Is BigData/MachineLearning/DeepLearning really the future?

[atyy]

He was just arguing that you should be wary of the hype, not that it's all useless (at least that's how I interpreted it).

There are lots of absurd pronouncements made by fan boys of the subject, as is the won't of fan boys of anything. The CEO (or some other higher up) of kaggle in an interview with Slate suggested that expert knowledge is actually a detriment, and that most problems will be solved using data science approaches, for instance (which is partially true but mostly delusional). Less obnoxiously, I've had numerous encounters with famous ML researchers where bullish proclamations about the application of ML to field X will have Y result, only to later see the media uncritically propagate them. Could novel ML techniques be useful in control engineering? In principle, yes. In practice, every control engineer I know finds the idea amusing at best. Can you do electronic structure calculations with neural nets and will this be the end of all other computational chemistry methods? Probably not. Etc etc.

And in the hype phase, fan boys breed prodigiously. Just think critically.

I agree in general, but regarding machine learning and control engineering, how about things like http://video.mit.edu/watch/meet-2011-tr35-winner-pieter-abbeel-4/ ?http://video.mit.edu/watch/meet-2011-tr35-winner-pieter-abbeel-4/

 

[MarneMath]

He was just arguing that you should be wary of the hype, not that it's all useless (at least that's how I interpreted it).

There are lots of absurd pronouncements made by fan boys of the subject, as is the won't of fan boys of anything. The CEO (or some other higher up) of kaggle in an interview with Slate suggested that expert knowledge is actually a detriment, and that most problems will be solved using data science approaches, for instance (which is partially true but mostly delusional). Less obnoxiously, I've had numerous encounters with famous ML researchers where bullish proclamations about the application of ML to field X will have Y result, only to later see the media uncritically propagate them. Could novel ML techniques be useful in control engineering? In principle, yes. In practice, every control engineer I know finds the idea amusing at best. Can you do electronic structure calculations with neural nets and will this be the end of all other computational chemistry methods? Probably not. Etc etc.

And in the hype phase, fan boys breed prodigiously. Just think critically.

I have two points of contention with your post. I think there's a fundamental difference between saying be skeptical of entire fields of study and their application versus understand the limitations of those entire fields of study and their applications. The former implies that there are fundamental problems with the field that one should be weary of, while the latter implies that the being aware of the short comings of tools is a good thing.

My second disagreement is using Jeremy Howard, who happens to be someone I know, as your reference for a fan boy. First off, he is not the CEO of Kaggle, nor was he back in 2012 when he gave his statements. He was the President and Chief Senior Data Scientist for Kaggle. His job is literally to get companies to invest time and money into using Kaggle. What do you expect him to say, "Yes sometimes Kaggle isn't the right tool and we can't solve all your problems?" He's immensely supportive of the accomplishments that "amateur" free lance data scientist have accomplished for companies, and he does truly believe that subject matter expertise can hamper insights. However, in my experience his feelings are outside of the norm in the real world, after all Jeremy is a bit of an outside of the norm type of guy.

I find the rhetoric of data science can solve everything, or expertise doesn't matter tends to exist in a vacuum in the internet. It's easy to find people who say this online but it's hard to find such people who say it without some agenda such as pushing their new BI products or selling their expertise. In reality, I haven't encountered many speakers, or working data scientist who hold those views. In fact, the most successful projects I've seen were accomplished through close collaboration between engineers, data scientist, and technicians. The constant feed back loop of question -> understanding -> more questions -> insight -> question can only really be accomplished by cross functional and diverse teams.

 

[Crass_Oscillator]

@ atyy

Looks neat. I will say that what I do is not related to control engineering. It doesn't bother me that people are considering applying ML to control engineering applications or other areas. What does bother me is when the application is dubious or absurd claims about it's potential successes are put forth; it's not a complaint about the field in particular, it's a complaint about the sociology that I've encountered.

I also dislike the token papers published where somebody had the bright idea of taking a flavor of the month algorithm and applied it to their problem without apparent rational justification.

@ MarneMath

Well I suppose I was a bit harsh there. I will say that I don't consider "he's selling his products, thus he must make strong remarks" to be a valid defense. If you're going to send a strong opinion into the public forum, it should be interpreted as your honest opinion, and will be.

It may be an experience issue. More bullish individuals I've encountered tend to be younger, but not always.

 

[MarneMath]

http://www.rogerschank.com/fraudulent-claims-made-by-IBM-about-Watson-and-AI

Roger Schank views seem to relate to this thread.