Cognitive Systems, ICANN panel, Q1

1
Cognitive Systems, ICANN panel, Q1

• What is machine intelligence, as beyond pattern
  matching, classification and prediction.

Low level cognitive functions perception,
sensorimotor actions, are basically active signal
analysis (control used to get better signal)
active pattern matching (anticipation, attention,
information filtering) to recognize objects and
structures. Higher-level cognitive functions
associative and episodic memory for natural
perception, representation of complex knowledge
structures, sequential logical and intuitive
reasoning processes, problems solving, planning
and other things symbolic AI works on ... In
between? Reinforcement learning, emotions?
Intuitive computing, solving compositionality
problems search constrained by separable neural
networks.
2
Cognitive Systems, ICANN panel, Q2

• How can such machine intelligence best be
  employed?
• There are already numerous educational
  industrial applications, more are coming in home
  and office automation, cars (vision and object
  recognition, planning routes) etc. Driving in
  urban environment requires some pre-symbolic
  reasoning.
• We need a detailed roadmap with progressively
  more difficult tasks
• what has been already done and may be integrated
  in other models to avoid duplication of work
  (although sometimes it is useful), may be used in
  applications improved sound/object
  localization, orientation mechanisms, control,
  recognitions of speech, gestures, lip movements,
  face recognition, person identification, etc
• what is doable in relatively short time some
  emotions, object recognition, attention control
• what is difficult neural approach to higher
  mental functions?

3
Cognitive Systems, ICANN panel, Q3/4

• How is intelligence actually achieved in the
  human brain (for example as related to recent
  researches on the capacity and power of human
  working memory)?
• Depending on the level.
• Perception, motor control good models of some
  functions.
• Higher cognitive functions - no one really knows?
• How is reasoning achieved without language?
• General idea at the base level, spreading
  activation networks, particular configuration of
  activation distributions represents the object at
  microlevel
• different hierarchical levels of search,
  left/right hemisphere interactions interesting
  experimental data from paired word associations
  and solving problems requiring insight.
• General principle learning new by re-using old.

4
Cognitive Systems, ICANN panel, Q5/6

• What are general simple architectures that
  support reasoning?
• Classical symbolic SOAR, ACT-R, have large
  number of applications, although they are very
  rough approximations to brain processes.
• Interesting connectionist architectures IDA
  (Franklin), Shruti (Shastri) and many others.
• Comparison of some architectures in real-time
  robot control applications would be useful.
• How can we implement primitive levels of
  reasoning as are observed in crows and
  chimpanzees?
• Animal reasoning is pre-symbolic, so first
  sensorimotor exploration is needed, involving
  object and motion recognition solving simple
  manipulation problems.

5
Cognitive Systems, ICANN panel, Q7/8

• Does language play an essential role in the
  reasoning process (sometimes hidden)?
• Representation of real objects and sensomotoric
  sequences in terms of activations has large
  variability, adding symbolic labels reduces
  variability in the part of activation space. This
  must influence the reasoning process.
• How can we build a truly creative architecture to
  solve difficult tasks?
• Ive proposed (WCCI06) to focus first on
  creation of new words, starting from description
  of products, organizations etc, simulating the
  process, as our simulations find some interesting
  words and about 2/3 words that have already been
  invented.
• This can be extended to higher-level mechanisms,
  as in Mazursky, Goldberg and Solomon work on
  ideas for advertisement.

6
Cognitive Systems, ICANN panel, Q9/10

• How would a reasoning system at non-linguistic
  level help in any branch of industry?
• One example is in understanding complex
  machinery reactions, as in the refineries or
  other plants this is relatively simple and may
  be achieved using correlation machines.
• Car driving in urban environments will need some
  reasoning.
• What are the ethical problems thrown up by future
  advances in this area, advancing as it does
  towards the 'soul' of humanity?
• People are very resistant to science and will
  harbor their ideas about souls and spirits
  independent of the development ...
• Problems may arise in distant future when more
  and more jobs will be automated.
• Conscious machines will open a Pandoras box ...

7
DREAM modules
DREAM project is focused on perception (visual,
auditory, text inputs), cognitive functions
(reasoning based on perceptions), natural
language communication in well defined contexts,
real time control of the simulated/physical head.