2021/09/01
Forecasting Uncertainty Is Never Too Large
Rob J. Hyndman gave a presentation titled “Uncertain futures: what can we forecast and when should we give up?” as part of the ACEMS public lecture series with recording available on Youtube.
He makes an often underappreciated point around minute 50 of the talk:
When the forecast uncertainty is too large to assist decision making? I don’t think that’s ever the case. Forecasting uncertainty being too large does assist decision making by telling the decision makers that the future is very uncertain and they should be planning for lots of different possible outcomes and not assuming just one outcome or another. And one of the problems we have in providing forecasts to decision makers is getting them to not focus in on the most likely outcome but to actually take into account the range of possibilities and to understand that futures are uncertain, that they need to plan for that uncertainty.
2021/05/02
Everything is an AI Technique
Along with their proposal for regulation of artificial intelligence, the EU published a definition of AI techniques. It includes everything, and that’s great!
From the proposal’s Annex I:
ARTIFICIAL INTELLIGENCE TECHNIQUES AND APPROACHES referred to in Article 3, point 1
- (a) Machine learning approaches, including supervised, unsupervised and reinforcement learning, using a wide variety of methods including deep learning;
- (b) Logic- and knowledge-based approaches, including knowledge representation, inductive (logic) programming, knowledge bases, inference and deductive engines, (symbolic) reasoning and expert systems;
- (c) Statistical approaches, Bayesian estimation, search and optimization methods.
Unsurprisingly, this definition and the rest of the proposal made the rounds: Bob Carpenter quipped about the fact that according to this definition, he has been doing AI for 30 years now (and that the EU feels the need to differentiate between statistics and Bayesian inference). In his newsletter, Thomas Vladeck takes the proposal apart to point out potential ramifications for applications. And Yoav Goldberg was tweeting about it ever since a draft of the document leaked.
From a data scientist’s point of view, this definition is fantastic: First, it highlights that AI is a marketing term used to sell whatever method does the job. Not including optimization as AI technique would have given everyone who called their optimizer “AI” a way to wiggle out of the regulation otherwise. This implicit acknowledgement is welcome.
Second, and more importantly, as practitioner it’s practical to have this “official” set of AI techniques in your backpocket for when someone asks what exactly AI is. The fact that one doesn’t have to use deep learning to wear the AI bumper sticker means that we can be comfortable in choosing the right tool for the job. At this point, AI refers less to a set of techniques or artificial intelligence, and more to a family of problems that are solved by one of the tools listed above.
2020/06/07
Rediscovering Bayesian Structural Time Series
This article derives the Local-Linear Trend specification of the Bayesian Structural Time Series model family from scratch, implements it in Stan and visualizes its components via tidybayes. To provide context, links to GAMs and the prophet package are highlighted. The code is available here. I tried to come up with a simple way to detect “outliers” in time series. Nothing special, no anomaly detection via variational auto-encoders, just finding values of low probability in a univariate time series.
2019/03/23
The Probabilistic Programming Workflow
Last week, I gave a presentation about the concept of and intuition behind probabilistic programming and model-based machine learning in front of a general audience. You can read my extended notes here. Drawing on ideas from Winn and Bishop’s “Model-Based Machine Learning” and van de Meent et al.’s “An Introduction to Probabilistic Programming”, I try to show why the combination of a data-generating process with an abstracted inference is a powerful concept by walking through the example of a simple survival model.