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SF Analytics + AI Meetup (Methods to implement AI without Big Data)

May 22, 2019 · San Francisco, United States of America

Come join us for our next SF Analytics & Artificial Intelligence Meetup to be hosted at Werqwise San Francisco.

Join in engaging discussions around how Analytics & AI are being applied in various fields to address problems and opportunities.

Theme: Implementing AI without access to Big Data (large data sets).

Agenda:

6:00-6:20pm - Networking
6:20-7:20pm - Analytics & AI Presentations (20 minutes per speaker)
7:20-7:40pm - Q&A Panel
7:40-8:00pm - Networking and Close

Speakers:

Ciro Greco - Founder/CEO - Tooso.AI (PhD in Cognitive Neuroscience from Università degli Studi di Milano-Bicocca) - Tooso is an AI technology designed to transform eCommerce search into an intelligent interactive experience: we help digital retailers make their search engines as smart as shopping assistants.

Topic: Less (data) is more - how to build efficient machine learning using
knowledge graphs and concept learning. Examples on how a different approach to learning and inference can bring superior results with less data, drastically reducing the time to ROI.

Tommaso Furlanello - Head of Science at GLIA Intelligence (PhD in Neuroscience from USC where he did research on long term memory and transfer learning in Deep Neural networks, interned multiple times in the Amazon Web Services Artificial Intelligence group). GLIAI is an AI company that offers machine learning solutions for real world markets.

Topic: Practical Deep Learning with Impractical Datasets - Modern deep learning techniques are developed and tested in overly controlled settings with artificially good data-sources. In real life data are noisy, incomplete, and expensive; sometimes there are too much data for a single scientist to wrap her head around, and sometimes there are not enough for any machine learning model to be trained. Most importantly, the actual data generation process is typically unknown and can not be fully controlled. In this talk I will show how recent algorithmic advances in deep learning can be combined with scientific best practices and domain knowledge to fit complex deep learning models to impractical datasets.

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