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We shouldn't ever confuse machine learning with predicting the future -- just because you've never encountered a black swan in the wild, doesn't mean they don't

We shouldn't ever confuse machine learning with predicting the future -- just because you've never encountered a black swan in the wild, doesn't mean they don't exist. That being said, the article otherwise seems like a great introduction. Not sure why they chose that title.

calafrax 9y ago
It is an unfortunate misconception that statistical probability can be used to predict the future. Any time you extend a statistical model temporally it immediately becomes mathematically invalid since probabilistic statistics are only valid for a fixed population at a fixed moment in time. Unfortunately business and government is rife with people predicting the future based on statistical models that have no more mathematical validity than reading tea leaves.
- gbrown 9y ago
  What??? Prediction is certainly a type of extrapolation, but to claim that it's "mathematically invalid" reveals a severe lack of knowledge on your part. In fact, under parametric assumptions about the data generating mechanism, we can exactly quantify the expected coverage of prediction intervals. That's literally a standard topic in an introductory statistics course.
  - sgt101 9y ago
    Hello, I think Calafrax is probably right. :o) I think you implicitly agree because you say "under parametric assumptions..." which means you know whats going on; but to make the point-> Statistics as we know it "works" (can be derived) under the assumptions of controlled experimental data. As a thought experiment think about the weather - we know that if we build a classifier that predicts the weather in my garden tomorrow based on the history of the weather in my garden it will do very badly. Why - well because weather is very very very complex; the range of behavior is vast. But worse, it's unstable. The weather in my garden is driven by several complex systems; the ocean, the atmosphere, the earth's orbit and sol! Statistics can't predict the future of the weather in my garden. Statistics also can't predict other things like the future of the financial markets (not least because if you find a statistical law about that they you will act on it and then screw it up) It's important to me to bang on about this because there are loads of people who sit through their introductory courses and read the example of predicting a biased roulette wheel. Years later they end up running the company/country/community that I live in and they have a view that they can use the same principles to do it... and this thinking leads to nasty surprises for me.
    - panarky 9y ago
      > if we build a classifier that predicts the weather in my garden tomorrow based on the history of the weather in my garden it will do very badly Give me hourly readings of temperature, wind speed, wind direction, precipitation, cloud cover and barometric pressure for the last 10 years and I can give you a very accurate prediction of tomorrow's weather in your garden.
      - calafrax 9y ago
        is that a joke? weather predictions are notoriously unreliable even though they are given with extreme granularity. that aside you are missing a larger point. if you predict the future based on past data all you are saying is "the future will be the same as the past." you aren't predicting anything. you will be wrong every single time something novel occurs, which is pretty frequently in the real world.
        ryanwaggoner 9y ago
        The perception that weather forecasting is notoriously unreliable is mostly false: https://mobile.nytimes.com/2012/09/09/magazine/the-weatherman-is-not-a-moron.html?referer= https://mobile.nytimes.com/2012/09/09/magazine/the-weatherma...
        sgt101 9y ago
        From your link : "Why are weather forecasters succeeding when other predictors fail? It’s because long ago they came to accept the imperfections in their knowledge. That helped them understand that even the most sophisticated computers, combing through seemingly limitless data, are painfully ill equipped to predict something as dynamic as weather all by themselves. So as fields like economics began relying more on Big Data, meteorologists recognized that data on its own isn’t enough."
        gbrown 9y ago
        Quantifying uncertainty is one of the main points of statistics. Don't confuse the limitations of point estimates provided by machine learning techniques with all of statistical practice.
ben_w 9y ago
Black Swans are the error rate of your predictions (the real error rather than your prediction of your error rate) not existential proof that prediction is always doomed. After all, if Black Swans were common enough to make prediction a fool's errand most of the time, the bird of that name would never have led to the book of that name, because everyone would be predicting their failure to predict things.
- sgt101 9y ago
  I think that a Black Swan is when a new factor appears in your domain. In science we are conditioned from the start to create fair tests in controlled experiments. Control is the fundamental of experiment - and statistics are designed to handle experimental data. In the real world there are often no controls, and complex systems can be driven by an attractor for a very long time before one morning they are not, and every rule that you have is useless (often worse than useless). Sources of error are not equal; "Black Swan Error" is unusual in that over time it may be that this source is more important than any other source of data in your domain - the strange attractor that drove the creation of your classifier over the last 20 years may never recapture your function and if that's the case your classifier will be literally the most wrong thing you could have!
  - ben_w 9y ago
    That's certainly one type of Black Swan. Taleb's example of that sort of thing being a Turkey predicting they will be fed (because that is what happened every other day of their life) but who is actually slaughtered. However, it is not the only type. There is also the stock market, which demonstrates major unpredictability every few years, but which can also be approximated the same way between each of the Black Swans. (And they keep being Black Swans because the gap between them is large enough for people to convince themselves that "This time it's different, this time n̵o̵b̵o̵d̵y̵ ̵w̵i̵l̵l̵ ̵h̵a̵v̵e̵ ̵t̵o̵ ̵b̵e̵ ̵n̵a̵i̵l̵e̵d̵ ̵t̵o̵ ̵a̵n̵y̵t̵h̵i̵n̵g̵ growth will be eternal!") Edit: Point is, it generalises as how wrong you are in your predictions, and the closer your estimate of your error rate is to your actual error rate, the better your model is.
- taneq 9y ago
zEVE16Ug50tV 9y ago
> just because you've never encountered a black swan in the wild, doesn't mean they don't exist. Great point. We can't know that a machine learning algorithm used to make predictions won't be wrong if the future turns out to be significantly different from the past. A swan-classifier trained on images of white swans would fail hard if given pictures of black swans. That said, people find it useful to use machine learning algorithms to predict the future, as the future tends to be similar to the past, at least in the limited domains to which machine learning is currently applied. As compute increases and we learn how to write machine learning architectures[0], we don't need to distinguish as much between 'machine learning' and plain old 'learning' and much of what philosophers have thought over the years about the problem of induction, and relevant domains of induction, becomes relevant to the topic. [0] Or learn them. Jeff Dean mentions experimental success learning RNN architectures: https://www.youtube.com/watch?v=vzoe2G5g-w4 https://www.youtube.com/watch?v=vzoe2G5g-w4
pcmaffey 9y ago
Any relevant prediction model should account for the probability of black swans existence, even if it may have no idea what a black swan might look like.
- TimPC 9y ago
  This runs against the challenge that (almost?) all statistical methods train by fitting a model to some sort of data. If you have zero examples of a black swan in the data you can agree in principle they might exist but you'd expect a statistical model to get them wrong.

calafrax 9y ago

It is an unfortunate misconception that statistical probability can be used to predict the future. Any time you extend a statistical model temporally it immediately becomes mathematically invalid since probabilistic statistics are only valid for a fixed population at a fixed moment in time. Unfortunately business and government is rife with people predicting the future based on statistical models that have no more mathematical validity than reading tea leaves.

gbrown 9y ago
What??? Prediction is certainly a type of extrapolation, but to claim that it's "mathematically invalid" reveals a severe lack of knowledge on your part. In fact, under parametric assumptions about the data generating mechanism, we can exactly quantify the expected coverage of prediction intervals. That's literally a standard topic in an introductory statistics course.
- sgt101 9y ago
  Hello, I think Calafrax is probably right. :o) I think you implicitly agree because you say "under parametric assumptions..." which means you know whats going on; but to make the point-> Statistics as we know it "works" (can be derived) under the assumptions of controlled experimental data. As a thought experiment think about the weather - we know that if we build a classifier that predicts the weather in my garden tomorrow based on the history of the weather in my garden it will do very badly. Why - well because weather is very very very complex; the range of behavior is vast. But worse, it's unstable. The weather in my garden is driven by several complex systems; the ocean, the atmosphere, the earth's orbit and sol! Statistics can't predict the future of the weather in my garden. Statistics also can't predict other things like the future of the financial markets (not least because if you find a statistical law about that they you will act on it and then screw it up) It's important to me to bang on about this because there are loads of people who sit through their introductory courses and read the example of predicting a biased roulette wheel. Years later they end up running the company/country/community that I live in and they have a view that they can use the same principles to do it... and this thinking leads to nasty surprises for me.
  - panarky 9y ago
    > if we build a classifier that predicts the weather in my garden tomorrow based on the history of the weather in my garden it will do very badly Give me hourly readings of temperature, wind speed, wind direction, precipitation, cloud cover and barometric pressure for the last 10 years and I can give you a very accurate prediction of tomorrow's weather in your garden.
    - calafrax 9y ago
      is that a joke? weather predictions are notoriously unreliable even though they are given with extreme granularity. that aside you are missing a larger point. if you predict the future based on past data all you are saying is "the future will be the same as the past." you aren't predicting anything. you will be wrong every single time something novel occurs, which is pretty frequently in the real world.
      - ryanwaggoner 9y ago
        The perception that weather forecasting is notoriously unreliable is mostly false: https://mobile.nytimes.com/2012/09/09/magazine/the-weatherman-is-not-a-moron.html?referer= https://mobile.nytimes.com/2012/09/09/magazine/the-weatherma...
        sgt101 9y ago
        From your link : "Why are weather forecasters succeeding when other predictors fail? It’s because long ago they came to accept the imperfections in their knowledge. That helped them understand that even the most sophisticated computers, combing through seemingly limitless data, are painfully ill equipped to predict something as dynamic as weather all by themselves. So as fields like economics began relying more on Big Data, meteorologists recognized that data on its own isn’t enough."
        gbrown 9y ago
        Quantifying uncertainty is one of the main points of statistics. Don't confuse the limitations of point estimates provided by machine learning techniques with all of statistical practice.

ben_w 9y ago

Black Swans are the error rate of your predictions (the real error rather than your prediction of your error rate) not existential proof that prediction is always doomed. After all, if Black Swans were common enough to make prediction a fool's errand most of the time, the bird of that name would never have led to the book of that name, because everyone would be predicting their failure to predict things.

sgt101 9y ago
I think that a Black Swan is when a new factor appears in your domain. In science we are conditioned from the start to create fair tests in controlled experiments. Control is the fundamental of experiment - and statistics are designed to handle experimental data. In the real world there are often no controls, and complex systems can be driven by an attractor for a very long time before one morning they are not, and every rule that you have is useless (often worse than useless). Sources of error are not equal; "Black Swan Error" is unusual in that over time it may be that this source is more important than any other source of data in your domain - the strange attractor that drove the creation of your classifier over the last 20 years may never recapture your function and if that's the case your classifier will be literally the most wrong thing you could have!
- ben_w 9y ago
  That's certainly one type of Black Swan. Taleb's example of that sort of thing being a Turkey predicting they will be fed (because that is what happened every other day of their life) but who is actually slaughtered. However, it is not the only type. There is also the stock market, which demonstrates major unpredictability every few years, but which can also be approximated the same way between each of the Black Swans. (And they keep being Black Swans because the gap between them is large enough for people to convince themselves that "This time it's different, this time n̵o̵b̵o̵d̵y̵ ̵w̵i̵l̵l̵ ̵h̵a̵v̵e̵ ̵t̵o̵ ̵b̵e̵ ̵n̵a̵i̵l̵e̵d̵ ̵t̵o̵ ̵a̵n̵y̵t̵h̵i̵n̵g̵ growth will be eternal!") Edit: Point is, it generalises as how wrong you are in your predictions, and the closer your estimate of your error rate is to your actual error rate, the better your model is.
taneq 9y ago

zEVE16Ug50tV 9y ago

> just because you've never encountered a black swan in the wild, doesn't mean they don't exist. Great point. We can't know that a machine learning algorithm used to make predictions won't be wrong if the future turns out to be significantly different from the past. A swan-classifier trained on images of white swans would fail hard if given pictures of black swans. That said, people find it useful to use machine learning algorithms to predict the future, as the future tends to be similar to the past, at least in the limited domains to which machine learning is currently applied. As compute increases and we learn how to write machine learning architectures[0], we don't need to distinguish as much between 'machine learning' and plain old 'learning' and much of what philosophers have thought over the years about the problem of induction, and relevant domains of induction, becomes relevant to the topic. [0] Or learn them. Jeff Dean mentions experimental success learning RNN architectures: https://www.youtube.com/watch?v=vzoe2G5g-w4 https://www.youtube.com/watch?v=vzoe2G5g-w4

pcmaffey 9y ago

Any relevant prediction model should account for the probability of black swans existence, even if it may have no idea what a black swan might look like.

TimPC 9y ago
This runs against the challenge that (almost?) all statistical methods train by fitting a model to some sort of data. If you have zero examples of a black swan in the data you can agree in principle they might exist but you'd expect a statistical model to get them wrong.