Augmented Intelligence

Decision Point Augmented Intelligence

Decision Point AI is focused on combining the best of both worlds by combining natural intelligence with artificial intelligence into augmented intelligence.

While there are many opportunities afforded by machine based systems, humans still maintain capacities for complex relational thinking that is outside of what is possible for an AI to draw down from a data file or in fact learn. In pure machine to machine environments AI makes pure logic decisions and enacts standard and agreed behaviours. Conversely humans are not logical while aspiring to be so, they operate in wild and complex rationals driven by hidden experiential architectures, meanings and insights. It is clear that some people have exceptional capabilities and rise to the top of organisations and companies, managing and valuing their knowledge and insights can now go beyond a meeting or a call but can be embedded into their AI through Decision Point AI.

Human and Machine creating Decision Points
Human and Machine creating Decision Points

Subjective Logic, Conditional Logic and Bayesian Networks for Augmented Intelligence

A fundamental aspect of the human condition is that nobody can ever determine with absolute certainty whether a proposition about the world is true or false. The human capacity to create theories or hunches can have profound impact on the direction and effectiveness of organisations, enterprise and countries. Decision Point utilises an Augmented Intelligence Platform built on subjective logic (natural intelligence) to refine and explicitly emphasise the issues and models that underwrite decision options. Additionally the Decision Point Augmented Intelligence Platform utilises human insights as part of its data queries to augment the processing power of the AI with context and meaning of that data through human knowledge and expertise.

Subjective logic is a type of probabilistic logic that combines the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit structure of formal argument. Subjective logic explicitly takes uncertainty and source data trust into account. Subjective logic is suitable for modelling and analysing situations involving uncertainty and relatively unreliable sources.

Subjective logic can be used for modelling and analysing trust networks where a measurement of the degree to which one social actor (an individual or a group) trusts another social actor and Bayesian networks that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.

Alan Turing used this approach to break the enigma code based upon the work of the Jerzy Rozycki, Henryk Zygalski and Marian Rejewski mathematicians from Poland

Technical References   

Date Description URL
2005 Analysis of Competing Hypotheses using Subjective Logic and Formal methods of Counterdeception in Intelligence Analysis
2005 Analysis of Competing Hypotheses using Subjective Logic ACH-SL DSTC
2006 Analysis of Competing Hypotheses using Subjective Logic
2013 How Intelfuze handles deception and misperception
2016 Bayesian networks and trust fusion with subjective logic

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