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Francesco Marconi
Computational Journalist
Francesco Marconi is a renowned computational journalist and the Co-founder & CEO of Applied XL, a company that leverages machine learning to automate clinical intelligence and landscape analysis in the biopharma industry.123 He is based in New York and has a diverse background in economics and journalism.
Education and Background
- Bachelor's Degree: Econometrics and Quantitative Economics from Universidade de Coimbra (2004-2007).
- Master's Degree: Econometrics and Quantitative Economics from Università Cattolica del Sacro Cuore (2007-2009).
- Master's Degree: Business and Data Journalism from the University of Missouri-Columbia (2010-2012).
- Research Fellowships: Berkman Center for Internet & Society at Harvard University (2014-2015) and Chazen Institute Research Scholar at Columbia University (2013-2014).1
Career Highlights
- United Nations: Analyst at G77 Science & Technology Transfer (2009-2010).
- Associated Press: Co-lead for AI & News Automation (2011-2018).
- The Wall Street Journal: R&D Chief (2018-2020).
- Columbia University: Adjunct Instructor at the Graduate School of Journalism (2016-2019).
- MIT Media Lab: Research Affiliate at the Laboratory for Social Machines (2017-2019).12
Notable Works
- Author: Published "Newsmakers: Artificial Intelligence and the Future of Journalism" in January 2020, exploring the intersection of AI and journalism.24
- Applied XL: Founded in partnership with New Lab, focusing on AI-driven tools for real-time data analysis and insights.36
Francesco Marconi is recognized for his innovative work in integrating AI with journalism, aiming to enhance news coverage and analysis through machine learning.45
Highlights
The future of AI is closely linked to the future of journalism. To ensure continued progress in artificial intelligence, investing in high-quality information is essential.
The amount of text data used to train AI models has been growing by about 2.5 times per year. However, a new study warns that AI language models could exhaust the world's supply of publicly available training data by 2032.
News organizations possess a wealth of data in their news archives that AI companies, such as OpenAI, are increasingly eager to license. However, data licensing is not typically viewed as a revenue center like subscriptions or advertising. This means product and technology leaders in news orgs often lack the resources to properly productize and monetize this data. Consequently, they are unable to fully capitalize on the opportunity to work with AI companies.
Machines have now become the largest consumers of information from publishers, and this shift presents an opportunity to treat them (the machines) as a new audience with entirely distinct consumption habits.
High-quality journalism is essential for providing AI models with reliable information. Without it, AI could suffer from "model collapse," where performance deteriorates due to poor or synthetic data. Investing in journalism is crucial for the continued evolution and reliability of AI, but news organizations must invest in their own data capabilities to fully monetize the “machine audience”.
AI in news generation can lower individual production costs but may drive up overall expenses due to increased demand and expectations. Here are the key points shared at WAN-IFRA, the World Association of News Publishers.
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Increased Efficiency: AI can automate routine reporting, generate news summaries, and draft initial versions of stories, reducing time and labor.
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Increased Demand: As AI handles more complex tasks, news organizations may seek more diverse and sophisticated content, driving demand.
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Raised Expectations: Consumers may expect personalized news feeds, quicker updates, and more in-depth analysis, requiring advanced AI tools.
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Greater Investment: News organizations might need to invest in advanced AI systems, R&D, and improved infrastructure, increasing costs.
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Skill Shifts: Retraining or hiring staff skilled in AI, including data scientists and AI specialists, adds to the expense.
