Artificial intelligence is altering how humans make decisions.
That means we need to have a good understanding of the real world problems in order to instruct AI’s appropriate; we know that ill-defined prompts produce rubbish results.
In my work developing post-agentic AI-cognitives, I find they perform well when instructed in an iterative way, with progressive precision and specificity rather than one-off interrogations. The objective to achieve metacognition, not just answers.
Here I am in conversation with my AI mind. The image is unprompted visualisation.
Prediction is power over the uncertainty of the problems of the emergent future.
Cassis has transitioned from a healthcare and life sciences consultancy to a developer of tools for use by clinicians and pharmaceutical companies; I don’t develop at this stage consumer products as my focus is on where the compelling challenges and costs are.
Overview
Work has been done developing models of ‘clinical minds’. As a result, there is a growing suite of cognitive applications which in the main are designed to help clinicians reflect critically on their reasoning and in so doing reduce the risk of avoidable medical errors. (You can likely tell my McMaster University Faculty of Health Sciences background here). Many of these have potential in medical education and others embedded in EHRs.
Feel free to get in touch on any of this work presented on the site.