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I still feel like much of AI is a plot to dumb down the modern economy. We want our business people to be just as effective as our quants; we want nothing to re

I still feel like much of AI is a plot to dumb down the modern economy. We want our business people to be just as effective as our quants; we want nothing to require real intellectual labor. The idea that you traditionally have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and all the more so because you are utterly dependent on them. ML breaks their control over the means of production. Now, that's not to say I am against labor saving devices. I most certainly am for them, but an an economy in which everyone is in a deep learning arms race is an irrational shit show that could only result in less productivity. (It's possible a single central planner AI could do better, because at least the training data would be "real world" and not output of other deep learning black box actors. But of course single-planner economies have a huge amount of other downsides.)

mlthoughts2018 6y ago
> “ The idea that you have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and this breaks their control over the means of production.” That is a very bizarre description of ML engineers. In every company I’ve worked at, ML is a team or teams that partners with product managers and other engineering teams to learn about problems they need solved. It’s very systematic, boring, and tied heavily to those other teams as the leaders and decision makers. First you look for high level value propositions, like automating a decision process, removing a customer friction point, creating key metrics where simple metrics are intractable, or various multi-modal information retrieval goals. You identify opportunities in these kinds of high level areas in lock-step with product managers and other engineering leaders. Then you move on to identify sources of data that can be leveraged, and eventually (much later) you get to the smaller set of work training a model, validating with acceptance tests and hardening the implementation for safe production deployment. If people are spouting “mumbo jumbo” and making big ML model decisions without lock-step synchronization with other stakeholders, that sounds like organizational dysfunction, not any type of issue with ML. I also find it odd that you bring up “cost[ing] a lot of money” .. that’s very out of place among everything else mentioned. That seems more like insecurity or jealousy over the market demand for ML talent, and wanting to cut other people down rather than work with them or acknowledge the level of effort it required to get that level of expertise in ML.
- Ericson2314 6y ago
  > That is a very bizarre description of ML engineers No no no, that's a description of regular programmers. There's so much more arcana in the field of programming and computer science as a whole, especially with the piss-poor job we've done deprecating bad old interfaces etc. (the monster that is modern Unix grows without bound). And of course there are the various langauge and other fads. All that is a nightmare for a traditional business person, whether they know it or not, and the regular programmers probably feel like an extortion racket of sorts.
funcDropShadow 6y ago
> Now, that's not to say I am against labor saving devices. That's good to know. Because "labor saving devices" is by no means the field of machine learning. A hammer is a "labor saving device" if all you have are rocks. I've got the impression that a lot of people conflate machine learning and robotics with labor saving in general. Of course compared to the state of art machine learning let's us hope to find magical shortcuts to get our work done. But an IDE, a word processor, or a compiler is also a labor saving devices. As well as a piece of paper, it is way faster to doodle on a piece of paper at your desk than finding the next cave to doodle.
- bendergarcia 6y ago
  Agreed I find that ml is a meta tool. You gotta Know the tool you need, then use ml to build it. Build the wrong tool and there’s little value. The right tool will make people more productive.
  - dumb1224 6y ago
    I always see that the data used in ML field are not being praised enough. I don't see enough people realising it was the large collection of data / curated information that enabled the advancement (may it be that the ML methods were heavily and manually tweaked to accomplish certain tasks). It simplify wasn't possible before the age of internet.
api 6y ago
Trying to use ML to get rid of programmers will just replace them with ML experts who also have to be programmers to implement the models and munge all the data. These people will in turn have to be paid more than the original programmers were.
- bidirectional 6y ago
  Won't they be much fewer in number, though?
  - Ericson2314 6y ago
    Yeah I think a very few but flashy situations, ML is worse results but even fewer programs, so worth it in some sense. The idea the non-programmers should be doing a little something is also very good. Just too the embodiment of that is the mess that is non-programmer Python.
- jjk166 6y ago
  The goal of ML isn't so much to get rid of programmers as to get rid of specialized programmers. Right now if you want to solve a problem, you need someone with a deep understanding of that specific problem to develop a solution. For a complex problem like diagnosing cancer patients, you are talking about a team of people with decades if not centuries of combined experience in oncology on top of the actual programming expertise to implement the tool. The holy grail of ML is to reach the point where someone who is an expert on making ML systems can apply the same (or substantially similar) tools and expertise to a wide range of problems - the same team that makes a cancer diagnosis system could also make a legal text search system or a protein folding system. Realistically we'll probably never get to the point where zero domain knowledge is required, but even if the requirement is just substantially reduced to the point where the ML expert can learn what they need in months instead of years, that would be revolutionary.
  - xpe 6y ago
    "The" goal of ML? No. A goal? No. ML is a field, not something with agency. One effect of ML is more generalization of prediction and inference problems. (I'm using inference in the statistical sense.)
gbolcer 6y ago
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.
wpietri 6y ago
This seems fair to me. The executive view of ML is "can you do me a magic?" And as this article's "Graduate Student Descent" bit makes clear, the worker response is often to semi-randomly perturb code, show some graphs, and say, "Is this a magic?" For me, most software development is about finding something boring and laborious. We get a computer to do the work so humans can level up and work on something requiring actual thought. That requires getting a deep understanding of the actual work. Some of that definitely happens in well-run ML projects. But there's a bunch of Silver Bullet Syndrome stuff going on, where ML's shiny results and magazine articles lead to inflated expectations and inflated claims of success. A fellow nerd says, "I did an algorithm!" Some turns that into an impressive presentation with claims of X% gains in the Key Business Metric, hallowed be its name. In reality, it's more plus or minus X% when you account for externalities, natural variation, and actor adaptation. But that's ok, because by the time anybody finds out, attention is elsewhere. That's not to really blame ML for that. For a period years ago, I kept getting asked, "Can we use a wiki for that?" I would start an explanation of what it actually takes to make a wiki work (hint: it's not the software). Their eyes would glaze over in short order, because they realized that it would take actual work. So many people want the silver bullet, the magic pill. Especially people in the managerial caste, as the reigning dogma there is that management is a universal skill. Details are for the little people.
- Ericson2314 6y ago
  > Especially people in the managerial caste, as the reigning dogma there is that management is a universal skill. Details are for the little people. Exactly. Machine learning is the perfect ideological duel. It's "universal labor" for "universal management", and both sides are equally illiterate in the ways of the world.
  - wpietri 6y ago
    Ooh, that's very well put.
- evjrob 6y ago
  This rings true from my experience as a data scientist for a non-tech compay. I spend more time doing expectation management than ML work. This seems to hold true whether we're trying to use ML on a new and novel problem statement, or trying to reproduce some published application or supposed solution a competitor claims to have.
EVa5I7bHFq9mnYK 6y ago
That reminds me how SQL was advertised - "Now you can program computers in plain English and fire all your programmers!". Fast forward, and today good luck getting a job as a Microsoft SQL programmer if you only know Oracle SQL.
- Ericson2314 6y ago
  Yes there is some of that, but at least SQL is rather readable and declarative, even if the syntax is way too irregular for the ideas. I blame less SQL false advertising, than the insane tunability and lack of effort into migrations tooling for making the relational database world so much more kafkaesque. Or really between the nature of Oracle, Microsoft, and their customers, it might have been an inevitable insanity along the lines of Conways law and too profitable -> too many cooks in the kitchen -> too complex.
visarga 6y ago
Wrong sub-field. Business insights are usually the domain of statistics, not deep learning. Deep learning is mostly for processing raw text, images, sounds and recommendations. It's dumb, boring stuff like translation, OCR, spam filtering and search ranking. How to run a business is the domain of game theory in economics, or maybe you could do something with Bayesian methods.
minimaxir 6y ago
As someone who works on open-source tooling for AI, good. The more people who can leverage AI, the better the industry will be as a whole. It serve as both a check on AI hype, and also leads innovating in encouraging new applications. Huggingface Transformers is a good demo of this philosophy in action.
xpe 6y ago
A plot? By whom? ML, depending on the situation may refer to a field of research, a methodology, a set of tools, among other things. Only things with agency 'plot'.

mlthoughts2018 6y ago

> “ The idea that you have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and this breaks their control over the means of production.” That is a very bizarre description of ML engineers. In every company I’ve worked at, ML is a team or teams that partners with product managers and other engineering teams to learn about problems they need solved. It’s very systematic, boring, and tied heavily to those other teams as the leaders and decision makers. First you look for high level value propositions, like automating a decision process, removing a customer friction point, creating key metrics where simple metrics are intractable, or various multi-modal information retrieval goals. You identify opportunities in these kinds of high level areas in lock-step with product managers and other engineering leaders. Then you move on to identify sources of data that can be leveraged, and eventually (much later) you get to the smaller set of work training a model, validating with acceptance tests and hardening the implementation for safe production deployment. If people are spouting “mumbo jumbo” and making big ML model decisions without lock-step synchronization with other stakeholders, that sounds like organizational dysfunction, not any type of issue with ML. I also find it odd that you bring up “cost[ing] a lot of money” .. that’s very out of place among everything else mentioned. That seems more like insecurity or jealousy over the market demand for ML talent, and wanting to cut other people down rather than work with them or acknowledge the level of effort it required to get that level of expertise in ML.

Ericson2314 6y ago
> That is a very bizarre description of ML engineers No no no, that's a description of regular programmers. There's so much more arcana in the field of programming and computer science as a whole, especially with the piss-poor job we've done deprecating bad old interfaces etc. (the monster that is modern Unix grows without bound). And of course there are the various langauge and other fads. All that is a nightmare for a traditional business person, whether they know it or not, and the regular programmers probably feel like an extortion racket of sorts.

funcDropShadow 6y ago

> Now, that's not to say I am against labor saving devices. That's good to know. Because "labor saving devices" is by no means the field of machine learning. A hammer is a "labor saving device" if all you have are rocks. I've got the impression that a lot of people conflate machine learning and robotics with labor saving in general. Of course compared to the state of art machine learning let's us hope to find magical shortcuts to get our work done. But an IDE, a word processor, or a compiler is also a labor saving devices. As well as a piece of paper, it is way faster to doodle on a piece of paper at your desk than finding the next cave to doodle.

bendergarcia 6y ago
Agreed I find that ml is a meta tool. You gotta Know the tool you need, then use ml to build it. Build the wrong tool and there’s little value. The right tool will make people more productive.
- dumb1224 6y ago
  I always see that the data used in ML field are not being praised enough. I don't see enough people realising it was the large collection of data / curated information that enabled the advancement (may it be that the ML methods were heavily and manually tweaked to accomplish certain tasks). It simplify wasn't possible before the age of internet.

api 6y ago

Trying to use ML to get rid of programmers will just replace them with ML experts who also have to be programmers to implement the models and munge all the data. These people will in turn have to be paid more than the original programmers were.

bidirectional 6y ago
Won't they be much fewer in number, though?
- Ericson2314 6y ago
  Yeah I think a very few but flashy situations, ML is worse results but even fewer programs, so worth it in some sense. The idea the non-programmers should be doing a little something is also very good. Just too the embodiment of that is the mess that is non-programmer Python.
jjk166 6y ago
The goal of ML isn't so much to get rid of programmers as to get rid of specialized programmers. Right now if you want to solve a problem, you need someone with a deep understanding of that specific problem to develop a solution. For a complex problem like diagnosing cancer patients, you are talking about a team of people with decades if not centuries of combined experience in oncology on top of the actual programming expertise to implement the tool. The holy grail of ML is to reach the point where someone who is an expert on making ML systems can apply the same (or substantially similar) tools and expertise to a wide range of problems - the same team that makes a cancer diagnosis system could also make a legal text search system or a protein folding system. Realistically we'll probably never get to the point where zero domain knowledge is required, but even if the requirement is just substantially reduced to the point where the ML expert can learn what they need in months instead of years, that would be revolutionary.
- xpe 6y ago
  "The" goal of ML? No. A goal? No. ML is a field, not something with agency. One effect of ML is more generalization of prediction and inference problems. (I'm using inference in the statistical sense.)

gbolcer 6y ago

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.

wpietri 6y ago

This seems fair to me. The executive view of ML is "can you do me a magic?" And as this article's "Graduate Student Descent" bit makes clear, the worker response is often to semi-randomly perturb code, show some graphs, and say, "Is this a magic?" For me, most software development is about finding something boring and laborious. We get a computer to do the work so humans can level up and work on something requiring actual thought. That requires getting a deep understanding of the actual work. Some of that definitely happens in well-run ML projects. But there's a bunch of Silver Bullet Syndrome stuff going on, where ML's shiny results and magazine articles lead to inflated expectations and inflated claims of success. A fellow nerd says, "I did an algorithm!" Some turns that into an impressive presentation with claims of X% gains in the Key Business Metric, hallowed be its name. In reality, it's more plus or minus X% when you account for externalities, natural variation, and actor adaptation. But that's ok, because by the time anybody finds out, attention is elsewhere. That's not to really blame ML for that. For a period years ago, I kept getting asked, "Can we use a wiki for that?" I would start an explanation of what it actually takes to make a wiki work (hint: it's not the software). Their eyes would glaze over in short order, because they realized that it would take actual work. So many people want the silver bullet, the magic pill. Especially people in the managerial caste, as the reigning dogma there is that management is a universal skill. Details are for the little people.

Ericson2314 6y ago
> Especially people in the managerial caste, as the reigning dogma there is that management is a universal skill. Details are for the little people. Exactly. Machine learning is the perfect ideological duel. It's "universal labor" for "universal management", and both sides are equally illiterate in the ways of the world.
- wpietri 6y ago
  Ooh, that's very well put.
evjrob 6y ago
This rings true from my experience as a data scientist for a non-tech compay. I spend more time doing expectation management than ML work. This seems to hold true whether we're trying to use ML on a new and novel problem statement, or trying to reproduce some published application or supposed solution a competitor claims to have.

EVa5I7bHFq9mnYK 6y ago

That reminds me how SQL was advertised - "Now you can program computers in plain English and fire all your programmers!". Fast forward, and today good luck getting a job as a Microsoft SQL programmer if you only know Oracle SQL.

Ericson2314 6y ago
Yes there is some of that, but at least SQL is rather readable and declarative, even if the syntax is way too irregular for the ideas. I blame less SQL false advertising, than the insane tunability and lack of effort into migrations tooling for making the relational database world so much more kafkaesque. Or really between the nature of Oracle, Microsoft, and their customers, it might have been an inevitable insanity along the lines of Conways law and too profitable -> too many cooks in the kitchen -> too complex.

visarga 6y ago

Wrong sub-field. Business insights are usually the domain of statistics, not deep learning. Deep learning is mostly for processing raw text, images, sounds and recommendations. It's dumb, boring stuff like translation, OCR, spam filtering and search ranking. How to run a business is the domain of game theory in economics, or maybe you could do something with Bayesian methods.

minimaxir 6y ago

As someone who works on open-source tooling for AI, good. The more people who can leverage AI, the better the industry will be as a whole. It serve as both a check on AI hype, and also leads innovating in encouraging new applications. Huggingface Transformers is a good demo of this philosophy in action.

xpe 6y ago

A plot? By whom? ML, depending on the situation may refer to a field of research, a methodology, a set of tools, among other things. Only things with agency 'plot'.