Karl Smith CEO of Paradigm Interactions Inc to lead Decision Point AI for 6 months

Initially discussed as a Joint Venture with Paradigm Interactions Inc the new division Decision Point of the as yet unnamed Augmented Intelligence Corporation will be lead by our CEO, Karl Smith.

Decision Point is the only #dddm_ai company, for #augmentedintelligence in #fintech #medtech #suptech #regtech #govtech #sectech #edtech #insurtech

Karl Smith, Decision Point

Always leading from the front Karl continues to drive innovation and consulting to new opportunities and possibilities for clients.

About Decision Point

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.

man think how to solve the problem

Decision Point augmented intelligence supports decision making when there is a lack of data to otherwise make decisions

Karl Smith, Paradigm Interactions Inc.

Decision Point has unlimited potential in all verticals and sectors and can deliver information in a useful format on which to based decisions. Additionally in line with the All-Party Parliamentary Group on Data and Analytics policy document Trust, Transparency and Technology: Building Data Policies for the Public Good, will have an Algorithmic Operational Passport linked to the existing capacity to create regulatory point in time reporting. Paragraph added 22nd May 2019

Decision Point, Decisive Impact

With the levels of complexity and constant market movement experienced by organisations, enterprises, divisions defining the right question can often be a huge and expensive process. Long before being able to decide the next move, having the capacity and time to understand all the data points, opinions and perspectives requires months or years to confidently direct transformation, customer relationship management, operational risk management. And for fast moving aspects of commerce, transactions need to rely more and more on automation.

Companies need traceability and proof to make decisions and technology alone cannot deliver that assurance

Decision Point creates augmented outcomes binding technology and human insights that enables decision makers and automated systems to deliver confident, effective and influential decisions driving organisations, enterprises, divisions and markets to new levels of success.

The science; subjective logic 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 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.

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