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It may not be ideal for fundamental research but demand for ML practitioners is only likely to increase over time. ML is pure alchemy for a business operating

It may not be ideal for fundamental research but demand for ML practitioners is only likely to increase over time. ML is pure alchemy for a business operating at scale. It’s as if a coal plant could turn its waste emissions into profit. You have this “data exhaust” from all the activity happening within your system that can be used to optimize your system Atleast a few percentage points beyond what is other wise possible. A team of 5 ML engineers can improve an ad targeting system by 5 % and if the company is google that’s billions. ML creates feedback loops of improvements in product that improve usage that lead to more data which further strengthens the moat a business has. It totally makes sense to jump on this train. It won’t solve AI but will make a lot of people wealthy optimizing systems.

YeGoblynQueenne 7y ago
>> It may not be ideal for fundamental research but demand for ML practitioners is only likely to increase over time. If research stagnates, application willl also stagnate. For the industry money to keep pouring in there has to be growth and for there to be growth there has to be progress- scientific progress. So if something is "not ideal for fundamental research" it is also "not ideal" for business.
heavenlyblue 7y ago
>> A team of 5 ML engineers can improve an ad targeting system by 5 % and if the company is google that’s billions. Not going to pick up on your arbitrary 5% constant here, but please elaborate how these ML engineers are any different from anyone with a quantitative background?
- bumby 7y ago
  I think that's the key here. It's not ML magic that's driving success, it data literacy. Steve Levitt (of Freakanomics fame) said his advice to students would be to ensure they have base knowledge in understanding data analytics regardless of their domain. ML is just the sexy subset that gets all the attention
  - ghaff 7y ago
    And ML is a tiny part of the whole data science toolchain and process. Getting the data to the point where you can use it for something interesting is probably the hardest part.
    - thewarrior 7y ago
      My point was that cutting edge ML can add a bit more on top of what data literacy can achieve. At scale that’s worth a lot.
- thewarrior 7y ago
  What I meant was for eg applying deep neural networks on large data sets for eg can give you a few percentage points improvements on systems implemented without them. At scale it makes a big difference.
aetherson 7y ago
I think that there are a pretty large number of companies that put out "data exhaust" as you put it that could increase their efficiency by 5% or so, sure. Not every company, but some. It's very unclear to me that there are a large number of companies that can increase their efficiency by much more than 5% through ML. And it's not clear to me that there is more than a couple-year's-worth of projects in turning data exhaust into money for each company. So if I were a new grad looking to get into ML, that might somewhat concern me.
itronitron 7y ago
not everyone wants, or needs, an ad targeting system

YeGoblynQueenne 7y ago

>> It may not be ideal for fundamental research but demand for ML practitioners is only likely to increase over time. If research stagnates, application willl also stagnate. For the industry money to keep pouring in there has to be growth and for there to be growth there has to be progress- scientific progress. So if something is "not ideal for fundamental research" it is also "not ideal" for business.

heavenlyblue 7y ago

>> A team of 5 ML engineers can improve an ad targeting system by 5 % and if the company is google that’s billions. Not going to pick up on your arbitrary 5% constant here, but please elaborate how these ML engineers are any different from anyone with a quantitative background?

bumby 7y ago
I think that's the key here. It's not ML magic that's driving success, it data literacy. Steve Levitt (of Freakanomics fame) said his advice to students would be to ensure they have base knowledge in understanding data analytics regardless of their domain. ML is just the sexy subset that gets all the attention
- ghaff 7y ago
  And ML is a tiny part of the whole data science toolchain and process. Getting the data to the point where you can use it for something interesting is probably the hardest part.
  - thewarrior 7y ago
    My point was that cutting edge ML can add a bit more on top of what data literacy can achieve. At scale that’s worth a lot.
thewarrior 7y ago
What I meant was for eg applying deep neural networks on large data sets for eg can give you a few percentage points improvements on systems implemented without them. At scale it makes a big difference.

aetherson 7y ago

I think that there are a pretty large number of companies that put out "data exhaust" as you put it that could increase their efficiency by 5% or so, sure. Not every company, but some. It's very unclear to me that there are a large number of companies that can increase their efficiency by much more than 5% through ML. And it's not clear to me that there is more than a couple-year's-worth of projects in turning data exhaust into money for each company. So if I were a new grad looking to get into ML, that might somewhat concern me.

itronitron 7y ago

not everyone wants, or needs, an ad targeting system