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> The goal was to create an algorithm that could predict how quickly a pet would be adopted based on its profile details, from its photo to its breed, sex, size

> The goal was to create an algorithm that could predict how quickly a pet would be adopted based on its profile details, from its photo to its breed, sex, size, age, and whether it had been vaccinated or not. > These predictions would be used to optimize and tweak future critters' profiles so that they are adopted as soon as possible. Sorry but, how is this useful? You can't just change the age of an animal to make it more likely to be adopted. The profile is meant to be an accurate representation of the animal so people know what they're getting. What exactly was the algorithm meant to achieve aside from being a predictor?

cjauvin 7y ago
Exactly my thought when I read this also, and I think it generalizes to a lot of cases where the value of trying to predict something is dubious at best, when there's nothing obvious you can do with the prediction itself.
- Thorentis 7y ago
  Sadly, many ML comps involve exactly this. Making a predictor for something, where the prediction has no real world value, and even dubious value at a thought experiment level. I think one of the biggest skills to have in the ML space, is knowing what is worth training a model to know, and what isn't. Just like in engineering, the most successful products are those that solve a real world problem, no matter how elegantly the others might have been made.
mxcrossb 7y ago
Well optimizing the photo seems a sensible task for AI.
singron 7y ago
You can use this to select which pets to put on a platform. For instance, no-kill shelters have to decide which animals they intake since they have finite room. They can save more animals if they pick animals that are likely to be adopted quickly. Obviously, kill shelters have a similar calculus when deciding which animals to cull (and indeed, animals that don't fit in the no-kill shelter go to the kill shelter). I'm not sure how this website manages "inventory", but they might have similar problems.
- endorphone 7y ago
  I'm pretty sure so-called no-kill shelters don't outsource their killing by simply refusing less adoptable animals. And if this contest were advertised as "help us decide which animals to kill first" it probably wouldn't gain traction. This contest sounds ridiculous. It sounds like an attempt to get in on that AI gravy but do so with some sort of feel-good element. Only there is no feel good to it, and the basic premise seems outlandish.
  - hcknwscommenter 7y ago
    I mean, isn't this "outsourcing the killing" a given? No-kill shelters simply don't have infinite resources. Some animals are simply not adoptable.
  - kaikai 7y ago
    > I'm pretty sure so-called no-kill shelters don't outsource their killing by simply refusing less adoptable animals. They do, though. That's how they are able to limit the number of animals they have at any given time.
    - endorphone 7y ago
      They limit the number of animals they have at any given time by not taking in more when they are at capacity. This is very different from running a DNN model on every applicant and refusing to intake those that aren't adoptable enough, which is a preposterous concept. And just to provide the full picture, most no-kill shelters of course have scenarios where they euthanize -- violent animals, sick animals, etc -- but they don't need a neural network to accomplish this. This is all neither here nor there, as the contest had positively nothing to do with any of this. Instead they wanted to determine the most adoptable traits so they could adjust the less adoptable traits with the more adoptable traits: The poodle goes through the hair straightener and gets a blonde hair color treatment (clearly I am being satirical) to make it more like a lab, for instance.
mirimir 7y ago
Unless it's a totally no-kill shelter, the results would presumably help determine which animals to euthanize.
rasz 7y ago
The purpose was eugenics, on animals this time around.
Normal_gaussian 7y ago
Its actually very useful. A limited number of parameters can be genuinely altered - a better photo can be taken, and vaccinations can be administered for example. Animal rehoming centres have to balance throughput with cost; reduced per pet costs mean that they are able to expand or support more complex cases. Whilst keeping a pet in a "space" and feeding it does cost, this cost can easily be significantly less than vaccinating, particularly as some vaccines require a few days hold post vaccine. Similarly if the vaccine will not alter a pets rehoming chance, then it is an unnecessary cost. Pictures may be more easily applied as a tighter feedback loop (of the 5, use the 3rd) however they may also indicate other issues that could be addressed (over / underweight, coat damage, etc.) and addressing those issues have costs to balance and predict.
- pbhjpbhj 7y ago
  This sounds like it would generalise to healthcare decisions.
tgv 7y ago
I have my doubts too, since there are quite likely ceiling effects. Perhaps the competition was set up out of desperation, or because the non-profit still had 25k to burn who knows? But I would be really interested to see if it really has an effect, and if that effect can be sustained.
- cowsandmilk 7y ago
  > because the non-profit still had 25k to burn who knows? I think this is the closest to the truth, but that Google spent the money. Google wants to show usefulness of ML. Marketing person comes up with competition and backs it. Pet adoption org just has to provide some data and says why not? At the very least, it is free publicity for minimal effort.

cjauvin 7y ago

Exactly my thought when I read this also, and I think it generalizes to a lot of cases where the value of trying to predict something is dubious at best, when there's nothing obvious you can do with the prediction itself.

Thorentis 7y ago
Sadly, many ML comps involve exactly this. Making a predictor for something, where the prediction has no real world value, and even dubious value at a thought experiment level. I think one of the biggest skills to have in the ML space, is knowing what is worth training a model to know, and what isn't. Just like in engineering, the most successful products are those that solve a real world problem, no matter how elegantly the others might have been made.

mxcrossb 7y ago

Well optimizing the photo seems a sensible task for AI.

singron 7y ago

You can use this to select which pets to put on a platform. For instance, no-kill shelters have to decide which animals they intake since they have finite room. They can save more animals if they pick animals that are likely to be adopted quickly. Obviously, kill shelters have a similar calculus when deciding which animals to cull (and indeed, animals that don't fit in the no-kill shelter go to the kill shelter). I'm not sure how this website manages "inventory", but they might have similar problems.

endorphone 7y ago
I'm pretty sure so-called no-kill shelters don't outsource their killing by simply refusing less adoptable animals. And if this contest were advertised as "help us decide which animals to kill first" it probably wouldn't gain traction. This contest sounds ridiculous. It sounds like an attempt to get in on that AI gravy but do so with some sort of feel-good element. Only there is no feel good to it, and the basic premise seems outlandish.
- hcknwscommenter 7y ago
  I mean, isn't this "outsourcing the killing" a given? No-kill shelters simply don't have infinite resources. Some animals are simply not adoptable.
- kaikai 7y ago
  > I'm pretty sure so-called no-kill shelters don't outsource their killing by simply refusing less adoptable animals. They do, though. That's how they are able to limit the number of animals they have at any given time.
  - endorphone 7y ago
    They limit the number of animals they have at any given time by not taking in more when they are at capacity. This is very different from running a DNN model on every applicant and refusing to intake those that aren't adoptable enough, which is a preposterous concept. And just to provide the full picture, most no-kill shelters of course have scenarios where they euthanize -- violent animals, sick animals, etc -- but they don't need a neural network to accomplish this. This is all neither here nor there, as the contest had positively nothing to do with any of this. Instead they wanted to determine the most adoptable traits so they could adjust the less adoptable traits with the more adoptable traits: The poodle goes through the hair straightener and gets a blonde hair color treatment (clearly I am being satirical) to make it more like a lab, for instance.

mirimir 7y ago

Unless it's a totally no-kill shelter, the results would presumably help determine which animals to euthanize.

rasz 7y ago

The purpose was eugenics, on animals this time around.

Normal_gaussian 7y ago

Its actually very useful. A limited number of parameters can be genuinely altered - a better photo can be taken, and vaccinations can be administered for example. Animal rehoming centres have to balance throughput with cost; reduced per pet costs mean that they are able to expand or support more complex cases. Whilst keeping a pet in a "space" and feeding it does cost, this cost can easily be significantly less than vaccinating, particularly as some vaccines require a few days hold post vaccine. Similarly if the vaccine will not alter a pets rehoming chance, then it is an unnecessary cost. Pictures may be more easily applied as a tighter feedback loop (of the 5, use the 3rd) however they may also indicate other issues that could be addressed (over / underweight, coat damage, etc.) and addressing those issues have costs to balance and predict.

pbhjpbhj 7y ago
This sounds like it would generalise to healthcare decisions.

tgv 7y ago

I have my doubts too, since there are quite likely ceiling effects. Perhaps the competition was set up out of desperation, or because the non-profit still had 25k to burn who knows? But I would be really interested to see if it really has an effect, and if that effect can be sustained.

cowsandmilk 7y ago
> because the non-profit still had 25k to burn who knows? I think this is the closest to the truth, but that Google spent the money. Google wants to show usefulness of ML. Marketing person comes up with competition and backs it. Pet adoption org just has to provide some data and says why not? At the very least, it is free publicity for minimal effort.