Causal AI

Causality is the principle that there is a cause for everything that happens

Traditional AI is Broken

Traditional machine learning AI is fundamentally broken. Results are only as good as the data we provide for training, so the outcomes are always based on the how we did things in the past and highly dependent on the quantity and quality of data available.

Another challenge is corelation and the potential to ‘discover’ false positives because its often unclear exactly which data is relevant, and which is not. Commonly referred to as a ‘data first’ approach this can lead to hidden bias in the outcomes we produce.

The Problem with Correlation

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Data Quality and Data First

Data science typically takes a data-first approach looking for corelations without necessarily truly understanding the problem domain or data quality.

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Large Datasets Only

By definition, machine learning it is constrained by the data we feed it, typically requiring extremely large datasets which are critical to accurate results. We are often faced with problems

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Bias and Variance

Using correlation to determine patterns that are used as a basis for decision is fundamentally flawed because unless we have data to represent every significant cause of an effect on our result and we’re able to eradicate backdoor paths and confounders, the probability of bias is high.

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Explainability and Transparency

ML algorithms are typically implemented from open-source AI libraries where the heavy work is done in a black box. This approach has a lack of explainability because without truly understanding what is in the black box it’s impossible to say ‘why’.

Causality Has the Answer

Thinking about the world, or more importantly your problem domain, in terms of cause and effect resolves many of the challenges faced with traditional machine learning.

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Common Language

Capturing the scope of your problem domain through causal modeling provides an easy way to collaborate within your team and business. Geminos Causeway provides a multi-user platform to enable collaboration  and capture domain knowledge is a simple intuitive way.

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Reduced Bias and Fair

The science of causal algebra has causal analysis and validation built in to eradicate bias. Geminos Causeway gives you access to these powerful techniques without having to worry about math.

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Adaptive to Change

Because of Causeways end-to-end traceability
(model <> application), changes to the causal model can be rippled through data to the AI implementation and visa-versa. This process is facilitated through the Causeway platform.

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Explainable

AI solutions built using causality are intrinsically explainable through the causal models. There is absolute traceability from each event to related data and ultimately AI functions and predictions.

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Human-Centric

Rather than limiting human-machine interaction, Causal AI allows a tighter partnership and sharing of knowledge

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Small or Large Datasets

Geminos Causeway can apply its discovery algorithms to only the information that matters, even small amounts of data. Coupled with human input, it can fill in gaps in data to work with any kind of dataset, big or small.

Maximize Your Digital Operational Efficiency with Causal AI

At Geminos, we’re dedicated to crafting the world’s most effective AI solutions underpinned by causal science to usher in the next industrial revolution. If you’re ready to harness the power of Causal AI, contact us to get started. See why more and more industry-leading businesses around the world are trusting causality to power their operations.

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