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Saying it uses Machine learning is a bit sensationalized, this seems to just give you static guidelines. They may arrived at the guidelines using ML, but it's

Saying it uses Machine learning is a bit sensationalized, this seems to just give you static guidelines. They may arrived at the guidelines using ML, but it's possible that their guidelines wouldn't be right for the types of emails you are sending out.

cpplinuxdude 10y ago
Doesn't Boomerang require you to grant their servers the right to read all your email? Seems like a high price to pay.
- leaningtower 10y ago
  +1
- camkego 10y ago
  The real question is, are they then using your email to train their models? Is your email being downloaded to their servers?
mrhektor 10y ago
While the tool is definitely useful, I agree with your comment. Too often, tech that is quite simple is misrepresented as AI because it makes for better headlines / a larger investment round in the case of startups
thomasahle 10y ago
It also measures the politeness, positivity and subjectivity of your mail. I'd say that's still a work in progress in machine learning.
deleted 10y ago
[deleted]
levbrie 10y ago
> They may arrived at the guidelines using ML, but it's possible that their guidelines wouldn't be right for the types of emails you are sending out. This is a great point, and it's something that users ought to consider with nearly every application of machine learning that ends with a definite recommendation to the user. Machine learning can be used to solve many many different types of problems - when it comes to solving problems related to human interaction, the insights that it has will tend to function more like the rules for running an effective business-focused popularity contest than the rules for crafting meaningful emails to every possible audience. That said, if you happen to be sending a business email and want nothing more than to improve the likelihood of response, this seems like a great tool for the job.
- baydinalex 10y ago
  Sort of true, in the sense that the product isn't separately trained for sales emails vs personal emails vs internal business emails, etc. But the calculations we chose don't provide a lot of constraints, and the variances were not as high as you'd likely expect. So I'd be comfortable saying that the recommendations generalize well to a vast majority of situations.
baydinalex 10y ago
Declaring that somebody is falsely claiming to use ML without looking at their product is a bit irresponsible ;) The positivity, subjectivity, and politeness calculations are all outputs from neural networks, and the overall likelihood is calculated using a decision forest so that we can explain the results to people. There are plenty of emails with a high likelihood of getting a response, even though each calculation may score poorly.

cpplinuxdude 10y ago

Doesn't Boomerang require you to grant their servers the right to read all your email? Seems like a high price to pay.

leaningtower 10y ago
+1
camkego 10y ago
The real question is, are they then using your email to train their models? Is your email being downloaded to their servers?

mrhektor 10y ago

While the tool is definitely useful, I agree with your comment. Too often, tech that is quite simple is misrepresented as AI because it makes for better headlines / a larger investment round in the case of startups

thomasahle 10y ago

It also measures the politeness, positivity and subjectivity of your mail. I'd say that's still a work in progress in machine learning.

deleted 10y ago

[deleted]

levbrie 10y ago

> They may arrived at the guidelines using ML, but it's possible that their guidelines wouldn't be right for the types of emails you are sending out. This is a great point, and it's something that users ought to consider with nearly every application of machine learning that ends with a definite recommendation to the user. Machine learning can be used to solve many many different types of problems - when it comes to solving problems related to human interaction, the insights that it has will tend to function more like the rules for running an effective business-focused popularity contest than the rules for crafting meaningful emails to every possible audience. That said, if you happen to be sending a business email and want nothing more than to improve the likelihood of response, this seems like a great tool for the job.

baydinalex 10y ago
Sort of true, in the sense that the product isn't separately trained for sales emails vs personal emails vs internal business emails, etc. But the calculations we chose don't provide a lot of constraints, and the variances were not as high as you'd likely expect. So I'd be comfortable saying that the recommendations generalize well to a vast majority of situations.

baydinalex 10y ago

Declaring that somebody is falsely claiming to use ML without looking at their product is a bit irresponsible ;) The positivity, subjectivity, and politeness calculations are all outputs from neural networks, and the overall likelihood is calculated using a decision forest so that we can explain the results to people. There are plenty of emails with a high likelihood of getting a response, even though each calculation may score poorly.