Relating Data, Recommendations, Boredom, and the ROI of AI
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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…
What Does a Training Data Market Mean for Authors?
If any text can be training data for a Large Language Model, then any text is a training dataset that can be valued through a market for training data. Which datasets have high value? Wikipedia, StackOverflow, Reddit, Quora are examples that have value for different reasons, that is, because they can be used to train…
Speed vs Uncertainty
Figures 1 and 2 show cost versus time; Figure 1 shows long iterations, Figure 2 short iterations. We choose to do something at time zero, at the origin of the graph in the Figure, and when we do so, we do it under some assumptions that we made at that time. Dashed red lines convey…
What Is the Depth of Expertise of an AI Training Dataset?
I use “depth of expertise” as a data quality dimension of AI training datasets. It describes how much a dataset reflects of expertise in a knowledge domain. This is not a common data quality dimension used in other contexts, and I haven’t seen it as such in discussions of, say, quality of data used for…
What is a “Decision” in an Artificial Intelligence System?
In the context of human decision making, a decision is a commitment to a course of action (see the note here); it involves mental states that lead to specific actions. An AI system, as long as it is a combination of statistical learning algorithms and/or logic, and data, cannot have mental states in the same…
Limits of Explainability in AI Built Using Statistical Learning
How good of an explanation can be provided by Artificial Intelligence built using statistical learning methods? This note is slightly more complicated than my usual ones. In logic, conclusions are computed from premises by applying well defined rules. When a conclusion is the appropriate one, given the premises and the rules, then it is said…