The three dimensions of AI and a fourth one as the key to unlock them

Comments on a review of AI by John Launchbury, special assistant to DIRO, DARPA

4 minute read

Abstract

In an awesome review of AI John Launchbury, special assistant to DIRO, DARPA, defined four dimensions of processing information:

  1. perceiving
  2. learning
  3. abstracting
  4. reasoning

This is not any perspective on AI, it is a perspective from the founders and pioneers of internet. Although there has been significant progress with first and second generation AI systems in reasoning, learning and perceiving, abstraction has not been part of the game. In fact Launchbury could not say much about this in his talk.

Nevertheless he explained clearly where we are heading in the third generation of AI systems. Models and contextual information will play a critical role. But this is a pretty close match with Aristotle’s theory of semiosis. The mechanism of abstraction is related to the ideal world of models and the world of real objects. Those two worlds are bridged by symbols, signs and these create meaning in contextual information. So this is the objective of third generation AI.

It is not a coincidence that I have been investigating since 2012 how this triangle of meaning can be applied to the problem of information representation. Recently I have presented officially to the public R3DM/S3DM data modeling framework in European Wolfram Technology Conference. In one of his talks Conrad Wolfram emphasized how important is abstraction to education and learning. But so far this mechanism of abstraction has not been computable. This is what we anticipate to see in the near future.

The mechanism of abstraction can unify these other three processes of perception-interpretation, learning, and reasoning. And it can also interconnect every thing on the internet, every bit of information, in the very same way our computers are interconnected with IP addresses.

We are here to build powerful, meaningful relationships easily. This is our mantra after all.

Cross-References

R3DM Project Posts

2017

Associative Semiotic Hypergraph API in Mathematica for Next-Generation BI Systems
European Wolfram Technology Conference 19-20 June 2017 in Amsterdam
My speech at European Wolfram Technology Conference 2017 about a new data modeling framework R3DM/S3DM that is implemented on top of OrientDB graph database and coded in Wolfram Mathematica

Are our old data model standards out of shape ?
An overview of critical points to consider when modeling with R3DM/S3DM
Both Topic Maps and RDF/OWL exhibit signs of aging. These signs do not indicate maturity levels but on the contrary they signal a re-examination of the data modeling, information representation problem

The three dimensions of AI and a fourth one as the key to unlock them
Comments on a review of AI by John Launchbury, special assistant to DIRO, DARPA
Although there has been significant progress with first and second generation AI systems in reasoning, learning and perceiving, abstraction has not been part of the game. The mechanism of abstraction can unify these other three processes.

Associative Data Modelling Demystified: Part 6/6
R3DM/S3DM: Build Powerful, Meaningful, Cohesive Relationships Easily
Demonstration of a new data model framework that transforms OrientDB into a HyperGraph Database

Data Modelling Topologies of a Graph Database
Definition and Classification of Graph Databases into Three Categories
The associative data graph database model is still a heavy hitter, stacking up well against property graphs and triples/quadruples. Expect a comeback.

A Quick Guide on How to Prevail in the Graph Database Arena
A brief discussion on criteria to meet a differentiation strategy for graph databases
A swift introduction to the key factors that influence the performance and unification character of graph databases

Associative Data Modeling Demystified: Part 5/6
Qlik Associative Model
Qlik's competitive advantage over other BI tools is that it manages associations in memory at the engine level and not at the application level. Every data point in every field of a table is associated with every other data point anywhere in the entire schema.

2016

Associative Data Modeling Demystified: Part 4/6
Association in RDF Data Model
In this article we will see how we can define an association in RDF and what are the differences with other data models that we analyzed in previous posts of our series

Do you Understand Many-to-Many Relationships ?
Associative entities are represented differently in various data models
It is 2016 and in my opinion the situation with associative entities has become darn confusing. Edges of a Property Graph data model are bidirectional but RDF links are unidirectional.

Associative Data Modeling Demystified: Part 3/6
Association in Property Graph Data Model
In this article, we continue our investigation with the Property Graph Data model. We discuss how a many-to-many relationship is represented and compare its structure in other data models

Associative Data Modeling Demystified: Part 2/6
Association in Topic Map Data Model
In this post, we demonstrate how Topic Map data model represents associations. In order to link the two, we continue with another SQL query from our relational database

Associative Data Modeling Demystified: Part 1/6
Relation, Relationship and Association
In this article, we introduce the concept of association from the perspective of Entity-Relationship (ER) data model and illustrate it with the modeling of a toy dataset

2015

Towards a New Data Modelling Architecture
Part 2: Atomic Information Resource (AIR)
We introduce the Atomic Information Resource (AIR) unit of R3DM conceptual framework

Towards a New Data Modelling Architecture
Part1 - Relational/ER Constructs in Wolfram Language
We start with terms and constructs that most of us are familiar with from the Relational and Entity-Relationship database management systems