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How to make the human touch more human using natural language generation
They'll point to fees as the culprit. In other words, the robo-advisor is far less expensive, charging less than 1% of the value of one's portfolio, as opposed to the traditional 1% to 3% charged by most professional financial managers.
They'll also jump on the shift in user experience expectations from boomers to millennials. Younger people tend to do everything digitally and quickly, with as little personal contact as necessary. That's how they shop, get themselves from place to place, order food, find lodging, and so on. On-demand, push-button, machine-recommended-options are just the way the kids do things these days.
As a counter-argument, the professional managers offer a more -- pun intended -- human touch. Their experience, their ability to research, and the option to call or email or visit the local branch are all selling points.
What the professional financial managers tend to miss is that the human touch, so often lauded as their unique differentiator, isn't as human as it used to be. If professional managers want to reach and accommodate this new investor class, they need to be able to scale the human touch.
read the rest at: https://automatedinsights.com/blog/nlg-the-secret-weapon-in-the-war-between-financial-managers-and-robo-advisors
The NFL announced that it will expand an existing stats-tracking test program and, for the 2014/15 season, will be equipping every player with a sensor under each shoulder pad. The sensors will provide near-real-time information on each player's location and speed.
Back in May, I gave a talk on the future of automated journalism at the Tow Center for Digital Journalism at Columbia University. During that talk, I devoted some time to discussing the Robot Reporter, a growing network of chips and sensors that collect and deliver data to automated content platforms like Automated Insights' Wordsmith, which then instantly creates news articles from that data.
One example I gave was Quakebot, the LA Times template-driven software that broke its first widely-recognized earthquake news back in March. The second example was about sports, and all of the sensors currently being used to track events like balls and strikes, measurements like first downs, and the NFL's existing trial with player sensors.
With those sensors in place, I said, it's easy for us to make the jump to more qualitative analysis of a game, not just a statistical overview.
That part was met with a lot of excitement, and I spent most of the Q&;A talking about whether or not it was true and how big an advancement those sensors were.
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Christer Clerwall, from the Department of Media and Communication Studies at Karstad University in Sweden, conducted a pilot. For the test, 46 students in media and communications studies were given either a professionally-written NFL game recap from the L.A. Times or an automated recap of the same game from Automated Insights. They were asked to assess their article on both quality and credibility. They were also asked whether the article was written by a journalist or our engine.
Robot or Human
This certainly isn't the first time Automated Insights has been directly or indirectly involved with a robot vs. human test. It's something we always do in-house as a part of our normal QA process. Considering we've been at this for almost four years, we weren't surprised at the results.
From the study:
"Of the 27 respondents who read the software-generated text, 10 thought a journalist wrote it and 17 thought it was software-generated. For the 18 respondents in the “journalist group”, 8 perceived it as having been written by a journalist, but 10 thought software wrote it. Using a Mann–Whitney test for significance, we can conclude that there is no significant difference (U = 225, r = -0.07, significance = 0.623) between how the groups have perceived the texts."
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In the great race to bring more eyeballs to web and mobile sites, the term “personalized content” has come to mean the employment of machines (algorithms) to determine what the viewer is most interested in reading. Automation is then used to aggregate that content from across the Internet to present to that viewer, who is then more likely to read and enjoy it. More importantly, the viewer is more likely to click on the ads that accompany that content.
So if you're reading an article about Las Vegas, and you've read other articles about Las Vegas, you're probably getting an ad for the Venetian.
This isn't a bad theory. It's like Pandora for content (and I've recently seen more than one startup call themselves the Pandora for content). But there are a couple of things wrong with it:
read the rest at: http://blog.automatedinsights.com/post/38379798154/power-to-the-people-redefining-personalized-content
The concept of personalized content isn't new. In fact, it's one of the oldest concepts in the communication book. The process of disseminating facts and aggregating data for the sole purpose of updating a single person is one usually undertaken by a team of people (or in some cases, one highly-paid expert individual), in order to provide a decision-maker with the necessary information to make additional decisions. It's an executive status update, a high-net-worth individual's financial statement, a scouting report for the head coach.
In all those cases, however, the person receiving the report is in a position to spend a lot of time and money to have someone draw insights, formulate conclusions, and coax suggestions out of impossibly large amounts of data.
read the rest at: http://blog.automatedinsights.com/post/34630760630/evolving-personalized-automated-content-with-yahoo
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