About

Karma Flows introduces a mathematical formulation of the social force of Karma and a machine learning framework combining mathematical graph computation and artificial intelligence, which form a global sentiment aggregator for the measurement of social energy flows.

Karma Flows was authored by Christoph Kohlhepp in Queensland, Australia and is distributed by Fulcrumbright Ltd. of Nova Scotia, Canada. Karma Flows is patent pending in the United States of America, the United Kingdom and Australia as well as internationally through the Patent Cooperation Treaty (PCT).

Karma Flows is formulated as a data processing, filtering and sampling framework which fuses structural, interpretable graph machine learning with graph neural networks, in particular graph deep learning, and introduces a Graph Attention Mechanism (GAM) whereby deep learning guides an interpretable graph computation substrate in order to move from a paradigm of “Human Driven & Machine Assisted” to a paradigm of “Machine Guided & Human Evaluated.”

In the context of society, we regard Karma as the social energy flow that compels entities towards action. We observe similar dynamics at stock exchanges where order books collate price levels with bids and offers representing the sentiments of traders and which distil towards what is called the base weight for the instrument being traded. When price pressures build sufficiently in the direction of either bids or offers, prompting a shift in the base weight, then trade events occur. In physics (thermodynamics) all energy build-up in a connected system results in the transfer (flow) of energy along the lines of connection. In human society, sentiment represents emotional energy which precipitates action. In aggregate, that action becomes predictable. It is this we model as Karma and its flow through society.

Karma Flows enables investigators to identify leading influencers in news events, quantify the news negativity bias, attribute both likely endorsement and likely blame for news events to specific entities as well as identify hidden collaborators. Crucially, Karma Flows allows investigators to structurally predict (foresight) the general future, structurally nowcast (foresight) the general present and structurally interpret (gain insight) the general past. Additionally, Karma Flows allows the quantitative measurement of relatedness of entities such as persons, organizations, countries, or places to thematic concepts such as economic volatility or security threats, both in the present and future and to quantify the positivity or negativity of such relatedness. For instance, an investigator might ask if an organization is positively or negatively related to economic volatility - now and in the near future - and quantify this relatedness to facilitate objective comparisons. Crucially, and leaning on the notion of General Artificial Intelligence, Karma Flows can use the newly introduced formulation of Karma to make general predictions for the future, rather than predictions constrained to a particular domain or discipline, as is customary with present Machine Learning technology.