From Machine Cognition to Conscious Machines

1. Exystence Workshop Machine Consciousness: Complexity Aspects From Machine Cognition to Conscious Machines Dr. Pentti O A Haikonen, Principal Scientist, Cognitive Technology Nokia Research Center

2. The Outline of the Story Why machine cognition? About intelligence, understanding and cognition A model for cognitive and thinking machines Machine cognition and consciousness; -one sided coins or egg and chicken?

3. Towards More Sophisticated Information Technology Products Many of today’s products are so complicated that the user cannot possibly control all the involved processes, the product itself must take care of these and leave only higher level control and decisions to the user. This is achieved by preprogrammed rules and algorithms that are sometimes called embedded intelligence. It is seen that blind algorithms are only a limited solution, more complex context awareness, machine understanding, is required.

4. Levels of Machines; One more Step to Go The machine as a tool: -The user can execute an action with the machine The machine as an automaton: -The user (or condition) can initiate action sequences The machine as an agent: -The user can request a context dependent function The machine as an autonomous agent: -The machine executes context dependent actions as is deemed necessary by (heuristic) set of rules The machine as a cognitive agent: -The machineunderstands and is aware;is able to execute tasks requiring real intelligence and thought

5. Machine Understanding of Meaning and Context Will Enable New Possibilities Speech and language; recognition, understanding and translation Vision; visual episode understanding, prediction Unified sensory information understanding Non-indexed data base search and information compilation Improved Artificial Intelligence, artificial creativity Personal artificial assistants and companions Autonomous robots; sterile nurses that do not fall ill, rescue robots, etc. Security, defense and law enforcement Etc.

6. What We Want: Cognitive Information Technology or Human-like Information Processing From speech recognition to speech understanding From pattern recognition to scene understanding From text parsing to story understanding From statistical "learning" to cognitive learning From numerical simulation to free imagination

7. Traditional Models and Tools GOFAI - The brain is a computer; human-like intelligence and cognition via programs and algorithms? Artificial Neural Networks; human-like intelligence via statistical computing? DSP; Systems with sensors, sensory information represented by numeric values - processing by transforms, filtering, etc. with partly parallel architectures. Semantic Networks; understanding via classification and indexing?

8. Traditional Methods have not Provided True Intelligence and Understanding Humans surpass the computer in everyday tasks because humans are intelligent and are able to understand. But, what is intelligence and understanding?

9. What is Intelligence? Instruction booklet No intelligence is needed if you can use this by following the instructions only

10. RIP GOFAI What is Intelligence Intelligence is what we use when rules do not help.This excludes the possibility of rule-based AI

11. Understanding is not that Simple For instance - Episodic Understanding: NOT tape recorder type storage and playback BUT the ABILITY to -Answer questions about the subject; -what is where -what is happening -who is doing what to whom, etc. -Paraphrase; describe in own words -Detect contradictions -Predict what happens next, what is possible -Evaluate significance, is this good or bad -Give reasons for present situation, Etc. .

12. Understanding Necessitates the Grounding of Meaning Real world concepts must be grounded to real world entities. -This calls for a perception process Concepts must be connected to other concepts. -This calls for associative cross-connections The general model of cognition provides these functions and more.

13. The General Model of Cognition

14. The Cognitive Model as an Associative Structure

15. Representation of Information or the Power of Power Sets The idea behind distributed signal representations: The set of all subsets -the power set- is always larger than the original set. Therefore a limited number of original set members -here the basic signals- can give rise to a much larger number of subsets -possible signal combinations- allowing thus the representation of large number of different cases with only limited number of basic signals.

16. Distributed Representations and Associative Processing Signals and signal sets carry meaning Individual signals correspond to elementary features Signal sets or arrays correspond to entities Entities can be associated together by linking the corresponding signal arrays An entity can be evoked by incomplete or slightly different signal array -”the closest guess” Also episodes, signal set sequences, can be handled

17. Distributed Representations and Associative Processing Go Well Together Provided that Combinatorial explosion is avoided by attention; a mechanism that limits the actual connections by relevance and importance, etc. Hence the eventual need for emotional significance. Interference -the false evocation of undesired signals- is controlled, various methods exist.

18. The Outline of a Conscious Machine

19. The General Architecture

20. Characteristic Properties of the Model Each modality works on its own and produces streams of percepts about environment and internal states. Modalities are associatively cross-connected, therefore the activity of one modality may be reflected in the other modalities; percepts may be named and labeled, names may evoke corresponding percepts…the activity of one modality may be memorized and reported in terms of other modalities, etc. Attention determines which percepts are accepted for further action. Attention is controlled by signal intensity and thresholds, these are controlled by e.g. emotional significance. Pain and pleasure are system reactions that affect attention. The flow of inner speech and inner imagery is supported.

21. Consciousness in the Machine? How do we know if the machine is conscious? There may be some telltale symptoms that we could look for. For instance Prof. Aleksander lists five axioms: 1) sense of place, 2) imagination, 3) directed attention, 4) planning, 5) decision/emotion In the following a rather similar list is given perhaps with some twists.

22. Consciousness in the Machine? -Does the machine have mental content that is about something? -Is the machine able to report its mental content to itself (and others) and does it recognize the ownership of the same? -Is the machine able to make the difference between the environment and the machine self? -Does the machine have (episodic) sense of time? -Does the machine bind its present experience to personal history and expected future? (“the flow of existence”) -Is the machine aware of its own existence? -The “hammer test” of phenomenal awareness: Does the machine feel pain?

23. Consciousness in the Machine? And finally, if the machine were to genuinely ask: Where did I come from? Then we would know that we are into something deep.

24. Cognition and Consciousness; Which Comes First? In this model understanding arises from the processing with meaning -on the other hand meaning-carrying signal arrays are intentional, a supposed prerequisite for consciousness. Here also the cross-modality binding and reporting -hallmarks of consciousness- arise from the requirements of cognition. Therefore, are true cognition and consciousness connected or separate properties? Thus, would the proper realization of cognition automatically result in some kind of consciousness (Or do zombies exist)?

25. From Machine Cognition to Conscious Machines Thank You for Your Attention! Dr. Pentti O A Haikonen, Principal Scientist, Cognitive Technology Nokia Research Center

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