Any technology we imagine will become reality — even if decades later.

  1. In 1889, Jules Vernes foresaw live news: “Earth Chronicle is every morning spoken to subscribers, who, from interesting conversations with reporters, statesmen and scientists, learn the news of the day.” In 1980, CNN launched the first TV channel providing 24-hour news coverage.

2. Hugo Gernsback described news personalization back in 1911! “Morning newspapers transmitted to the sleeping subscriber by wire at 5am. The newspaper office, notified by each subscriber subscriber what kind of news is desirable, furnished only such news”.

Computational journalists to develop high precision AI systems with experts in the loop to distill data used by professionals and investors in the life sciences sector.

Brooklyn, N.Y. (April 21, 2021) Applied XL, a next-generation B2B information company tracking the health of people, places and the planet, today announced the closure of $1.5 million in seed funding through a round led by Tuesday Capital with participation by frog Ventures, Correlation Ventures, Team Europe, the investing arm of entrepreneur Lukasz Gadowski, co-founder of Delivery Hero, and Ringier executive Robin Lingg, a global media and marketplaces specialist.

Applied XL will use the seed funding to continue developing its AI-powered information system, which dynamically gathers data and applies machine learning and natural language processing to drive insights that…

Applied XL raises $1.5M to develop high precision AI systems that distill insights from big data, giving professionals and investors access to reliable life sciences information.

Editorial algorithms are developed by computational journalists and guided by journalistic principles.

Humans are producing more information right now than we did at any point in history. We’re up to our eyeballs in data — data from the web, from sensors, from mobile devices and satellites, all growing exponentially. The amount of data created over the next three years will be more than the data created over the past three decades. In the next five years, these technologies will produce more than three times the information that they did in the previous five.

What does this flood of data mean for the people whose job it is to make information accessible? …

  1. From a machine learning perspective, journalism is *simply* event detection applied to continuously updating data.
  2. Let’s look at an hypothetical example where there’s a lot of reliable data: Sports. If we were to monitor all the professional soccer/football matches around the world, occurring over a given weekend, how do we determine what the most newsworthy games were?
  3. That decision is traditionally made by a journalist based on their domain knowledge and context of the subject — they may emphasize matches that had the biggest offset, focus on top ranked teams, or even pay more attention to teams where highly paid…

This is why journalism must evolve into becoming an “Information Science”

1. The pandemic forced many news organizations to innovate how they use technology, rethink how they manage remote staff and make sense of huge amounts of complex information. Covid also demonstrated the importance of tracking data in real-time and making it a central reference for all reporting!

2. The sad reality is that the frequency of how often pandemics occur will only accelerate with climate change, which means newsrooms will need to be at least 10x more efficient in how they collect data — or risk their very own survival.

3. As temperatures rise and the global population grows to…

1.A key premise of computational journalism is that news events can be explained with the same rigor used by scientists to study the natural world. Journalism is going through a process of “mathematization” and eventually evolve into *Information Science*

2.The explosion of data from the web, sensors, mobile devices and satellites, combined with the accessibility to machine learning, creates the ideal environment for transforming how all information around us is sourced.

3. Advances in AI research are already allowing computational journalists to measure issues that were previously impossible to quantify (like counting trees in a desert), and as a result…

The crisis has shown a secondary, positive side effect: accelerating the speed of journalistic innovation.

The growing demand for information during the pandemic has not been met with an adequate increase in journalistic resources. In fact, the opposite has been true. According to the Poynter Institute, in the US alone over 33,000 journalists have been laid off, furloughed, or given pay cuts as a direct consequence of the coronavirus, resulting in a reduced capacity to cover fundamental public health issues. Despite the dire situation, the crisis has shown a secondary, positive side effect: accelerating the speed of journalistic innovation.

Take the case of British local wire service Radar AI. The joint venture between Press Association…

Data scientists are working alongside journalists to explore how well-established machine learning methods can help to easily find gaps in editorial coverage.

Coverage Map is a tool that can find opportunities in WSJ’s editorial coverage and audience impact by using deep learning.

Only a few years ago, using artificial intelligence in journalism was cutting edge, but nowadays it is quickly entering the workflow of a growing number of news organizations. The knowledge around machine learning methods is becoming more accessible as the cost of AI projects has gone down since many models are readily available online and can be implemented — even by small newsrooms.

Using these models, our team of data scientists took on, at first sight, an enormous challenge: analyzing and deriving insights from a decade’s worth of WSJ articles. …

The Wall Street Journal is experimenting with a new approach for reporting how smart algorithms work, beyond simply describing them.

Image Credit: Gabriel Gianordoli/ WSJ

Journalists, who routinely ask questions of their sources, should also be asking questions about an algorithm’s methodology. The rules created for algorithms need to be explicit and understood. The Wall Street Journal has been experimenting with a new approach to explain how AI works by letting readers experiment with it.

“Interactive graphics can provide insights into how algorithms work in a way beyond simply describing its output. They can do this by acting as safe spaces in which readers can experiment with different inputs and immediately see how the computer might respond to it,” said deputy graphics director Elliot Bentley.

Francesco Marconi

Computational journalist and co-founder of Applied XL. I write about data science, storytelling and innovation.

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