org
@kanair