Deep Learning/Machine Learning Technical Interest Group (TIG)

Applying new analytics, neural networks, computational approaches using structured and unstructured data, and also training neural networks with supervised and unsupervised algorithms. Chaired by Patrick Ng and Andrew Munoz.
Deep Learning - Machine Learning TIG
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Last Post 02 Oct 2020 01:53 PM by  Patrick Ng
Business Acumen, Soft Skills and Machine Learning
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Patrick Ng
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02 Oct 2020 01:53 PM
    In the opening Michel T. Halbouty Lecture: Business Acumen and Soft Skills in an Everchanging Exploration World Day: Duval talked about “soft skills” now considered all the more important since the “engineering” preparation of a prospective deal and the related technologies are and will be more and more dependent on data analytics, advanced approaches involving AI, machine learning, etc.

    Takeaway - two ML questions were raised in Chat, worthy of recap here.

    Q1: if you have physics based model to train ML, why not stay with that?

    Model is built on limited data (usually we use statistics to infer) and because of time, it usually built on Monte Carlo simulation. (My take is combining both, i.e., a hybrid approach.)

    Q2: how soon do you think a fully AI-interpreted prospect will be drilled?

    That one wasn’t addressed before the session ended. We picked it up on Thursday at the Tackle the Issues - Where ML should go (and not go)?

    Hint - not a matter of if, but when. Watch this space on recap "Where ML should go (not go)?"
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