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It's just another tool. I have this saying: all machine learning is just clever tricks and techniques until someone learns how to brute force the solution. It

It's just another tool. I have this saying: all machine learning is just clever tricks and techniques until someone learns how to brute force the solution. It's the same adoption/business technology tension that has existed since Frederick Taylor in the early 1900s or Vonnegut's Player Piano concept where they propose taking a recorder to automated human-adverse tasks by recording their movements. The hype is trying to replace people. Real-world adoption seems to take place where machine learning complements human activity to do things humans are not good at, not replaces it. It's not making them dumber, it's making them more enabled. [reference] In the early 1900’s Frederick Taylor called public attention to the problem of ‘national efficiency’ by proposing to eliminate ‘rule of thumb’ management techniques and replacing them with the principle of scientific management [157]. Scientific management reasons about the motions and activities of workers and states that wasted work can be eliminated through careful planning and optimization by managers. While Taylor was concerned about the inputs and outputs of manufacturing and material processes, computers and the information age brought about parallel concepts in the management and organization of information and its processes. Work efficiency could now be measured not by bricks or steel, but by their information flows. F. W. Taylor, “Principles of Scientific Management”, Harper & Row, New York, NY, 1911.

Ericson2314 6y ago
Not all tools are alike. Taylorism in particular was based around, yes, a bunch of empiracism, but also trying to understand the production process in greater detail. The modern ML seems more like the modern MBAism where the details are considered beneath management. That would have been antithetical to Taylor, who was all about how the sausage is made.
xpe 6y ago
> I have this saying: all machine learning is just clever tricks ... It appears to me that you are missing the mark here, unless this is largely a definitional issue. Do you consider the foundations of ML to be a clever trick? Do you think human brains primarily learn by clever tricks? When a metaphor or saying falls apart with one more level of questioning, I would suggest it may be time to find a better metaphor.
- gbolcer 6y ago
  To your first question, yes it's just a style of solving a problem that can be implemented in software for 90%+ of the time. For your second, I think that's part of the problem. Most people confuse how a human brain learns with what is actually running in a machine learning program. It's similar at some level, but not really doing the same thing at another.
  - gbolcer 6y ago
    BTW, Google Brain posted this on the subject of "brute force" solutions yesterday. I thought it would be of interest here. The big idea here is that enough brute force will lead to better compute-use techniques which will in turn make it possible to do more with less compute. But the current reality is that these systems don’t tend to justify their existence when compared to greener, more useful technologies. It’s hard to pitch an AI system that can only be operated by trillion-dollar tech companies willing to ignore the massive carbon footprint a system this big creates.
  - xpe 6y ago
    > To your first question, yes it's just a style of solving a problem that can be implemented in software for 90%+ of the time. What insight is gained by this statement? What does it explain? What does it downplay? I'm not currently seeing much value in it. I'll explain why. Saying 'just a method of solving a problem' comes across as reductive without being useful. Imagine if someone said 'flying is just a means of movement' in the context of studying a hummingbird's agility. It says more about the speaker than the subject. It suggests the person is uninterested or focused on other things. So with regards to your statement, it suggests you don't care and/or don't appreciate what makes learning difficult. I'm talking about learning theory. About generalization given data. This is certainly not easy. Yes, it can be encoded in an algorithm, but that does not make it less interesting.

Ericson2314 6y ago

Not all tools are alike. Taylorism in particular was based around, yes, a bunch of empiracism, but also trying to understand the production process in greater detail. The modern ML seems more like the modern MBAism where the details are considered beneath management. That would have been antithetical to Taylor, who was all about how the sausage is made.

xpe 6y ago

> I have this saying: all machine learning is just clever tricks ... It appears to me that you are missing the mark here, unless this is largely a definitional issue. Do you consider the foundations of ML to be a clever trick? Do you think human brains primarily learn by clever tricks? When a metaphor or saying falls apart with one more level of questioning, I would suggest it may be time to find a better metaphor.

gbolcer 6y ago
To your first question, yes it's just a style of solving a problem that can be implemented in software for 90%+ of the time. For your second, I think that's part of the problem. Most people confuse how a human brain learns with what is actually running in a machine learning program. It's similar at some level, but not really doing the same thing at another.
- gbolcer 6y ago
  BTW, Google Brain posted this on the subject of "brute force" solutions yesterday. I thought it would be of interest here. The big idea here is that enough brute force will lead to better compute-use techniques which will in turn make it possible to do more with less compute. But the current reality is that these systems don’t tend to justify their existence when compared to greener, more useful technologies. It’s hard to pitch an AI system that can only be operated by trillion-dollar tech companies willing to ignore the massive carbon footprint a system this big creates.
- xpe 6y ago
  > To your first question, yes it's just a style of solving a problem that can be implemented in software for 90%+ of the time. What insight is gained by this statement? What does it explain? What does it downplay? I'm not currently seeing much value in it. I'll explain why. Saying 'just a method of solving a problem' comes across as reductive without being useful. Imagine if someone said 'flying is just a means of movement' in the context of studying a hummingbird's agility. It says more about the speaker than the subject. It suggests the person is uninterested or focused on other things. So with regards to your statement, it suggests you don't care and/or don't appreciate what makes learning difficult. I'm talking about learning theory. About generalization given data. This is certainly not easy. Yes, it can be encoded in an algorithm, but that does not make it less interesting.