DECISION POINT AI™ GROUP

The world is experiencing Data Overload and Insights are highly interpretive and time sensitive. Critical decisions cannot wait for manual analysis to create contextual meaning from insights, they need to be automated to deliver decision guidance and Augmented Outcomes for Companies, Executives and Shareholders. Decision Point AI™ is the company that has an Augmented Intelligence Platform that facilitates Decisive Impact for Companies, Executives and Shareholders. It utilises Decision Science... instead of Data Science to focus corporate efforts on critical and Augmented Outcomes to deliver Decisive Impact to its clients. Decision Point AI™ is vertical and horizontal Market agnostic and is focused on technology and consultancy services for any industry or organisation. Our Augmented Intelligence Platform is already in use in Government decision making and Decision Point is currently engaging with a number of commercial clients on their specific business cases. Decision Science Data driven decision making augmented intelligence #dddm_ai "Decision Point AI™ augmented intelligence supports decision making when there is a lack of data to otherwise make decisions" While the standard pathway for Decision Science is to focus on data as a tool to make decisions and solve business problems. Decision Point 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. Real time DDDM through Decision Point AI™ 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. Subjective Logic 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 to refine and explicitly emphasise the factors and certainties 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.More