Decision Science

Data driven decision making through augmented intelligence

While the standard pathway for Decision Science is to focus on data as a tool to make decisions and solve business problems. Decision Point AI™ has further augmented the current thinking by adding collated data through an augmented intelligence platform and applying human interpretive algorithms that enable expert context to establish both true meaning and trajectory in a real time manner.

Decision Point AI™ UK Augmented Intelligence Decision Making Platform
Decision Point AI™ UK Augmented Intelligence Decision Making Platform

Real time DDDM through Decision Point dddm_ai establishes a clear competitive advantage in a world where enterprises are told competitive advantage no longer exists

DDDM_AI is fundamentally different from the slower data science route which is involved in finding insights by analysis and modelling from data, after this data has been collected, processed and structured by data engineer. While business who are

driven most by data-based decision making had 4% higher productivity rates and 5-6% higher profits, a study of 179 large publicly traded firms

by Brynjolfsson, Hitt and Kim of MIT Center for Digital Business found in 2011 based on Data Science. No impact analysis currently exist for the evolutionary real time DDDM_AI that utilises augmented intelligence but by extrapolation the expectation is for higher productivity, utilisation and profits for client companies.

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 and is the end point in the functional use of AI and its translation layer back into human context for businesses and organisations

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™.

Neural Networking and Bayesian Networks to Augment 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.

Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules. 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

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