nickdavidhaynes
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Comment · 9 years ago
Comment · 9 years ago
It's a good idea to follow the links before accusing someone of illegal behavior.
Here's the link to the study again: [1]. This is specifically in reference to the reporting on quarterly earnings reports that we automate for the Associated Press. It's an objective summary of the financial performance of these companies that appears in news outlets across the country (for example, [2,3,4,5,6]). The companies being reported on have no influence over the content of the articles.
From the summary:
>These articles synthesize information from firms’ press releases, analyst reports and stock performance, and are widely disseminated by major news outlets within hours of publication...This study found a positive effect between the public dissemination of objective information and market efficiency.
[1] https://insights.ap.org/industry-trends/study-news-automatio...
[2] http://www.thenewstribune.com/news/business/article158779784...
[3] http://wtop.com/business-finance/2017/05/yum-beats-street-1q...
[4] http://www.foxbusiness.com/markets/2017/04/27/dominos-sales-...
[5] http://www.businessinsider.com/ap-fedex-beats-street-1q-fore...
[6] https://www.usnews.com/news/business/articles/2017-04-26/her...
Comment · 9 years ago
If you do automated content well, it's not awkward and difficult to read ;)
As far as visuals vs prose, I see it as "both-and" rather than "either-or". And in addition to our journalism and personalization work, we also integrate with interactive visualization tools like Tableau.
Comment · 9 years ago
I work for Automated Insights, a company that makes a SaaS platform very similar to what's in the article. Here's an example "in the wild" of the content we produce - http://www.thenewstribune.com/news/business/article158774809...
Many of your criticisms are totally valid. Lots of the phrasing is awkward - even the lede is really bad ("Tesla, Inc. (TSLA) has been having a set of eventful trading activity"...wat). And it feels really deceptive to put a human byline on an automated article.
We're pretty open about the fact that our solution to this problem is not "magical" at all [1, 2] - it's good, old-fashioned automation. This approach allows our customers to QA their content heavily before pushing it to production, which eliminates many of the problems with awkward/incorrect phrasing that people who rely more heavily on machine learning tend to run into. And the news articles we publish always have a note at the end saying that they were generated by Automated Insights, and don't include a human byline.
There is real value in this type of reporting - a recent study [3] found that the articles we produce for less well-known publicly-traded companies has increased the trading volume for those companies. The idea is that, yes, the content is fairly formulaic, but there's now reporting on companies that had very little coverage before we existed. There are similar arguments for mass personalization work we've done for companies like Activision Yahoo - having prose that describes raw data (even if it is formulaic to an extent) is often better than not having prose.
[1] https://automatedinsights.com/blog/the-state-of-artificial-i...
[2] https://automatedinsights.com/blog/creating-great-automated-...
[3] https://insights.ap.org/industry-trends/study-news-automatio...
Comment · 9 years ago
Automated Insights | Software engineer, applied research | Durham, NC | Onsite | Full-time
http://automatedinsights.applytojob.com/apply/oMu2ojUP8M/Sof...
I'm the tech lead for the R&D team at Automated Insights, a company that turns raw data into human-sounding narratives. Our SaaS platform, Wordsmith, generated billions of articles last year for companies like the AP, Yahoo, and Activision.
My team's responsibility is mainly algorithm development (machine learning and natural language processing, as well as more traditional methods), with the goal of making Wordsmith more powerful and easier to use. We primarily develop in Python (spaCy, gensim, scikit-learn, Tensorflow) and NodeJS.
We're looking for an engineer with a couple years experience and familiarity with machine learning. A research background and/or advanced degree is a plus.
Interview: Quick chat with HR, technical phone screen, at-home programming evaluation, on-site interview. No adversarial whiteboard sessions or trivia quizzes.
Comment · 9 years ago
Agreed all around. At the very least, I don't think it was obvious what message the author was trying to convey.
Comment · 9 years ago
While I definitely agree that advances in ML in the last 20 extremely important (and potentially revolutionary), I think this article misses the mark in a few places.
>Now, instead of humans designing algorithms to be executed by a computer, the computer is designing the algorithms. (Albeit guided by human-devised algorithms)
This line is way off in both tone and substance. On tone, it really underplays the human effort involved in effective machine learning (as it is practiced in 2017) and anthropomorphizes "machines" to an unreasonable extent. In substance, I fail to see how a machine that "designs its own algorithms" according to an algorithm designed and implemented by a human is fundamentally different than an algorithm coded directly by a human. To use the author's example, machine learning allows humans to build complex software systems in less time just as a bicycle allows humans to cover more distance with less energy. It's a big improvement, but it's not, say, teleportation.
>it is only now that the machines are creating themselves, at least to a degree. (And, by extension, there is at least a plausible path to general intelligence)
I could not disagree more strongly with this addendum. Simply put, I fail to see any path from state-of-the-art ML/DL research today to AGI, and I would even go so far as to say that humans have made approximately zero progress on this task since it was first formulated in the 50s. I think we know about as much about "intelligence" (and consequently, what would constitute AGI) as star-gazers in ancient times know about the universe. That's not to say that it will take millennia to invent AGI, but the path to get there is probably quite orthogonal to modern ML research.
Comment · 9 years ago
Not sure exactly why this was posted today, since spaCy has been around at least a couple years, but - spaCy is a great tool, and I have a ton of respect for Matthew Honnibal, the main developer.
Coincidentally, I wrote a blog post [1] that went up just this morning that, in part, compares spaCy with the other giant in the Python NLP ecosystem, NLTK. TLDR - I think that, right now, the majority of users are better served by spaCy than NLTK.
[1] https://automatedinsights.com/blog/the-python-nlp-ccosystem-...
Comment · 9 years ago
Could you talk more about your phone screens? What criteria are you filtering on? What sorts of questions have you found to be effective at stratifying candidates?
Comment · 9 years ago
>There is no "fake world" with "fake world" problems
You've never met a number theorist.
Comment · 9 years ago
Seeing an article refer to "AI" as if it's a monolith is a really good indicator of the quality of that article.
Comment · 9 years ago
>just a theory
I think the way you're using the word "theory" here dilutes its technical meaning. More accurate to call it speculation or philosophizing.
Comment · 9 years ago
Sorry, I might not have been clear about what I meant by "bottom-heavy". I think we actually agree - as someone who's hiring for DS roles right now, I've seen a ton of exactly what you're talking about.
-Some candidates can write great code, but don't have the math background to understand what ML black boxes are doing.
-Then there are STEM PhDs that have never written non-research (i.e. maintainable) code or had to formulate a qualitative business problem into a quantitative problem they can solve.
Both types of candidates need to come in at a "junior" level and do some on-the-job learning in order to be fully successful data scientists. IMO it appears to be easier to teach STEM PhDs how to code than programmers how to do math, but that might be personal bias (since I came from the former group).
Comment · 9 years ago
Call it "the kaggle effect" - once someone defines the problem and the metric you'll be graded on and gives you a relatively clean dataset, "solving the problem" is just as simple as importing xgboost and plowing away. But there is often an under-appreciation among people without much job experience how hard it is to get to that point. The OP article touched on it a bit, but really, the most difficult job a data scientist has is defining what problem they're trying to solve and getting buy-in from other business stakeholders. And frankly, no data science masters program or boot camp can teach those skills.
Comment · 9 years ago
To be fair, learning statistics is hard for the same reason that doing statistics is hard - any statistic involves assumptions, and the different assumptions underlying different models can be very subtle. There's a lot of disagreement among even professional, academic statisticians about fundamental concepts like p values [1] and how to quantify uncertainty under multiple hypothesis testing [2]. Unfortunately, I don't see any of this getting easier any time soon (although I would love to be proven wrong).
[1] http://www.tandfonline.com/doi/full/10.1080/00031305.2016.11...
http://automatedinsights.applytojob.com/apply/oMu2ojUP8M/Sof...
I'm the tech lead for the R&D team at Automated Insights, a company that turns raw data into human-sounding narratives. Our SaaS platform, Wordsmith, generated billions of articles last year for companies like the AP, Yahoo, and Activision.
The team's responsibility is mainly algorithm development (machine learning and natural language processing, as well as more traditional methods), with the goal of making Wordsmith more powerful and easier to use. We primarily develop in Python (spaCy, gensim, scikit-learn, Tensorflow) and NodeJS. We're looking for an engineer with a couple years experience and familiarity with machine learning. A research background and/or advanced degree is a plus.
Interview: Quick chat with HR, technical phone screen, at-home programming evaluation, on-site interview. No adversarial whiteboard sessions or trivia quizzes.