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> I think this is why computer science has been so successful at co-opting a lot of statistic's thunder. It's reorganizing a lot of concepts, and because everyo

> I think this is why computer science has been so successful at co-opting a lot of statistic's thunder. It's reorganizing a lot of concepts, and because everyone is interested in the results (image generation, computer vision, etc) it's getting a lot of adoption. I think it's also that "computer science" can be perceived as having succeeded where statistics failed. And the terminology is frankly a lot more appealing: would you rather "train an AI algorithm", or "fit a model"? Even the recasting of "model" as "algorithm" has a marketing benefit: models have uncertainty and uncertainty is scary, whereas algorithms are perceived as precise and correct.

canjobear 4y ago
Usually I hear “training a model”
- blitzar 4y ago
  When I hear "training a model" I translate that to y=mx+c
  - wenc 4y ago
    I think of Y = f(X). f can be anything from tree based model to a neural network to nonparametric model. Y = Mx + c is simple and common but by no means the only kind of model.
    - ihatepython 4y ago
      I think what the poster was implying was that most people create simple models, because they have a linear thought process, but they call it a model to sound sophisticated.
      - blitzar 4y ago
        I was going for they call it a model to sound sophisticated - they call it training a model when they are really doing a linear regression. Technically doing a pair of linest in excel and a goal seek to weight the output of each is Ai. Sometimes the Groundbreaking Ai startup that just raised a gazillion dollars in their 10th up round, is just a plain old basic high school statistical model.
        nerdponx 4y ago
        Linear regression is a legitimate model, and fitting its parameters is definitionally machine learning.
  - smohare 4y ago
majormajor 4y ago
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...)
- nerdponx 4y ago
  > 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! [0]: https://www.acko.com/digital-insurance-trends-and-benefits/ https://www.acko.com/digital-insurance-trends-and-benefits/
  - jwozn 4y ago
    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.
    - nerdponx 4y ago
      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.

canjobear 4y ago

Usually I hear “training a model”

blitzar 4y ago
When I hear "training a model" I translate that to y=mx+c
- wenc 4y ago
  I think of Y = f(X). f can be anything from tree based model to a neural network to nonparametric model. Y = Mx + c is simple and common but by no means the only kind of model.
  - ihatepython 4y ago
    I think what the poster was implying was that most people create simple models, because they have a linear thought process, but they call it a model to sound sophisticated.
    - blitzar 4y ago
      I was going for they call it a model to sound sophisticated - they call it training a model when they are really doing a linear regression. Technically doing a pair of linest in excel and a goal seek to weight the output of each is Ai. Sometimes the Groundbreaking Ai startup that just raised a gazillion dollars in their 10th up round, is just a plain old basic high school statistical model.
      - nerdponx 4y ago
        Linear regression is a legitimate model, and fitting its parameters is definitionally machine learning.
- smohare 4y ago

majormajor 4y ago

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...)

nerdponx 4y ago
> 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! [0]: https://www.acko.com/digital-insurance-trends-and-benefits/ https://www.acko.com/digital-insurance-trends-and-benefits/
- jwozn 4y ago
  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.
  - nerdponx 4y ago
    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.