In the year 1900 at the International Congress of Mathematicians in Paris David Hilbert delivered what is now considered the most important talk ever given in the history of mathematics, proposing 23 major problems worth working at in future. 100 years later the impact of this talk is still strong: some problems have been solved, new problems have been added, but the direction once set - identify the most important problems and focus on them - is still important.

It became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction.

• Wlodzislaw Duch, What Is Computational Intelligence and Where Is It Going?

• Jurgen Schmidhuber, New Millennium AI and the Convergence of History

• Ron Sun, The Challenges of Building Computational Cognitive• Architectures• James A. Anderson et al.

Programming a Parallel Computer: The Ersatz Brain Project • JG Taylor,

The Human Brain as a Hierarchical Intelligent Control System• Soo-Young Lee, Artificial Brain and OfficeMateTR based on Brain

Information Processing Mechanism• Stan Gielen, Natural Intelligence and Artificial Intelligence:

Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour

• DeLiang Wang, Computational Scene Analysis

• Nikola Kasabov, Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities

• Robert P.W. Duin, Elżbieta Pękalska, The Science of Pattern Recognition. Achievements and Perspectives

• Wlodzislaw Duch, Towards Comprehensive Foundations of Computational Intelligence

• Witold Pedrycz, Knowledge-Based Clustering in Computational Intelligence

• Vera Kurkova, Generalization in Learning from Examples

• Lei Xu, A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective

• Jacek Mańdziuk, Computational Intelligence in Mind Games• Xindi Cai and Donald C. Wunsch II,

Computer Go: A Grand Challenge to AI• Lipo Wang and Haixiang Shi,

Noisy Chaotic Neural Networks for Combinatorial Optimization

Grand challengesGrand challenges

Our discipline is broad, and there many grand challenges for the next 20 years.

• Foundations for CI theory, integrating all methods.• Learning from data in difficult cases• Complex models, structured data, natural perception• Understanding brain/mind relations, neuromorphic models• Natural language processing• Combining CI (perception) with AI (systematic reasoning)• Towards artificial minds

Artificial Minds (AMs), or personoids, are software and robotic agentsthat humans can talk to and relate to in a similar way as they relate to other humans.

Neurocognitive informatics!

Crises of the richness Crises of the richness

Hundreds of components ... transforming, visualizing ...

Yale 3.3: type # components

Data preprocessing 74Experiment operations 35 Learning methods 114Metaoptimization schemes 17Postprocessing 5Performance validation 14Visualization, presentation, plugins ...

Visual “knowledge flow” to link components, or script languages (XML) to define complex experiments.

Are new methods better than what we already have in our treasure box?

How can we be sure?

Meta-learning as search in model spaceMeta-learning as search in model space

Search in a well-defined transformation framework, from the simplest kNN to novel combination of procedures & parameterizations.

k-NN 67.5/76.6%

+d(x,y); Canberra 89.9/90.7 %

+ si=(0,0,1,0,1,1); 71.6/64.4 %

+selection, 67.5/76.6 %

+k opt; 67.5/76.6 %

+d(x,y) + si=(1,0,1,0.6,0.9,1); Canberra 74.6/72.9 %

+d(x,y) + sel. or opt k; Canberra 89.9/90.7 %

k-NN 67.5/76.6%

+d(x,y); Canberra 89.9/90.7 %

+ si=(0,0,1,0,1,1); 71.6/64.4 %

+selection, 67.5/76.6 %

+k opt; 67.5/76.6 %

+d(x,y) + si=(1,0,1,0.6,0.9,1); Canberra 74.6/72.9 %

+d(x,y) + selection; Canberra 89.9/90.7 %

Difficult caseDifficult casess: complex logic: complex logic

For n bits there are 2n nodes; in extreme cases such as parity all neighbors are from the wrong class, so localized networks will fail.Achieving linear separability without special architecture may be impossible.

Redefining goal of learning and defining complexity classes: the concept of k-separability.

DREAM modules DREAM modules

Natural input modules

Cognitive functions

Affectivefunctions

Web/text/databases interface

Behavior control

Control of devices

Talking head

Text to speechNLP

functions

Specializedagents

Natural perception requires many specialized transformations, not genera learning techniques; cognitive functions go beyond pattern recognition, to learning from partial observations and systematic reasoning.

Humanized interfaces:graphics or android heads

Wlodzislaw Duch,Google: Duch NTU

Store

Applications, eg. 20 questions game& other word games,medical systems

Query

Semantic memory for artificial minds: integration of perception and cognition

Parser

POS taggers, phrases, NLP connectionist systems

On line dictionaries,encyclopedias, ontologies, free text sources …Manual cleaning,

collaborative knowledge acquisition

verification

HIT related areasHIT related areas

HIT projectsHIT projects

T-T-S synthesisT-T-S synthesis

Speech recognitionSpeech recognition

Talking headsTalking heads

BehavioralBehavioralmodelsmodels

GraphicsGraphics

Cognitive ArchitecturesCognitive Architectures

Cognitive Cognitive sciencescience

AIAI

A-MindsA-MindsLingu-botsLingu-bots

KnowledgeKnowledgemodelingmodelingInfo-retrievalInfo-retrieval

VR avatarsVR avatars

RoboticsRobotics

Brain modelsBrain models

Affective Affective computingcomputing

EpisodicEpisodicMemoryMemorySemantic Semantic

memorymemory

WorkingWorkingMemoryMemory

LearningLearning

Cognitive SystemsCognitive Systems

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

Low level cognitive functionsLow level cognitive functions: perception, sensorimotor actions, : perception, sensorimotor actions, are basically active signal analysis (control used to get better signal) are basically active signal analysis (control used to get better signal) + active pattern matching (anticipation, attention, information filtering) + active pattern matching (anticipation, attention, information filtering) to recognize objects and structures. to recognize objects and structures.

Higher-level cognitive functionsHigher-level cognitive functions: associative and episodic memory : associative and episodic memory for natural perception, representation of complex knowledge for natural perception, representation of complex knowledge structures, sequential logical and intuitive reasoning processes, structures, sequential logical and intuitive reasoning processes, problems solving, planning and other things symbolic AI works on ... problems solving, planning and other things symbolic AI works on ...

In between? Reinforcement learning, emotions? In between? Reinforcement learning, emotions?

Intuitive computing, solving compositionality problems – search Intuitive computing, solving compositionality problems – search constrained by separable neural networks.constrained by separable neural networks.

Cognitive Systems Cognitive Systems

• How can such machine intelligence best be employed?How can such machine intelligence best be employed?There are already numerous educational + industrial applications, There are already numerous educational + industrial applications, more are coming in home and office automation, cars (vision and more are coming in home and office automation, cars (vision and object recognition, planning routes) etc. Driving in urban object recognition, planning routes) etc. Driving in urban environment requires some pre-symbolic reasoning.environment requires some pre-symbolic reasoning.

We need a detailed roadmap with progressively more difficult tasks:We need a detailed roadmap with progressively more difficult tasks:

• what has been already done and may be integrated in other what has been already done and may be integrated in other models to avoid duplication of work (although sometimes it is models to avoid duplication of work (although sometimes it is useful), may be used in applications & improved; sound/object useful), may be used in applications & improved; sound/object localization, orientation mechanisms, control, recognitions of localization, orientation mechanisms, control, recognitions of speech, gestures, lip movements, face recognition, person speech, gestures, lip movements, face recognition, person identification, etc;identification, etc;

• what is doable in relatively short time – some emotions, object what is doable in relatively short time – some emotions, object recognition, attention control; recognition, attention control;

• what is difficult – neural approach to higher mental functions?what is difficult – neural approach to higher mental functions?

Cognitive SystemsCognitive Systems

• How is intelligence actually achieved in the human brain (for How is intelligence actually achieved in the human brain (for example as related to recent researches on the capacity and power example as related to recent researches on the capacity and power of human working memory)?of human working memory)?

Depending on the level. Depending on the level. Perception, motor control – good models of some functions.Perception, motor control – good models of some functions.Higher cognitive functions - no one really knows?Higher cognitive functions - no one really knows?

• How is reasoning achieved without language?How is reasoning achieved without language?General idea: at the base level, spreading activation networks, General idea: at the base level, spreading activation networks,

particular configuration of activation distributions represents the particular configuration of activation distributions represents the object at microlevel; object at microlevel; different hierarchical levels of search, left/right hemisphere different hierarchical levels of search, left/right hemisphere interactions – interesting experimental data from paired word interactions – interesting experimental data from paired word associations and solving problems requiring insight. associations and solving problems requiring insight.

General principle: learning new by re-using old. General principle: learning new by re-using old.

Cognitive SystemsCognitive Systems

• What are general simple architectures that support reasoning?What are general simple architectures that support reasoning?

Classical symbolic: SOAR, ACT-R, have large number of applications, Classical symbolic: SOAR, ACT-R, have large number of applications, although they are very rough approximations to brain processes.although they are very rough approximations to brain processes.

Interesting connectionist architectures: IDA (Franklin), Shruti (Shastri) Interesting connectionist architectures: IDA (Franklin), Shruti (Shastri) and many others.and many others.

Comparison of some architectures in real-time robot control Comparison of some architectures in real-time robot control applications would be useful. applications would be useful.

• How can we implement primitive levels of reasoning as are How can we implement primitive levels of reasoning as are observed in crows and chimpanzees?observed in crows and chimpanzees?

Animal reasoning is pre-symbolic, so first sensorimotor exploration is Animal reasoning is pre-symbolic, so first sensorimotor exploration is needed, involving object and motion recognition + solving simple needed, involving object and motion recognition + solving simple manipulation problems. manipulation problems.

Cognitive SystemsCognitive Systems

• Does language play an essential role in the reasoning process Does language play an essential role in the reasoning process (sometimes hidden)?(sometimes hidden)?Representation of real objects and sensomotoric sequences in Representation of real objects and sensomotoric sequences in terms of activations has large variability, adding symbolic labels terms of activations has large variability, adding symbolic labels reduces variability in the part of activation space. This must reduces variability in the part of activation space. This must influence the reasoning process. influence the reasoning process.

• How can we build a truly creative architecture to solve difficult How can we build a truly creative architecture to solve difficult tasks?tasks?I’ve proposed (WCCI’06) to focus first on creation of new words, I’ve proposed (WCCI’06) to focus first on creation of new words, starting from description of products, organizations etc, simulating starting from description of products, organizations etc, simulating the process, as our simulations find some interesting words and the process, as our simulations find some interesting words and about 2/3 words that have already been invented. about 2/3 words that have already been invented. This can be extended to higher-level mechanisms, as in Mazursky, This can be extended to higher-level mechanisms, as in Mazursky, Goldberg and Solomon work on ideas for advertisement. Goldberg and Solomon work on ideas for advertisement.

Cognitive SystemsCognitive Systems

• How would a reasoning system at non-linguistic level help in any How would a reasoning system at non-linguistic level help in any branch of industry?branch of industry?One example is in understanding complex machinery reactions, as One example is in understanding complex machinery reactions, as in the refineries or other plants; this is relatively simple and may be in the refineries or other plants; this is relatively simple and may be achieved using correlation machines. achieved using correlation machines. Car driving in urban environments will need some reasoning. Car driving in urban environments will need some reasoning.

• What are the ethical problems thrown up by future advances in this What are the ethical problems thrown up by future advances in this area, advancing as it does towards the 'soul' of humanity?area, advancing as it does towards the 'soul' of humanity?People are very resistant to science and will harbor their ideas People are very resistant to science and will harbor their ideas about souls and spirits independent of the development ... about souls and spirits independent of the development ... Problems may arise in distant future when more and more jobs will Problems may arise in distant future when more and more jobs will be automated. be automated. Conscious machines will open a Pandora’s box ...Conscious machines will open a Pandora’s box ...