How Data Availability and Cost Relate to AI Differentiation?
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Opaque, Complex, Biased, and Unpredictable AI
Opacity, complexity, bias, and unpredictability are key negative nonfunctional requirements to address when designing AI systems. Negative means that if you have a design that reduces opacity, for example, relative to another design, the former is preferred, all else being equal. The first thing is to understand what each term refers to in general, that…
Can Decision Autonomy of an AI Be Distinguished from Malfunction?
I wrote in another note (here) that AI cannot decide autonomously because it does not have self-made preferences. I argued that its preferences are always a reflection of those that its designers wanted it to exhibit, or that reflect patterns in training data. The irony with this argument is that if an AI is making…
Data Authenticity, Accuracy, Objectivity, and Diversity Requirements in Generative AI
In April 2023, the Cyberspace Administration of China released a draft Regulation for Generative Artificial Intelligence Services. The note below continues the previous one related to the same regulation, here. One of the requirements on Generative AI is that the authenticity, accuracy, objectivity, and diversity of the data can be guaranteed. My intent below is…
Business Risks of IP Compliance Requirements for Generative AI
IP compliance requirements on generative AI reduce the readily and cheaply available amount of training data, with a few consequences on how product development and product operations are done.
When Is a Career Resilient to AI?
The short answer: careers that reward creative problem solving in domains with scarce knowledge. Let’s unpack that.