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An Alternative for Artificial Intelligence – conceptual and formal theory. Prof. Dr. Tamás Gergely Applied Logic Laboratory. AI “ field ”.
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An Alternative for Artificial Intelligence – conceptual and formal theory Prof. Dr. Tamás Gergely Applied Logic Laboratory
AI “field” Upto these days there has not existed a coherent field called AI with its own conceptual apparatus and with a scientifically based methodology. Also, there has not yet been a generally accepted philosophical foundation developed in the form of such epistemology and ontology, which would have considered and responded to the challenges that arise during the development of the AI “field”.
What is AI? Today, AI is a conglomerate of techniques, technologies and of various research and development directions. Machine learning and deep learning, especially, are the most common methods. Artificial intelligence covers many technologies and realities that lead to misunderstandings about what it exactly means.
Some areas related to AI V. Kotu, B. Deshpande, Data Science - Concepts and Practice, Morgan Kaufmann, 2019
What we propose? We propose an approach, the target of which is to provide a technologically well-equipped Amplifier for Intelligence, that would be able to act as genuine problem-solving companions understanding and responding to complex problem situations.
Artificial intelligence as such should be a symbioTIC PARTNER, not a replacement https://blogs.berkeley.edu/2017/09/18/coevolution-of-human-and-artificial-intelligences/
Collaborative Intelligence The longterm vision is to develop a theoretically well-founded, coherent, integrated theory, technology and design methodology for a new computation paradigm – the so called COgnitive Intelligence co-Operating System (COIOS) In our approach COIOS supports a Collaborative Intelligence, where humans and AI systems are joining their abilities.
COIOS Systems COIOSsystemswill be able to act as genuine problem-solving companions that can understand and respond to complex problem situations. COIOSsystem will be able to serve eitheras a partner system for cooperative functioning with a human agent, oras an autonomouscognitivesystemfor a welldefinedproblemarea.
The external world Two types of worlds: (i) world of regular events („Mediocristan”*) and the world of extreme events („Extremistan”*). Mediocristan is where normal things happen, things that are expected, whose probabilities of occurring are easy to compute, and whose impact is not terribly huge. The Guassian distribution is emblems of Mediocristan. Mediocristan therefore constitutes of the normal, the easy to predict, the expected, the small impact, the mundane. Exstremistan is where extreme things happen. In Extremistan, nothing can be predicted accurately and events that seemed unlikely or impossible, occur frequently and have a huge impact. The Cauchy distribution is emblems of Exstremistan. *Taleb, Nassim Nicholas. The Black Swan: The impact of the highly improbable. Random House, 2007.
Cauchy and Gaussian density functions Solid red curve is a Cauchy density function with z0=10 and b=1. The dashedcurve is a Gaussian one with the same peak as the Cauchy(1/π) with mean=10 andvariance= π/2. The Cauchy has heavier tails.
World of regular events Types of regular worlds: • Deterministic • Probabilistic • Mixed
Philosophical level Epistemology without cognizing agents General knowledge about how knowledge is acquired represented and used Epistemology with cognizing agents It is focused on the analysis of the concepts: knowledge justification, truth, rationality etc., methodology of knowledge management and information processing
Integrated Logic Framework for Computing Cognitive Reasoning Framework Hierarchic Data Science with data structured analysis Computational cognitive linguistics
Integrated Logic Framework for Computing The first pillar
The first pillar Integrated Logic Framework for Computingprovides anintegrated theory of programming and a unified theory of specification. The theory of programming covers different programming paradigms, such as imperative, declarative and OO programming
Cognitive Reasoning Framework The second pillar
The second pillar Cognitive Reasoning Framework(CRF) is a generic, unified technological and methodological framework. It integrates cutting-edge research in non-classical logics related to cognizing. It helps to detect and solveactual uncertainties through complex information processing and cognitive reasoning.
Some CRF properties CRF integratesplausible and reliableinference. Plausiblereasoningcontainsthefollowingclasses: • probabilisticreasoning • approximatereasoning (forexample, usingtheapparatus of fuzzy setsorpossibilitytheory) • heuristicalreasoning (forexample, explanationreasoning, JSM reasoning, etc.) • Model-basedreasoning CRF supportstheformalisation and embedding of anytypeofheuristics
Bridge between cognitive and semantic computations Referential reasoningis based on an innovative non-classical logic foundation and connects the level of semantic representations with that of reasoning. This type of reasoning supports the semantic aspect as follows: • reasoning rules implicitly use evaluation in some standard models of the theory within which the reasoning processes take place and they also refer to the system’s knowledge; • in a reasoning process obtaining new statements and estimation of their degree of plausibility occur simultaneously.
Hierarchic Data Science with data structured analysis The third pillar
Hierarchic Data Science 1 Hierarchic data science is a specific one, which uses hierarchic analysis and structuring methods for realising the data-information-knowledge sequence processes. Itprovides • technology for appropriate data handling, data analysis, information and knowledge acquisition, • cognitive methods to control and supervise the data-information-knowledge sequence process.
Hierarchic Data Science 2 The data-information-knowledge sequence process is realised in a knowledge-driven and semantically controlled way. It uses, among others, a special form of knowledge organisation by the use of the symptom-syndrome network technology together with the syndrome analysis method.
Capsule based cognitive semantics The fourth pillar
The fourth pillar A special approach to computational cognitive linguistics,which is a generalisation of Filmore’s frame semantics and it is based on ontology technology and cognitive semantics.
Computational cognitive linguistics The ontology capsule method and the corresponding cognitive semantic approach were developed to support the management of natural language related situations. The ontology capsule method enables the integration of various perception channels by their interpretation in the capsule structure. This encapsulates detailed visual, spatial-geometric and audio information in addition to language-oriented, symbolic conceptual content (basic semantic units). The ontology capsule-based, modality-independent semantic representation methodology helps cognitive computing systems to understand problem situations and embed them in the relevant knowledge and cognitive reasoning environment.
Types of capsules • Cognizing: situations are represented by knowledge capsules, • which reflect the capsule descriptions at the modelling level, but its structure may be changed (e.g. In the case when the systemis one of the actors), and the data types of the parameters have to be given. • which are linked to knowledge that controls the processing of factual situations. • Languaging: capsules in the semantic lexicon reflect the knowledge capsules, and • linguistic knowledge is added to the capsules, which may cause some modifications w.r.t. to the elements of the capsules, • a message language is defined to the knowledge base, and coupled to the elements of the capsules.
Towards the integrated universal theory and methodology of COIOS
The R+D roadmap • First stage: selecting the perspective modules from the Foundation Pillars and making them operational and usable. Development of the work programme. • Second stage: Development of the unified conceptual and formal theories that integrate the main relevant aspects of the theories of the Pillars. • Third stage: Development of the technological components • Fourth stage: Platform development with all necessary methodologically supportive modules and systems • Fifth stage: Development of the COIOSecosystem to support application systems development
The unified mathematical foundation A unique trans-disciplinary logical-mathematical foundation is developed, which integrates (i) computing process theory that describe the computer world, (ii) Cognitive Reasoning Framework that integratesplausible and reliable inference and contradictory thinking processes, (iii) Hierarchic Data Science that provides knowledge and semantics driven special data handling calculus.
Examples of generality The proposed framework can represent e.g.: Granular programming Probabilistic programming Machine learning
Cognition Kernel Cognition Kernel will be the main construct, which generalises (i) the information theory and the corresponding reasoning and inference system, (ii) the modification theory and the corresponding modification calculi and (iii) the knowledge components together with knowledge and semantics driven special data handling calculi.
