What Data Can’t Do
newyorker.com
What Data Can’t Do
1–10 of 106 posts
Re: What Data Can’t Do
#2The article seems to stop pretty early, as if something is missing.
It’s an anecdote about a government incentive to have doctors see patients within 48 hours causing doctors to refuse scheduling patients later than 48 hours in order to get the incentive bonus.
This is not an example of limits of data, but an example of perverse incentives.
Re: What Data Can’t Do
#3The article seems to stop pretty early, as if something is missing. It’s an anecdote about a government incentive to have doctors see patients within 48 hours causing doctors to refuse scheduling patients later than 48 hours in order to get the incentive bonus. This is not an example of limits of data, but an example of perverse incentives.
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Re: What Data Can’t Do
#4I am increasingly worried with people applying ML in everything without any rigour.
Statical inference generally only works well in very specific conditions:
1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong)
2 - Using (1), you calculate how much data you need so you get an estimation error below x%
Even though most ML models are essentially statistics and have all the same limitations (issues with convergence, fat tailed distributions, etc...) it seems the industry standard is to pretend none of that exists and hope for the best.
IMO the best moneymaking opportunities in the decade will involve exploiting unsecured IOT devices and naive ML models, we will have plenty of those.
Re: What Data Can’t Do
#5The article seems to stop pretty early, as if something is missing. It’s an anecdote about a government incentive to have doctors see patients within 48 hours causing doctors to refuse scheduling patients later than 48 hours in order to get the incentive bonus. This is not an example of limits of data, but an example of perverse incentives.
> It’s an anecdote about a government incentive to have doctors see patients within 48 hours causing doctors to refuse scheduling patients later than 48 hours in order to get the incentive bonus.
That part is probably just the first 1/8th or so of the article (rough guess). Sounds like it was cut short for you?
Re: What Data Can’t Do
#6Data always needs to be paired with empathy. ML/AI simply doesn't have empathy so it will always be missing a piece of the overall pie.
Let AI crunch the numbers, but combine it with a human who can understand the "why" of things and you can really kick butt.
Re: What Data Can’t Do
#7I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially…
Wait until you find out low many studies have been published in medical journals with serious statistical flaws.
Re: What Data Can’t Do
#8From the ungated archive [1]:
> Whenever you try to force the real world to do something that can be counted, unintended consequences abound. That’s the subject of two new books about data and statistics: “Counting: How We Use Numbers to Decide What Matters”, by Deborah Stone, which warns of the risks of relying too heavily on numbers, and “The Data Detective”, by Tim Harford, which shows ways of avoiding the pitfalls of a world driven by data.
Data is a powerful feedback mechanism that can enable system gamification; it can also expose it. The evil is extracting unearned value from a system through gamification not the tools employed to do so. I’m looking forward to reading both books.
Re: What Data Can’t Do
#9Data always needs to be paired with empathy. ML/AI simply doesn't have empathy so it will always be missing a piece of the overall pie. Let AI crunch the numbers, but combine it with a human who can understand the "why" of things and you can really kick butt.
I agree with you, although, unfortunately, most -- if not all -- engineers I know would respond to this by complaining about how "a human who can understand the 'why'" cannot be automated.
Re: What Data Can’t Do
#10I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially…
ML looks (for many peole) like a way to circunvent your grumpy statiscian saying that the underlying data is worthless and/or you should focus on getting the data pipeline done properly for a logit model on your churn rate.