Woah, talk about a misleading title.
Analyzing electric utility data using machine learning
11–20 of 27 posts
Re: Analyzing electric utility data using machine learning
#12I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
I have to add there is zero reverse-engineering here. The main purpose of this article seems to be name-dropping for the 500M company while trying to generate some 'thoughtleader'-cred
Yep, "reverse engineering" used to mean something. Nowadays the term is misused just like "hacker".
Re: Analyzing electric utility data using machine learning
#13I really don't like the arrogance coming through the writing style, especially given how clear it is the author has at best basic working knowledge of ML. Never mind the fact that k-means is ML 101 and the $500M company is likely using more sophisticated ones, the fact that he says the following tells me he's just reading tutorials and plugging data into libraries (which is fine but not with this tone of know-it-all…
To be fair, he did say that he was just reporting the final outcome. It's unclear exactly what was involved in his trial and error approach or how he judged "significant clusters".
Re: Analyzing electric utility data using machine learning
#14(Disclosure: former Opower engineer)
Opower is most certainly not an 'Artificial Intelligence company'. Opower's foundations are in Behavioral Science, literally applying the results of an experiment performed by Robert Cialdini to electrical utilities all over the world. (http://www.slate.com/articles/technology/the_efficient_plane...). If being a 'Big Data company' is a thing, that would better describe Opower - they ingested massive amounts of utility customer usage data, and from that were able to find similar house holds, rank them, and produce a behavioral effect that worked. It's an impressive feat but not AI.
Also, I really hope "I recreated this AI company in a weekend" isn't the new "I recreated Twitter in a weekend." No, you didn't.
Re: Analyzing electric utility data using machine learning
#15Posting an outrageous title to get attention, thereby triggering a discussion entirely focused on the outrageous title is such an anti-pattern that we've demoted this post.
Normally we can find representative language in the body of an article to serve as a substantive title, but I tried and came up empty in this case. That can't be a good sign.
Re: Analyzing electric utility data using machine learning
#16Posting an outrageous title to get attention, thereby triggering a discussion entirely focused on the outrageous title is such an anti-pattern that we've demoted this post. Normally we can find representative language in the body of an article to serve as a substantive title, but I tried and came up empty in this case. That can't be a good sign.
Analyzing electric load profile data using machine learning?
Re: Analyzing electric utility data using machine learning
#17I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
As someone who does ML for a living, couldn't agree more. Usually your initial research only constitutes a tiny portion of your spent resources, it's putting it in production that makes you a 500 mil company. As always the devil is in the details, and it's unreal how much can go wrong once you actually have to ACT using the model and not just theorize. Sounds like someone hasn't left academia yet...
Re: Analyzing electric utility data using machine learning
#18Posting an outrageous title to get attention, thereby triggering a discussion entirely focused on the outrageous title is such an anti-pattern that we've demoted this post. Normally we can find representative language in the body of an article to serve as a substantive title, but I tried and came up empty in this case. That can't be a good sign.
That's a shame, because it is otherwise an interesting and informative article for entry-level ML. It's just really, really poorly framed.
Re: Analyzing electric utility data using machine learning
#19Posting an outrageous title to get attention, thereby triggering a discussion entirely focused on the outrageous title is such an anti-pattern that we've demoted this post. Normally we can find representative language in the body of an article to serve as a substantive title, but I tried and came up empty in this case. That can't be a good sign.
Unfortunately, this type of headline is less clickbaity than other headlines I've seen recently relating to Data Science/Machine Learning, even on Hacker News. (although in this case, the clickbaityness is more deliberate)
It is honestly one of the reasons I am cutting down on producing blog articles on those topics, because I can't compete with clickbait-articles-which-peddle-machine-learning-as-magic-when-it-is-not, and it is beginning to get frustrating.
Re: Analyzing electric utility data using machine learning
#20Posting an outrageous title to get attention, thereby triggering a discussion entirely focused on the outrageous title is such an anti-pattern that we've demoted this post. Normally we can find representative language in the body of an article to serve as a substantive title, but I tried and came up empty in this case. That can't be a good sign.
Analyzing electric load profile data using machine learning?
Ok, we'll use that (slightly tweaked). Thanks!