Part of the ongoing work of Argyris Kalogeratos, Stefano Sarao, Kevin Scaman, and Nicolas Vayatis, on behavioral epidemics and dynamic control is going to be presented in a poster format at the 42nd Middle European Cooperation in Statistical Physics (MECO), Feb 2017, Lyon, France.
The rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions. We present a case for the use of advanced machine learning techniques as an aide in this task and introduce a novel methodology that is shown to be capable of extracting meaningful information from large longitudinal corpora, and of tracking complex temporal changes within it.
How the pollsters missed Trump : a failure for prediction models and Big Data ?
L’élection de Trump et les trois échecs du « big data » électoral
La campagne de Hillary Clinton, supposée être à la pointe des technologies de ciblage électoral, a échoué à la faire élire. Du côté des médias, les modèles prévisionnistes, eux aussi basés sur le « big data », n’ont pas su prédire l’issue du scrutin. Lire…
LE MONDE |
Pollsters struggle to explain failures of US presidential forecasts
Most surveys did not predict Donald Trump’s victory over Hillary Clinton. Read more…
NATURE | 09
Is Donald Trump’s Surprise Win a Failure of Big Data? Not Really
Here’s how The White House wants the U.S. to approach AI R&D
In 2015, government spending on unclassified research and development in AI-related technologies was around $1.1 billion, according to one of the twin reports released today. But in the last five years alone, mergers and acquisitions among private companies vying for dominance in the AI market have far outstripped that figure, according to data from CB Insights.