Category Archives: General

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Text-mining & Machine Learning to predict tomorrow’s Medical breakthroughs

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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.

Open Access version in St Andrews Research Repository

16-20 Aug. 2016
Identification of promising research directions using machine learning aided medical literature analysis / Victor Andrei ; Ognjen Arandjelovic.
Published in: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference
DOI: 10.1109/EMBC.2016.7591231

 

New Academic Chair on “Industrial Data Analytics & Machine Learning”

The ENS Paris-Saclay, Atos and the CEA (French commission for alternate energies including atomic) signed an agreement on October 20th, 2016.

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The chair is a cooperative program oriented towards industrial applications of artificial intelligence, setting the foundations for a unique ecosystem in France.

It includes training at the master and PhD level (MVA : Mathematics, Vision, Learning), joint research projects bringing together field experts and academic researchers, and support for the development of spin-offs.

cp-cea-atos-enseng-pdf

Recent US polls challenge Big Data

How the pollsters missed Trump : a failure for prediction models and Big Data ?

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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

Humans failed, not the data.

Hillary Clinton’s campaign for the presidency was famously, proudly, data-driven. For months, a trail of reporters chronicled the magic of the Clinton team’s “digital strategy” with dizzied wonderment. A data chief who scribbles on walls in erasable marker like Russell Crowe in A Beautiful Mind! Subtle but telling changes to landing page design! Something called “cost per flippable delegate!”
Read more…

FORTUNE |

US pollsters’ failure to forecast Donald Trump victory a ‘massive, historical, epic disaster’ says British academic who led review into mistakes over 2015 general election

The British academic who led an official review into how pollsters failed to forecast the result of the 2015 general election said their’ failure to predict Donald Trump’s clear victory is a “massive disaster” for the polling industry which is “high up the Richter scale”.
Read more…

FORTUNE |

A. I. in the U. S.

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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.

Read more…

TECHCRUNCH |

Obama : « l’intelligence artificielle pourrait accroître les inégalités »

Le président américain a consacré un long entretien à la question de l’intelligence artificielle, tandis que la Maison Blanche a publié une série de recommandations à ce sujet. Lire…

LE MONDE |

 

NIPS likes MLMDA

MLMDA will present latest results at NIPS’15 in Montréal from 7 to 12 December:

Regular session:

  • Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks.
    Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis.
  • Cornering Stationary and Restless Mixing Bandits with Remix-UCB.
    Julien Audiffren, Liva Ralaivola.

Workshops:

  • Workshop on “Networks in the Social and Information Sciences”
    Learning to Suppress SIS Epidemics in Networks.
    Argyris Kalogeratos, Kevin Scaman, and Nicolas Vayatis.
  • Workshop on “Bayesian Optimization: Scalability and Flexibility”,
    Optimization for Gaussian Processes via Chaining.
    Emile Contal, Cédric Malherbe, and Nicolas Vayatis.
  • Workshop on “Nonparametric Methods for Large Scale Representation Learning”
    Distributed Convolutional Sparse Coding via Message Passing Interface.
    Thomas Moreau, Laurent Oudre, and Nicolas Vayatis.