
Analyzing electric utility data using machine learning - asfldjwneqeda
https://blog.ai-academy.com/i-reverse-engineered-a-500m-artificial-intelligence-company-in-one-week-heres-the-full-story-d067cef99e1c
======
srainier
(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...](http://www.slate.com/articles/technology/the_efficient_planet/2013/03/opower_using_smiley_faces_and_peer_pressure_to_save_the_planet.html)).
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.

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taneq
I 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.

~~~
bullsandabears
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

~~~
passiveincomelg
Yep, "reverse engineering" used to mean something. Nowadays the term is
misused just like "hacker".

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simonrobb
It's a great effort here and I appreciate the post, but I think the title is a
little misleading. There's a whole lot more to a company, let alone a $500MM
company, than its tech/product. It's like saying you reverse engineered a
kettle and now you're Breville. Consider team, brand, adoption, supporting
infrastructure/marketing channels/processes/etc etc etc.

I apologise if this is nitpicky, but I think a more accurate description would
be "I reverse-engineered a $500M AI company's algorithm in one week".

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markovbling
I 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 writing):

"I played with the number of clusters, and the one that allowed me to get the
most significant clusters was 6 (this was a trial and error approach, for
brevity I’ll report just the final outcome)."

Anyone who has studied clustering knows you would at the very least do a scree
plot here. You can defer to intuition but there's more to it than running
kmeans and claiming you've reverse-engineered a $500M company.

~~~
nilkn
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".

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bpacker
Ex-Opower Data Scientist here. Cool to see this done. Just wanted to add a
link to the Opower blog post describing the original work this was based on,
done by Erik Shilts: [https://blog.opower.com/2014/10/load-curve-
archetypes/](https://blog.opower.com/2014/10/load-curve-archetypes/).

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dang
Posting 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.

~~~
minimaxir
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.

~~~
dang
Yes, this genre is well along on the hype curve. We may not always be able to
assess when articles are doing this but we're definitely open to suggestions
for better (= more accurate and neutral) titles.

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gianlucahmd
Hi guys, I'm the person who wrote the article.

I perfectly know that the title is an exaggeration, but choosing an headline
for medium is a tough job :)

The reason why I chose to call it like this, is not just to get a couple of
clicks, but because while talking to companies that are not ML-aware I often
get the question: "how the hell does X do that?!?!?", and the answer 80% of
the time could be 4 lines of sklearn.

My goal is to spread awareness on the potential of ML among companies: my
intended audience was not ML engineers, to whom this article looks more like
"I spent 6 days cleaning data, 20 minutes plotting different clusters
representations, and the rest of the day writing an article", but the business
person that is not fully aware of what it means to use ML today, and to whom
it looks like something amazing and extremely valuable.

Does it make more sense now? :D

~~~
minimaxir
As the top comment on this post
([https://news.ycombinator.com/item?id=13965043](https://news.ycombinator.com/item?id=13965043))
demonstrates, the title is misleading _at best_ , and I do not believe you
want to intentionally mislead your readers. Rhetorical smiley faces do not
change that.

Keep in mind that clickbait titles do get penalized on Hacker News, as dang
notes.

~~~
gianlucahmd
I didn't even post it on HN, you guys aren't my target as I explained.

I'm trying to make business execs more eager to experiment ML in their
companies, and less afraid about the years of R&D and skynet scenarios they
currently relate AI to.

The title was an hyperbole? Yes, but it worked in getting the attention of my
target audience. Anyone trying to work as an ML engineer knows that what I did
is simple and far from being worth $500M, but should still thank me for
spreading awareness on the potential of this technology among who's still
scared.

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samblr
Finding problem space in haystack is more important than building solution.
Because that would mean founders' know why a solution exists as it exists in
problem space and how to mould/pivot it down the line and build company around
it.

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hahaker
No you didn't recreated anything. That company is worth 500M. Your fun
experiment is not.

On the other hand, this is excellent PR for Opower.

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r00t-
Woah, talk about a misleading title.

