Where are you viewing statistics as having failed, precisely?
Lots of well-documented success in traditional manufacturing and logistics type things. The "AI winter" wasn't a statistical thing at all - rather, stats have succeeded where logical programming had failed. Branded as "machine learning" or "data science," sure, but I don't recall a time of handwringing over stats failing in between.
(And I've heard "train a model" a thousand times more than "train an algorithm" from data folks in industry...)
> Where are you viewing statistics as having failed, precisely? <etc>
I don't think it's failed. But it certainly failed to capture the public imagination. Some people I talk to (fewer now than in the past) seem to think that statistics is old and irrelevant, and that machine learning is new and changing the world. Those people also don't usually have any idea of what they're talking about, but the idea is out there.
> "train a model"
But how often do mass media or industry publications talk about "models" as some kind of new hotness? It's all about "AI algorithms" now.
The terminology usage is especially surreal in the "digital insurance" space [0], where content marketing writers churn out breathless articles about how AI is the next big thing, as if insurance execs didn't understand what a model was!
It is my understanding that AI is being used in insurance not to supplant the risk models, but to automate human-driven tasks, such as reviewing claims or documents, or to augment risk models with new data.
That's correct. But the difference between an AI claim processing algorithm and the risk model lies more in how the model is used than in how it actually works.
Of course, the technical details of a text transformer differ significantly from the technical details of a decision tree or logistic regression. But when you zoom out a little, it's clear that many of the same core principles and techniques are used in both cases.
So the content marketers are right in that the next big thing is automating tasks like claim processing. But I find it at least a little bit silly because, at its core, an AI claim processing algorithm is not so conceptually different from a pricing model, and it's well within the realm of understanding for many people on the quantitative side of the insurance business.
I also want to avoid making particular claims or judgments about what AI "is". I will leave that to the futurists, philosophers, and AI researchers.
Lots of well-documented success in traditional manufacturing and logistics type things. The "AI winter" wasn't a statistical thing at all - rather, stats have succeeded where logical programming had failed. Branded as "machine learning" or "data science," sure, but I don't recall a time of handwringing over stats failing in between.
(And I've heard "train a model" a thousand times more than "train an algorithm" from data folks in industry...)