Robots are taking our jobs, no doubt about it. Just in the past year we’ve seen barista robots, fast-food robots, pizza delivery robots, and even a robot conducting a symphony orchestra. But robots can’t replace journalists, right? The dogged reporters, members of the vaunted Fourth Estate, the men and women who bring us the news stories we read every day?
Think again. It’s happening, and odds are that you’ve been reading stories created by artificial intelligence in local and world news already.
A year ago, the Washington Post introduced Heliograf, an in-house program that automatically generates short reports for their live blog. It was first used during the Rio Olympics to provide information such as the results of medal events for services like Alexa. At that time Sam Han, engineering director of data science, said, “The next challenge is to broaden the subjects covered, deepen the kind of analysis possible and identify potential stories for our newsroom.” Read More
Daily Archives: May 17, 2019
The Washington Post’s robot reporter has published 850 articles in the past year
It’s been a year since The Washington Post started using its homegrown artificial intelligence technology, Heliograf, to spit out around 300 short reports and alerts on the Rio Olympics. Since then, it’s used Heliograf to cover congressional and gubernatorial races on Election Day and D.C.-area high school football games, producing stories like this one.
The Associated Press has used robots to automate earnings coverage, while USA Today has used video software to create short videos. But media executives are more excited about AI’s potential to go beyond rote reporting. Jeremy Gilbert, director of strategic initiatives at the Post, shared what the paper has learned so far from robo reporting and what it’s still trying to figure out.
In its first year, the Post has produced around 850 articles using Heliograf. That included 500 articles around the election that generated more than 500,000 clicks — not a ton in the scheme of things, but most of these were stories the Post wasn’t going to dedicate staff to anyway. For the 2012 election, for example, the Post did just 15 percent of what it generated in 2016. Read More
Meet Bertie, Heliograf And Cyborg, The New Journalists On The Block
An article in The New York Times, “The rise of the robot reporter”, discusses the new machine learning tools such as Forbes’ Content Management System called Bertie, The Washington Post’s Heliograf, Bloomberg’s Cyborg, and others used by Reuters, the Associated Press and The Guardian for a range of tasks in their newsrooms, providing further insight into how robots are taking over a growing number of jobs.
As the article points out, a number of media, notably those with a financial focus, now use algorithms to analyze data such as quarterly earnings reports and that can then be used to chart their progress over time, detect anomalies, as well as writing up stories. Machine learning can now be put to a growing number of uses in a newsroom. I have seen journalists use powerful analytical tools to analyze graphs, write and document their articles, adding information from different sources, as well as identifying information that may come from suspicious sources. It is hardly surprising that Bloomberg and Reuters now compete to provide real time financial information with the same hedge funds that were previously their clients but that now have access to the same means and resources. Read More
The “smarter” wall: how drones, sensors, and AI are patrolling the border
In an era of increasingly polarized politics, there are few issues as divisive as President Trump’s proposal to build a physical wall across part of the 2,000-mile US-Mexico border.
The Trump administration has argued that the border wall is a necessary deterrent to drug smugglers and immigrants seeking to enter the country unlawfully. It says unchecked immigration is a national security crisis, and one that needs to be addressed.
Critics, meanwhile, argue that the wall is a wildly expensive, ineffective, and misdirected effort. The actual crisis, they say, is a humanitarian one worsened by Trump’s restrictive immigration policies — about refugees seeking lawful entry into the US to flee violence and poverty in their home countries.
But there is another kind of border wall increasingly being talked about — one that proponents pitch as being less costly, less disruptive, and less politically controversial than a physical barrier: a so-called “smart wall.” Read More
Natural Language Processing – Business Applications
Executives worry about their businesses.
They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process.
But recently, machines have demonstrated their abilities to help shine some light on this chaos and provide, if not direct answers, context clues that help guide executives in using AI to handle business problems.
In this article, we delve into examples of how natural language processing (NLP) business applications can be applied at scale to address 5 pressing business questions. Read More
Natural Language Processing – Current Applications and Future Possibilities
A 2017 Tractica report on the natural language processing (NLP) market estimates the total NLP software, hardware, and services market opportunity to be around $22.3 billion by 2025. The report also forecasts that NLP software solutions leveraging AI will see a market growth from $136 million in 2016 to $5.4 billion by 2025.
In order to shed more light on the growing applications of NLP solutions, Dan Faggella, the CEO of Emerj, converses with Vlad Sejnoha, the CTO of Nuance Communications, an organization offering AI and NLP solutions in voice, natural language understanding, reasoning and systems integration. Read More
Natural Language Processing projects & startups to watch in 2019
Along with other tech trends, Natural Language Processing became another buzzword in the past years. But not everyone really understands what NLP is and how it can be used to improve efficiency of the process and impact your business in a positive way. In this article I will be briefly explaining what natural language processing is, how it is used, a few benefits on-site search get from doing it and I will mention a some cool startups that are doing natural language processing today. Read More
How to Apply Machine Learning to Business Problems
It’s easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for “machine learning” since 2012 – but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems.
With new AI buzzwords being created weekly, it can seem difficult to get ahold of what applications are viable, and which are hype, hyperbole or hoax
In this article, we’ll break down categories of business problems that are commonly handled by ML, and we’ll also provide actionable advice to begin a ML initiative with the right approach and perspective (even it’s the first such project you’ve undertaken at your company). Read More
Adversarial Vulnerability of Neural Networks Increases with Input Dimension
Over the past four years, neural networks have been proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We show that adversarial vulnerability increases with the gradients of the training objective when viewed as a function of the inputs. For most current network architectures, we prove that the`1-norm of these gradients grows as the square root of the input size. These nets therefore become increasingly vulnerable with growing image size. Our proofs rely on the network’s weight distribution at initialization, but extensive experiments confirm that our conclusions still hold after training. Read More
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