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.
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.
This would be funny if it were not also so true and sad... management as a skill (and it is a skill, it is not IMHO something that can be taught, especially in business schools!) is such a rarity.
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.
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.
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.
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.
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.
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.
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.
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.
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.)
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.
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.
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.)