Discussing TensorFlow, Google Brain, OpenAI, and End-to-End Examples

by Sophia TurolFebruary 22, 2016
Learn about the differences of TensorFlow from similar libraries, end-to-end examples, and open collaboration around artificial intelligence.

This post contains the recordings of the panels and technology sessions from the TensorFlow San Francisco meetup—sponsored and organized by Altoros on January 20, 2016.


A TensorFlow overview

In this video, Alex Londeree, Sr. Data Scientist at Knit Health, provides an overview of TensorFlow, comparing it to the similar libraries (Torch, Theano, Caffe, etc.). He also talked about TensorFlow’s mechanics, likely scenarios of the tool’s evolution, etc.



An end-to-end example of using TensorFlow

In his talk, Delip Rao of Joostware focused on under-the-hood mechanisms of TensorFlow with an actual code example. His goal was to demonstrate various TensorFlow concepts in the context of a working application.



Analytics with Juttle: A data flow language for everyone

From this lightning talk with Daria Mehra of Jut—a data analytics platform, you will learn about a new open-source Juttle project. Juttle is an analytics system and language for developers built upon a stream-processing core and targeted for the presentation-layer scale. Dari provides a demo of what Juttle is capable of as a language and what kind of a program can be written in it.



TensorFlow at the Google Brain team

This session with Geoffrey Irving of Google Brain mostly covers the problems TensorFlow is good at solving and how the tool is applied to tackle some internal issues within Google. In addition, Geoffrey gave some recommendations for those who are trying TensorFlow out and spoke of some data size restraints.



Open collaboration around AI

Gregory Renard, Chief Visionary Officer at XBrain—the company designing assistance for automakers—talked about OpenAI and its possible impact. (OpenAI is a non-profit artificial intelligence research organization founded by recognized ML/AI engineers and researchers, including Elon Musk.) He demonstrated some of XBrain’s solutions that make daily life easier.



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Further reading


About the speakers

Alex Londeree is Sr. Data Scientist at Knit Health. He is primarily training deep learning networks to do the necessary computer vision tasks for the company’s products. Alex has got a production background in deep learning and distributed machine learning systems.


Delip Rao is Founder of Joostware. He is working on natural language processing and machine learning research problems (semisupervised learning, graph-based ranking, sequence learning, distributed machine learning, etc.) and published several highly cited papers in these areas. Prior to founding his own company, Delip was working at Amazon, Twitter, and Google Research.


Daria Mehra is Director of Quality at Jut. She is an experienced QA Lead with 10+ years in the industry both on the test and development sides. Daria’s domain expertise is in data analytics, storage, distributed systems, SaaS, fault tolerance, and big data technologies.


Geoffrey Irving is a member of the AI safety team at OpenAI. He has got Ph.D. in Computer Science from Stanford University and B.Sc. in Mathematics and Computer Science from California Institute of Technology. Geoffrey’s interests lie in AI safety, theorem proving, computational physics, etc.


Gregory Renard is Chief AI Officer at xBrain. For over 20 years, Gregory has been working on data pipeline, unsupervised machine learning and deep learning applied to natural language processing, spoken dialogue systems, emotional AI, etc. As a former math professor and data scientist, he is passionate about a connected world where universal access to information and knowledge frees people to realize their full potential.