4 ms·

ML is there to maximize business income--nothing else. If ML was benefiting me, it would know that 90% of the time I fire up Hulu I plan to watch the next epis

ML is there to maximize business income--nothing else. If ML was benefiting me, it would know that 90% of the time I fire up Hulu I plan to watch the next episode of what I was watching last time. And it would make that a one click action. Instead I have to scroll past promotional garbage...every single time. Assholes.

buscoquadnary 4y ago
Honestly a lot of this ML to me seems eerily similar to how in older times people would use sheep entrails or crow droppings to try and predict the future. I mean basically that is what ML is, trying to predict the future, the difference is they called it magic, we call it math, but both seem to have about the same outcome, or understandability.
- treesprite82 4y ago
  > I mean basically that is what ML is, trying to predict the future If being so reductive, that's also the scientific method. Form a model on some existing data, with the goal of it being predictive on new unseen data. Key is in favoring the more predictive models. > they called it magic, we call it math, but both seem to have about the same outcome Find me some sheep entrails that can do this: https://imagen.research.google/ https://imagen.research.google/
  - ninkendo 4y ago
    One of the oft overlooked, yet critically important aspects of the scientific method is the hypothesis. You don’t design an experiment having absolutely no idea what to expect. You have an educated guess in mind (the hypothesis), and you design the experiment in such a way that says “this result will rule out my hypothesis, while this other result might confirm it.” Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent.
    - treesprite82 4y ago
      Note that I'm being intentionally reductive to argue that the net buscoquadnary threw around ML models and future-telling with sheep entrails also includes the scientific method. I do also think that ML as a field progresses through the scientific method ("I theorise that this network with residual connections will converge faster, lets see if there's a significant difference") - but maybe not to the full extent it could. > Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent. I'd say that's closer to evolutionary algorithms. GD finds (locally) the direction to tweak the weights to improve predictions on a given batch.
    - potatoman22 4y ago
      The trained model is the hypothesis. We test the hypothesis by validating the model against unseen data.
HWR_14 4y ago
I don't know why you assume the goal is "help aaronax watch what he wants quickly" vs "make sure when aaronax switches to his next series/movie it's on Hulu"
- mirrorlake 4y ago
  Customer satisfaction often translates into more dollars, though, because it means they won't cancel their service. I've had the same thought: if only this multi-billion dollar company could figure out that I want to continue watching the show I watched yesterday.
  - HWR_14 4y ago
    I would think it would be long-term satisfaction optimization. I'm not trying to optimize your binging of a single show (which you might watch then cancel after), I'm trying to get you to love enough of my product line to stick around.
- majormajor 4y ago
  Yeah, in these cases people try to balance for both "engagement" and "retention" (probably called something like "discovery" - the more they can show you about what other options they have, and the more shows you start watching, the less you quit after the first show you watch). The problem is that they don't have a way of collecting "annoying" metrics as easily as they do those two. So it's a big blind spot in terms of "should we tweak more in favor of one or the other."

buscoquadnary 4y ago

Honestly a lot of this ML to me seems eerily similar to how in older times people would use sheep entrails or crow droppings to try and predict the future. I mean basically that is what ML is, trying to predict the future, the difference is they called it magic, we call it math, but both seem to have about the same outcome, or understandability.

treesprite82 4y ago
> I mean basically that is what ML is, trying to predict the future If being so reductive, that's also the scientific method. Form a model on some existing data, with the goal of it being predictive on new unseen data. Key is in favoring the more predictive models. > they called it magic, we call it math, but both seem to have about the same outcome Find me some sheep entrails that can do this: https://imagen.research.google/ https://imagen.research.google/
- ninkendo 4y ago
  One of the oft overlooked, yet critically important aspects of the scientific method is the hypothesis. You don’t design an experiment having absolutely no idea what to expect. You have an educated guess in mind (the hypothesis), and you design the experiment in such a way that says “this result will rule out my hypothesis, while this other result might confirm it.” Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent.
  - treesprite82 4y ago
    Note that I'm being intentionally reductive to argue that the net buscoquadnary threw around ML models and future-telling with sheep entrails also includes the scientific method. I do also think that ML as a field progresses through the scientific method ("I theorise that this network with residual connections will converge faster, lets see if there's a significant difference") - but maybe not to the full extent it could. > Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent. I'd say that's closer to evolutionary algorithms. GD finds (locally) the direction to tweak the weights to improve predictions on a given batch.
  - potatoman22 4y ago
    The trained model is the hypothesis. We test the hypothesis by validating the model against unseen data.

HWR_14 4y ago

I don't know why you assume the goal is "help aaronax watch what he wants quickly" vs "make sure when aaronax switches to his next series/movie it's on Hulu"

mirrorlake 4y ago
Customer satisfaction often translates into more dollars, though, because it means they won't cancel their service. I've had the same thought: if only this multi-billion dollar company could figure out that I want to continue watching the show I watched yesterday.
- HWR_14 4y ago
  I would think it would be long-term satisfaction optimization. I'm not trying to optimize your binging of a single show (which you might watch then cancel after), I'm trying to get you to love enough of my product line to stick around.
majormajor 4y ago
Yeah, in these cases people try to balance for both "engagement" and "retention" (probably called something like "discovery" - the more they can show you about what other options they have, and the more shows you start watching, the less you quit after the first show you watch). The problem is that they don't have a way of collecting "annoying" metrics as easily as they do those two. So it's a big blind spot in terms of "should we tweak more in favor of one or the other."